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APPLICATION OF DYNAMIC MATRIX CONTROL TO INTERACTIVE MULTILOOPS: A Case Study of a Fischer-Tropsch Hydrocracking Reactor
A Dissertation Presented to
The Engineering Institute of Technology
by
OBIEZU IFENNA AMAOBI
In Partial Fulfillment of the Requirements for the Degree
Master of Engineering in INDUSTRIAL AUTOMATION
Date JANUARY 2018
COPYRIGHT © 2018 BY OBIEZU IFENNA AMAOBI
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ACKNOWLEDGEMENTS First and foremost, I would like to thank God Almighty, the Creator of Heavens and Earth, for without Him,
none of this would be possible.
I would like to express my profound gratitude to my supervisor Ian Alers for giving me the opportunity to work
under his professional supervision. Your motivation and guidance were very admirable and inspiring. I will
sincerely be grateful for the effort and time you took in providing valuable advice and comments on the entire
thesis.
Finally, I must express my very profound appreciation to my loving wife Frances Obiezu for providing me with
unfailing support and continuous encouragement throughout my years of study. This achievement would not
have been possible without her emotional support. Thank you.
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TABLE OF CONTENTS ACKNOWLEDGEMENTS ................................................................................................................................ ii
LIST OF TABLES.............................................................................................................................................. v
LIST OF FIGURES ........................................................................................................................................... vi
ABSTRACT ..................................................................................................................................................... vii
ACRONYMS .................................................................................................................................................. viii
CHAPTER 1 ........................................................................................................................................................... 1
INTRODUCTION .................................................................................................................................................. 1
1.1 Background ................................................................................................................................................... 1
1.2 Research Aims and Objectives ..................................................................................................................... 2
1.3 Thesis Organization ...................................................................................................................................... 2
CHAPTER 2 ........................................................................................................................................................... 4
ISSUES DESCRIPTION ........................................................................................................................................ 4
2.1 The Multiloop Control Problem ................................................................................................................... 4
2.2 Case Study: The Hydrocracker Reactor ....................................................................................................... 4
2.2.1 Process Description ............................................................................................................................... 4
2.2.2 Current Control Strategy ........................................................................................................................ 6
2.2.3 Limitations of the Current Control Strategy .......................................................................................... 7
2.2.4 Proposed Solution .................................................................................................................................. 8
2.3 What is Model Predictive Control (MPC) .................................................................................................... 9
2.3.1 History and Evolution of MPC ............................................................................................................ 10
2.3.2 Advantages and Disadvantages of MPC [18] [19] .............................................................................. 12
2.3.3 Characteristics of MPC ........................................................................................................................ 12
2.3.4 Formulation of MPC Algorithm .......................................................................................................... 12
CHAPTER 3 ......................................................................................................................................................... 17
METHODOLOGY ............................................................................................................................................... 17
3.0 Modelling of Plant Dynamic System Using Historical Data ...................................................................... 17
3.1 Step 1: Selection of variables ................................................................................................................. 17
3.2 Step 2: Plant Test .................................................................................................................................... 21
3.3 Step 3: Model Estimation ....................................................................................................................... 22
3.4 Step 4: Design of MPC Controller .......................................................................................................... 36
3.5 Step 5: Commissioning of MPC Controller ............................................................................................ 36
CHAPTER 4 ......................................................................................................................................................... 37
MPC CONTROLLER DESIGN AND SIMULATIONS ..................................................................................... 37
4.1 Specifying MPC Controller Parameters ..................................................................................................... 37
4.1.1 Sample Time ........................................................................................................................................ 37
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4.1.2 Prediction Horizon ............................................................................................................................... 37
4.1.3 Control Horizon ................................................................................................................................... 37
4.1.4 Constraints ........................................................................................................................................... 38
4.1.5 Tuning Weights ................................................................................................................................... 38
4.2 Controller Design Using MPC Designer .................................................................................................... 39
4.2.1 Import Plant and Define MPC Structure.............................................................................................. 39
4.2.2 Case 1: Open-Loop Simulation – Verifying Interactions between MVs and CVs .............................. 41
4.2.3 Case 2: Closed-Loop Simulations ....................................................................................................... 44
CHAPTER 5 ......................................................................................................................................................... 52
CONCLUSIONS AND RECOMMENDATIONS ............................................................................................... 52
5.1 Conclusions ................................................................................................................................................ 52
5.2 Recommendations for Future Work ........................................................................................................... 52
REFERENCES ..................................................................................................................................................... 53
APPENDIX A....................................................................................................................................................... 55
APPENDIX B ....................................................................................................................................................... 60
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LIST OF TABLES Table 3.1: Raw process data from PI historian showing interactions between MV1 and CVs 18 Table 3.2: Raw process data from PI historian showing interactions between MV2 and CVs 19 Table 3.3: Raw process data from PI historian showing interactions between MV3 and CVs 20 Table 3.4: Comparison between State-space mode and transfer function model fit to estimate data 26
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LIST OF FIGURES Figure 1.1: Control Loop Interaction 1 Figure 2.1: Simplified PFD of Refinery section in the EGTL facility (From Fischer-Tropsch Refining) 5 Figure 2.2: Simplified PFD of Hydrocracker Reactor Showing Bed Temperature Control 7 Figure 2.3: Operation Hierarchy of MPC (From Georgia Institute of Technology) 9 Figure 2.4: Basic MPC Structure (From Georgia Institute of Technology) 10 Figure 2.5: Simplified evolutionary tree of the most significant industrial MPC algorithms 10 Figure 3.1: Plant historical data showing interactions between MV1 and CV1 and CV2 21 Figure 3.2: Plant historical data showing interactions between MV2 and CV1 and CV2 21 Figure 3.3: Time domain input-output data for MV1 and CV1 23 Figure 3.4: Time domain input-output data for MV1 and CV2 23 Figure 3.5: Time domain input-output data for MV2 and CV1 24 Figure 3.6: Time domain input-output data for MV2 and CV2 24 Figure 3.7: System Identification Toolbox showing imported data and estimated models 25 Figure 3.8: Step response plot for MV1CV1. (Settling time to steady state = 340s) 27 Figure 3.9: Step response plot for MV1CV2. (Settling time to steady state = 146s) 28 Figure 3.10: Step response plot for MV2CV1. (Settling time to steady state = 197s) 29 Figure 3.11: Step response plot for MV1CV1. (Settling time to steady state = 390s) 30 Figure 3.12a: Overall plant step response model 32 Figure 3.12b: Combined plant step response model 33 Figure 3.13: Model validation plot showing Best Fits for MV1CV1 (Best Fits = 84.48%) 34 Figure 3.14: Model validation plot showing Best Fits for MV1CV2 (Best Fits = 79.2%) 34 Figure 3.15: Model validation plot showing Best Fits fir MV2CV1 (Best Fits = 89.08%) 35 Figure 3.16: Model validation plot showing Best Fits fir MV2CV2 (Best Fits = 71.2%) 35 Figure 4.1: Basic concept of MPC – Prediction and Control horizons 38 Figure 4.2: MPC designer – defining MPC structure from imported plant model 39 Figure 4.3: MPC designer – Input and Output Channel Specifications 40 Figure 4.4: MPC designer – Default simulation scenario using default MPC controller 40 Figure 4.5: MPC designer – Interactions Between MV1 and CV1 and CV2 (MV2 is constant) 41 Figure 4.6: MPC designer – Input and Output Response between MV1 and CV1 and CV2 (MV2 is constant) 42 Figure 4.7: MPC designer – Interactions Between MV2 and CV1 and CV2 (MV1 is constant) 42 Figure 4.8: MPC designer – Input and Output Response between MV2 and CV1 and CV2 (MV1 is constant) 43 Figure 4.9: MPC designer – Combined step changes on MV1 and MV2 43 Figure 4.10: MPC designer – Input and Output Response Plots for Combined step changes on MV1 and MV2 44 Figure 4.11: MPC designer – Tuning parameters configuration 44 Figure 4.12: MPC designer – Simulation Settings for Case Study 1 45 Figure 4.13: MPC designer – Input and Output Response Plots for Case Study 1 45 Figure 4.14: MPC designer – Simulation Settings for Case Study 2 46 Figure 4.15: MPC designer – Input and Output Response Plots for Case Study 2 47 Figure 4.16: MPC designer – Simulation Settings for Case Study 3 48 Figure 4.17: MPC designer – Input and Output Response Plots for Case Study 3 48 Figure 4.18: MPC designer – Input Constraint Specification for Case Study 4 49 Figure 4.19: MPC designer – Input and Output Response Plots for Case Study 4 50 Figure 4.20: MPC designer – Tuning Weights Specification for Case Study 5 51 Figure 4.21: MPC designer – Input and Output Response Plots for Case Study 5 51
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ABSTRACT Most practical control processes are multivariable processes and are characterized by process interactions. A
situation where a change in one manipulated variable can affect several controlled variables. The conventional
industrial approach for dealing with such multivariable control problems is to use a multi-loop control system
consisting of set of conventional PI or PID controllers where each manipulated variable depends on only a
single controlled variable. The three-bed hydrocracker reactor in the Escravos Gas-to-Liquid (EGTL) plant,
located in Nigeria is controlled using three sets of temperature control loops each dedicated to controlling the
temperature of each bed. This current control strategy does not control the hydrocracker effectively due to the
limitations of the PID loops in controlling processes with significant interactions.
The research details the design and simulation of a model predictive controller (MPC) to deal with such control
problems associated with interactive multiloops using a case study of a hydrocracker reactor that converts wax
via Fischer-Tropsch (FT) synthesis to valuable products. Several case studies were simulated to highlight MPC
controller’s improved capability in handling process interactions and constraints compared to conventional
single loop PID control. The tool used for the MPC controller design is MATLAB MPC designer app.
This work also further details how to develop the dynamic model of any process given the plant historical data
from which input-output data correlations can be deduced. This is particularly important for two reasons. First,
it is always not convenient to perform step response tests on live plants to gather input-output data due to
operational reasons. Secondly, for most complex processes, it is difficult to obtain the dynamic model of most
processes from first principles or by modelling from mathematical formula. Hence the data-driven modelling
approach presented in this research work can be used to reconstruct the dynamic of any plant provided the
historical data of that plant is available. The tool used for the model estimation is MATLAB system
identification app.
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ACRONYMS PID - Proportional Integral Derivative PI - Proportional Integral NMP - Non-Minimum Phase PC - Pressure Controller FC - Flow Controller TC - Temperature Controller LC - Level Controller CV - Controlled Variable MV - Manipulated Variable DMC - Dynamic Matrix Control FT - Fischer-Tropsch MPC - Model Predictive Control EGTL - Escravos Gas-to-Liquid LPG - Liquefied Petroleum Gas PI - Process Information MIMO - Multi-Input, Multi-Output RGA - Relative Gain Array QDMC - Quadratic Dynamic Matrix Control PFD - Process Flow Diagram HP - High Pressure LHSV - Liquid Hourly Space Velocity DCS - Distributed Control System LAT - Level Average Temperature BAT - Bed Average Temperature ABT - Average Bed Temperature WABT - Weighted Average Bed Temperature LQG - Linear Quadratic Gaussian IDCOM - Identification and Command FIR - Finite Impulse Response MPHC - Model Predictive Heuristic Control HEICON - Hierarchical Constraint Control SMCA - Setpoint Multivariable Control Architecture SMOC - Shell Multivariable Optimizing Controller RMPCT - Robust Multivariable Predictive Control Technology QUADPROG - Quadratic Programming SISO - Single Input, Single Output N4SID - Numerical algorithms for Subspace State Space System IDentification FPE - Final Prediction Error MSE - Mean Square Error MO - Measured Output OV - Output Variable QP - Quadratic Programming
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CHAPTER 1 INTRODUCTION
1.1 Background
For over forty (40) years, 80% of installed automated control devices in the chemical and process industries
have been implemented used single loop Proportional Integral Derivative (PID) control [1]. This is due to their
simple control structure and seemingly easy tuning method (example, Ziegler-Nichol’s 1st and 2nd tuning rule,
and so on). Despite these advantages, single-loop PID control has limitations in areas like; non-linearity of
model, non-minimum phase processes exhibiting inverse response behaviour, problems associated control-loop
interaction, problem associated with time delay, and finally constraints problems [2]. These limitations will be
discussed briefly below.
Limitations of single-Loop PID Control
Model Non-linearity: Since control gains are usually obtained by linearizing a non-linear system around a
particular equilibrium point, then it means that, the control gain which gives a good performance in a particular
equilibrium point may not perform very well in another equilibrium point. Single loop PID control will not
thrive here, but a non-linear controller that switches controller parameters in different operating regions (Gain
Scheduling) can be used.
Non-Minimum Phase (NMP) Systems: Some NMP systems exhibit inverse responses, that is, they initially act
in a direction opposite to their final response when reacting to a control input. Single-loop PID control cannot
be used for NMP systems.
Control Loop Interaction: This mostly occurs in ill-conditioned processes, that is, where control variables
(CVs) are more than the manipulated variables (MVs). This results in poor control performances. See figure 1.1
below.
Figure 1.1: Control Loop Interaction [2]
Time Delay Problems: The problem of time delay cannot be over emphasized. It is because of taking
measurements far away from the MVs. When the issue of time delay is not properly catered for, it causes
oscillations, and can cause the closed-loop system to be unstable. PIDs do not perform well when the process
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uncontrollability factor 𝑃𝑃𝑃𝑃 > 1, and PIDs do not work at all when 𝑃𝑃𝑃𝑃 > 10. 𝑃𝑃𝑃𝑃 = 𝑇𝑇𝑑𝑑𝜏𝜏
where 𝑇𝑇𝑑𝑑is the time
delay, and 𝜏𝜏 is the time constant [1].
Constraints Problems: Many chemical or petrochemical processes contain several constraints. These
constraints can affect the MVs and/or CVs. A very popular example is a valve cannot be opened more than
100% (this is input constraint). Example of an output constraint can be perhaps the level of a tank specified to
be within ± 10m of its steady state value. Note that, in the case of Single loop PID controllers, valve saturation
or even valve stiction is caused by integral wind-up, which is as a result of constraints.
Since the 1960s, advanced process control has been taken to mean any control algorithm or strategy that shifts
from the classical or conventional, Proportional Integral Derivative (PID), control. A major reason for the
advent of advanced process control was the advancements made possible in the computer technology. This
meant that all previous algorithms could now be realized by the new digital computational technologies.
Nowadays, advanced process control is synonymous with the implementation of computer based technologies
[1] [31].
1.2 Research Aims and Objectives
The primary aim of the work described in this thesis details the application of an advanced process control
strategy - dynamic matrix control (DMC) to interactive multiloops using a case study of a hydrocracker reactor
that converts wax via Fischer-Tropsch (FT) synthesis to valuable products. This is to show how DMC improved
capability in handling process interactions compared to conventional single loop PID control. This work also
further details how to develop the dynamic model of any process given the input-output measurement data or
historical data from which input-output data correlations can be deduced. This is particularly important because
it is difficult to obtain the dynamic model of most processes from first principles or by modelling from
mathematical formula. Finally, this work will demonstrate how robust model predictive control (MPC) is with
respect to constraint handling, setpoint tracking and disturbance rejection when compared to conventional
single loop PID controller. The reference facility from where historical data for this thesis will be collected is
hydrocracker reactor in the Escravos Gas-to-Liquid (EGTL) plant which is in Delta State, Nigeria. The
objective of the Escravos GTL plant is the conversion of natural gas feedstock into high-quality,
environmentally superior, liquid GTL Fuel (diesel), chemical-grade Naphtha and Liquefied Petroleum Gas
(LPG). The hydrocracker reactor is mainly responsible for this conversion, hence, optimization of the
hydrocracker reactor control using advanced process control strategies such as Dynamic Matrix Control could
lead to higher product yield and more efficient energy usage.
1.3 Thesis Organization
The remainder of this thesis is organized as follows:
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In chapter 2, all the background related to this research work is introduced. First, the multiloop control problem
is detailed as it affects hydrocracking process. Then, current technologies used for dealing with strong process
interactions are discussed. Dynamic Matrix Control is proposed as a method to be used in this research solve
process interaction problem. This chapter also discusses the hydrocracker process and the current control
strategy as applied to the hydrocracker unit in Escravos Gas-to-Liquid (EGTL) plant located in Nigeria. Finally,
a brief review of Model Predictive Control with some mathematical derivations is included.
Chapter 3 provides detailed information how the hydrocracker process model was modelled from historical data
of the plant stored in Process Information (PI) historian. It detailed the process involved in the selection of
manipulated variables and controlled variables, and how MATLAB system identification toolbox was used in
estimating and validating the dynamic model of the plant from measured input-output data.
Chapter 4 opens with a brief review of the MPC controller design process with a brief discussion on how to
select each of the MPC controller parameters. A great deal of this chapter is devoted to designing of the MPC
controller and testing of the controller against the dynamic model of the plant using various simulated scenarios
to highlight some of the striking features of MPC controller like constraint handling, tuning weights, prediction
and control horizons.
Chapter 5 provides concluding remarks based on results of simulations in chapter 4. This chapter ended with
outlining recommendations for future research work.
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CHAPTER 2 ISSUES DESCRIPTION
2.1 The Multiloop Control Problem
Most practical control processes are multivariable processes and are characterized by process interactions. that
is, a situation where several process variables are to be controlled and several variables can be manipulated. In
this case, a change in one manipulated variable say u1 can affect several controlled variables (y1, y2… yn). This
is commonly referred to Multi-input, multi-output (MIMO) control system or multivariable processes. The
conventional industrial approach for dealing with such multivariable control problems is to use a multi-loop
control system consisting of set of conventional PI or PID controllers where each manipulated variable depends
on only a single controlled variable [3] [4]. To design such control systems, the selection of variable pairing is
of primary importance. There are several methods adopted to ensure that variables are paired relative to the
degree of interactions that exist between the manipulated variables (inputs) and controlled variables (outputs).
The most prominent and widely used approach for characterizing process interactions is the Relative Gain Array (RGA) method proposed by Bristol (1966). The chief advantages of the RGA approach are that it is easy
to use and only requires a crude process model, namely, the process gains which can be determined from steady
state information [3] [4]. It was suspected that due to its lack of dynamic information the RGA failed to provide
an accurate configuration. However, for processes with significant interactions, even the best multiloop control
system may not provide satisfactory control.
Model-based multivariable control strategies such as model predictive control can provide significant
improvements over conventional multiloop control. [7]. This research work focuses on how to deal with
problems associated with multiloop interactions in processes by the application of advanced process control
strategies like dynamic matrix control (DMC) using a case study of a hydrocracker reactor that converts wax via
Fischer-Tropsch (FT) synthesis to valuable products.
Dynamic Matrix Control (DMC), devised by Shell Oil (Cutler and Ramaker, 1980), was the first Model
Predictive Control (MPC) algorithm [5] [25]. Over the years, MPC have become generally accepted within the
chemical and process industries for dealing with difficult multivariable control problems that include inequality
constraints. Per an MPC survey by Qin and Badgwell (2003), there were over 4,500 applications worldwide by
the end of 1999, primarily in oil refineries and petrochemical plants [6]. Some specific applications of MPC to
hydrocracker units are: Quadratic Dynamic Matrix Control (QDMC) implemented on four multibed
hydrocracker reactors at the Shell Canada Limited Scotford Refinery [8], and a Model Predictive Controller
design in Izmit Refinery hydrocracker unit [9].
2.2 Case Study: The Hydrocracker Reactor
2.2.1 Process Description A simplified process flow diagram (PFD) is given in Figure 2.1. The hydrocracker receives two feed streams
from the Fischer-Tropsch gas loop, namely wax and cold condensate. The wax and the cold condensate are
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combined to serve as feed to the hydrocracker [10]. This feed is converted into more valuable products in the in
the hydrocracker under high hydrogen pressure and catalytic condition. Commonly used hydrocracking
catalysts consists of sulphide base metals on an acidic support. Since the Fischer-Tropsch Synthetic crude is
sulphur-free, a sulphiding agent is co-fed to keep the catalyst in a sulphide state [10].
The reactor inlet pressure should be maintained at 70bar to maximize the hydrogen partial pressure. The
exothermic cracking and saturation reactions result in a large heat release, which increases the temperature of
the reactants. This increased temperature further increases the rate of reaction. To control this temperature rise,
and likewise, the rate of reaction, the catalyst is separated into three beds in the reactor. In between each bed is
the quench section. In the quench section, hot process fluids from the preceding bed are combined with
relatively cold hydrogen-rich recycle gas to quench the reacting fluids before the mixture passes into the next
bed, and thereby control the amount of temperature rise and the rate of reaction. Reactor internals between the
catalyst beds are designed to ensure thorough mixing of the reactants with the quench gas and good distribution
of the vapour and liquid flowing down to the next bed. Good distribution of the reactants across the catalyst bed
prevents hot spots and maximizes catalyst performance and life [11].
The effluent stream from the hydrocracker reactor enters the hot High Pressure (HP) separator where the
hydrocracker effluent vapor is separated from the hydrocracker effluent liquid phase. The hydrogen-rich gas
from the hot HP separator is compressed and recycled back through the high-pressure loop. The liquid phase
from the hot HP separator is routed through the main fractionator and stabilizer column where the products
(LPG, Naphtha and distillate) are recovered. The lower fractions are recycled back to the hydrocracker reactor.
The product from hydrocracking is distilled to produce LPG (3 – 7%), naphtha (20 – 30%), and distillate (65 –
75%), with the unconverted waxy product being recycled to the hydrocracker feed [5]. Typical operating
conditions are a “liquid hourly space velocity” (LHSV = Reactant Liquid Flow Rate/Reactor Volume) of 1.2
per hr., at 350 ℃, and 70bar, with the temperature being adjusted to keep the per pass conversion at around
65%. [10]
Figure 2.1: Simplified PFD of Refinery section in the EGTL facility (From Fischer-Tropsch Refining) [10]
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2.2.2 Current Control Strategy Regulatory control in the EGTL plant is performed by a Yokogawa Distributed Control System (DCS). The
most important parameter to monitor in the hydrocracker reactor is the per pass conversion which is an
indication of the total amount of feed that is converted into valuable products, hence a measure of profitability
for the hydrocracker. [9] To achieve this, parameters such as feed flow, temperature, hydrogen partial pressure
and recycling are monitored and controlled. The inlet temperature and the temperatures at the top and bottom of
each bed is closely monitored and controlled to achieve required per pass conversion.
The inlet temperature of the reactor is controlled by varying the fuel gas flow to the preheat furnace.
Temperatures in the reactor are measured by thermocouples placed at the top and bottom of each catalyst bed.
The temperature readings at the top of each catalyst bed is used to control the flow of hydrogen quench above
that bed. Either the average of the temperature readings or the largest reading is sent to the temperature
controller [12].
The hydrocracker was designed for "flat" temperature profiles in the reactor [12]. A "flat" temperature profile is
one in which the average temperature of each catalyst bed is equal. This type of profile can be achieved by
injecting an adequate amount of quench gas between the catalyst beds, such that the outlet of each bed is
quenched back to the desired inlet temperature for the bed below. Operating with a "flat" temperature profile
maximizes catalyst life and product yields by minimizing coking and peak catalyst temperature.
In addition to providing temperature control, the temperatures measured at the top and bottom of each bed can
be used to monitor reaction progress and to indicate the location and extent of possible channelling of reactants.
Temperatures measured at different heights but at the same circumferential position (i.e., temperature difference
across a bed e.g. T2Bottom – T2Top) indicate the extent of reaction in the bed. Temperatures measured at the same
height but at different circumferential positions in the bed can indicate the location and extent of possible
maldistribution of reactants (e.g. T2Top – T5Top). For example, a hydrogen- or liquid-poor region may experience
a higher temperature rise because slower moving liquid has more time to react and will liberate more heat (local
hot spot). Hydrocracking operation may become unstable and a dangerous "runaway" condition may be
approached if the temperature change across any bed in a reactor is higher than 40°C [12]. Hence, the following
it is important to monitor the following the temperature indications for a hydrocracker reactor:
Level Average Temperature (LAT): LAT is the average temperature at a given elevation. LAT(x) = SUM [Ti(x)]/n. Where “x” is the level, “i” is the radial position and n is the temperature measuring instruments.
Bed Average Temperature (BAT): BAT is the average of the LAT of the bed. BAT(x) =
[LAT_in(x)+LAT_out(x)]/2. Where “x” is the bed. Bed ∆TU: Reactor Bed ∆T is the difference between LAT of each bed. Bed ∆𝐓𝐓 = LAT_out(x)-
LAT_in(x). Where “x” is the bed. 6
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Weighted Average Bed Temperature (WABT): WABT is the average of temperature of catalyst present
in the reactor, which is a measure of reaction severity. It is generally referred to as the sign of catalyst activation. WABT is calculated by the bed temperatures of reactors; each temperature contributes to WABT per catalyst weight distribution. WABT=0.25*BAT(1) + 0.35*BAT(2) + 0.40*BAT(3) where Bed 1 contains 25% weight of catalyst, Bed 2 contains 35% weight of catalyst and Bed 3 contains 40% weight of catalyst. Hence, the WABT is controlled by manipulation of individual bed average temperatures [4] [12].
Figure 2.2: Simplified PFD of Hydrocracker Reactor Showing Bed Temperature Control [12]
2.2.3 Limitations of the Current Control Strategy The primary control objective of the hydrocracker reactor is the regulation of the reaction severity, as measured
by the WABT, which is required to keep the per pass conversion within acceptable limits. WABT is controlled
by manipulation of individual bed average temperatures (BAT). Temperature controls in the hydrocracker
reactor in EGTL plant is done by different temperature control loops, dedicated to controlling the inlet
temperature of each bed by using quench hydrogen gas to cool down the incoming stream from the bed above.
Only the inlet of the first bed does not have quench hydrogen gas. The inlet temperature of the reactor is
controlled by varying the fuel gas flow to the preheat furnace using a temperature-flow cascade loop
arrangement.
There are several problems inherent with this current control setup. Firstly, a hydrocracker is a classic example
of a multiple-input, multiple-output (MIMO) control problem. That is, there are several manipulated variables
and several controlled variables. Examples of manipulated variables are fresh feed flow to the reactor, fuel gas
flow to the preheat furnace, setpoints for the two bed inlet temperatures and the reactor inlet temperature.
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Master of Engineering (Industrial Automation)
Examples of controlled variables are the three bed average temperatures, the reactor inlet temperature, the
reactor WABT, the three beds ∆T and the inlet quench valve position [11] [7]. A characteristic feature of
MIMO control problems is the presence of process interactions, that is, each manipulated variable can affect
several control variables. The traditional Proportional-Integral-Derivative (PID) feedback control loop is poor at
handling process interactions [7].
Secondly, there are several process constraints associated with the hydrocracker reaction. For instance, there is
allowable minimum and maximum quench valve opening required during normal operation. For example, it is
imperative that the valves on the quench gas used to cool the reactor catalyst beds not be more than 60 percent
open. These valve position constraints provide capacitance which allows the bed inlet temperature controllers to
handle large process disturbances which could cause a reactor temperature excursion. There is a maximum
allowable temperature change across any bed in the reactor during normal operation to prevent the
hydrocracking operation from becoming unstable or leading to temperature excursion [11]. There is a minimum
pressure permitted on the preheat furnace fuel gas header. Again, traditional PID feedback control loop is poor
at handling several process constraints.
2.2.4 Proposed Solution To improve on the hydrocracker reactor control, this paper proposes the implementation of an advanced process
control strategy, specifically a model predictive control (MPC) algorithm like dynamic matrix control (DMC).
Model predictive control (MPC) refers to a class of control algorithms that have a dynamic model of the process
programmed into the control architecture [13]. The DMC controller uses the dynamic process model to predict
the future response of the process based upon past controller moves and the current state of the process. At each
sample time, the next controller move is computed from a comparison of this predicted future behaviour with
the desired set point trajectory [7] [13]. The controller is a true multivariable controller. It considers all the
interactions between the independent manipulated variables and the dependent controlled variables across the
time horizon to steady state at the set points or dependent variable constraints [14].
The proposed DMC implementation will not replace the classical PID controllers, instead, it would be
implemented in a hierarchy above the traditional PID loops (refer to figure 3). In this manner, the DMC
controller drives set points for individual PID control loops based on selected control objectives [13]. For
instance, the control objective could be to maximize production of the most valuable products, in this case, the
DMC controller is switched to flat temperature profile mode. At other times, the control objective could be to
minimize energy usage. In such case, the DMC controller is switched to energy minimization mode. In both
cases, the DMC controller automatically generates the required set points for the underlying PID controllers to
achieve the control objectives [15].
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Master of Engineering (Industrial Automation)
Figure 2.3: Operation Hierarchy of MPC (From Georgia Institute of Technology)
PID = Proportional Integral Derivative; FC = Flow Controller; PC = Pressure Controller; TC = Temperature Controller and
LC = Level Controller.
2.3 What is Model Predictive Control (MPC)
MPC is a form of model based controller in which the current control action is obtained by solving a finite
horizon cost function on-line, with the aid of a linear or non-linear dynamic predictive model [13], without
violating any constraints. It is located on the middle layer of the hierarchical control system architecture (See
figure 2.3 above). Model predictive control (MPC) belongs to a class of computer control algorithms, more
specifically optimal control methods which are using mathematical model of the process to predict the future
response of process on a sequence of control variable manipulations [17] [24] [27]. Once the predictions are
made, the control algorithm with usage of optimization techniques computes appropriate control actions to
provide desired output behavior of the process in optimal fashion. Colloquially we can describe this method as a
“look ahead” strategy, when the controller is able to foresee a future behavior of the process with usage of given
knowledge about that particular process and consequently evaluate the optimal control strategy to achieve the
best possible outcome, which are satisfying long term goals and criteria. This strategy stands in contrast with
classical control theory techniques e.g. PID controllers, which are able to achieve only short-term goals set in
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Master of Engineering (Industrial Automation)
actual time, resulting in costlier and often unsatisfactory long-term performance [17]. The figure 2.4 below
shows the basic structure of a model predictive controller.
Figure 2.4: Basic MPC Structure (From Georgia Institute of Technology)
2.3.1 History and Evolution of MPC This section will be devoted to brief history and evolution of Model Predictive Control, from early academia
based concepts of optimal control theory, giving the birth to very first industrial based control applications
using MPC technology. More comprehensive historical survey of industrial MPC can be found in article Qin
and Badgwell (2003) [6], from where the inspiration for this whole section was taken. Moreover, the simplified
evolution of industrial MPC algorithms is captured of Figure 2.5, forming a structural backbone for this section.
[16].
Figure 2.5: Simplified evolutionary tree of the most significant industrial MPC algorithms
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Master of Engineering (Industrial Automation)
Early Optimal Control Theory:
LQG (Linear Quadratic Gaussian) was developed by Kalman et al., in 1964. MPC is said to have stemmed from
this control scheme. It was used to solve an unconstrained infinite horizon Riccati equation. LQG’s infinite
prediction horizon gives it a powerful stability property, but its low impact in the process industry is due to lack
of constraint handling capability, poor performance due to process nonlinearities, model uncertainty
(robustness), and so on [26].
First Generation MPC:
IDCOM (Identification and Command) or MPHC (Model Predictive Heuristic Control) was developed by
Richalet et al., in 1976. The main features are; linear FIR model Input/output representation, ability to handle
constraint, optimal non-linear inputs computed using a heuristic iterative algorithm, hence, the name MPHC.
This type of MPC falls under the first generation of MPC.
DMC (Dynamic Matrix Control) was developed by Shell Oil Engineers Cutler and Ramaker in 1979 [17] [5].
The main features are; linear FSR model Input-output representation, unconstrained quadratic performance
objective solved over a finite prediction horizon, optimal inputs computed as the solution to a least squares
problem. This MPC falls under the first generation of MPC.
Second Generation MPC:
QDMC (Quadratic Dynamic Matrix Control) was developed by Shell Oil Engineers Cutler et al., in 1983. The
main features are; Explicit Hard constraints handling (improved DMC), linear FSR model for the plant, optimal
inputs computed as the solution to a quadratic program. This MPC falls under the second generation of MPC.
Third Generation MPC:
IDCOM-M, HIECON (Hierarchical Constraint Control), SMCA (Setpoint Multivariable Control Architecture)
and SMOC (Shell Multivariable Optimizing Controller). These were developed in the late 1980-1990s. They
were developed due to the problem of infeasibility in solving the quadratic program in QDMC. Note, SMCA is
a later version of IDCOM-M. These types of MPC fall under the third generation of MPC.
The main features for IDCOM-M/SMCA and HIECON are; linear FIR model for the plant, two
separate quadratic objective functions for inputs and outputs, handles hard and soft constraints with
hard constraints prioritize, screen out ill-conditioned, and single move is computed for each input.
The main features for SMOC are; State space representation, consists of a Kalman filter, Input and
output constraints are handled in a quadratic program, and so on.
Fourth Generation MPC:
DMC-plus and RMPCT were developed after the year 2000. These types of MPC fall under the four generation
of MPC. Their main features are; Improved identification technology, consideration of model uncertainty
(robust control design), consists of windows-based graphical user interfaces, and so on.
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Master of Engineering (Industrial Automation)
2.3.2 Advantages and Disadvantages of MPC [18] [19] Advantages:
MPC can handle multivariable, MIMO as well as SISO processes.
It can handle non-minimal phase and unstable processes.
It takes account of actuator limitations (constraints) in its cost function.
It allows operation closer to constraints, hence increased Profit.
It handles structural changes and can be used alongside PIDs.
It is easy to tune.
Disadvantages:
There is difficulty in obtaining the predictive model.
Computational burden arising from choice of input horizon length makes it preferable for slow
processes.
2.3.3 Characteristics of MPC It uses a dynamic model to predict future responses and thus provide control action.
Its discrete time framework makes it compatible with digital computers.
It only implements the first move in the optimal control scheme and discards the rest (Receding
Horizon).
It handles time-domain performance specification in its cost function.
It also handles time-domain constraints while solving the control problem.
2.3.4 Formulation of MPC Algorithm Since the cost function is quadratic, the optimization problem is solved using MATLAB QUADPROG function.
The MATLAB QUADPROG function returns incremental inputs for a particular cost function format. Thus, the
actual quadratic cost function must be changed into a format that the QUADPROG function can understand.
The process of obtaining the parameters for the QUADPROG cost function is mostly based on works by [20]
and also [21].
Consider a model
𝑥𝑥𝑡𝑡+1 = 𝐴𝐴𝑥𝑥𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … … … … . . (2.1)
𝑦𝑦𝑡𝑡 = 𝐶𝐶𝑥𝑥𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … … … … … … (2.2)
MPC usually requires estimates of the state and/or output over the entire prediction horizon from time t + 1
until time t + N, and can only make these predictions based on information up to and including the current time
t.
𝑥𝑥�𝑡𝑡+𝑖𝑖+1|𝑡𝑡 = 𝐴𝐴𝑥𝑥�𝑡𝑡+𝑖𝑖|𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡+𝑖𝑖|𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … . (2.3)
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Master of Engineering (Industrial Automation)
𝑦𝑦�𝑡𝑡+𝑖𝑖|𝑡𝑡 = 𝐶𝐶𝑥𝑥�𝑡𝑡+𝑖𝑖|𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑡𝑡+𝑖𝑖|𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … . . (2.4)
𝑓𝑓𝑓𝑓𝑓𝑓 𝑖𝑖 = 1, … … ,𝑁𝑁
Equation (2.3) can be expanded in terms of the initial state 𝑥𝑥�𝑡𝑡+1|𝑡𝑡 and future control actions 𝑃𝑃𝑡𝑡+𝑖𝑖|𝑡𝑡 as follows.
𝑥𝑥�𝑡𝑡+2|𝑡𝑡 = 𝐴𝐴𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡+1|𝑡𝑡
𝑥𝑥�𝑡𝑡+3|𝑡𝑡 = 𝐴𝐴𝑥𝑥�𝑡𝑡+2|𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡+2|𝑡𝑡
= 𝐴𝐴�𝐴𝐴𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡+1|𝑡𝑡� + 𝐵𝐵𝑃𝑃𝑡𝑡+2|𝑡𝑡 (Substituting for 𝑥𝑥�𝑡𝑡+2|𝑡𝑡)
= 𝐴𝐴2𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐴𝐴𝐵𝐵𝑃𝑃𝑡𝑡+1|𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡+2|𝑡𝑡
.
.
.
𝑥𝑥�𝑡𝑡+𝑗𝑗|𝑡𝑡 = 𝐴𝐴𝑗𝑗−1𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + ∑ 𝐴𝐴𝑗𝑗−𝑘𝑘−1𝐵𝐵𝑃𝑃𝑡𝑡+𝑘𝑘|𝑡𝑡.𝑗𝑗−1𝑘𝑘=1
Now in terms of predicting the output, Equation (2.4) can be expanded in terms of the above expression
for 𝑥𝑥�𝑡𝑡+𝑗𝑗|𝑡𝑡, which results in a series of equations that provide optimal output predictions. The key point to note is
that each output prediction is a function of the initial state 𝑥𝑥�𝑡𝑡+1|𝑡𝑡 and future inputs 𝑃𝑃�𝑡𝑡+𝑖𝑖|𝑡𝑡 only.
𝑦𝑦�𝑡𝑡+1|𝑡𝑡 = 𝐶𝐶𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑡𝑡+1|𝑡𝑡
𝑦𝑦�𝑡𝑡+2|𝑡𝑡 = 𝐶𝐶𝑥𝑥�𝑡𝑡+2|𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑡𝑡+2|𝑡𝑡
= 𝐶𝐶�𝐴𝐴𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡+1|𝑡𝑡� + 𝐷𝐷𝑃𝑃𝑡𝑡+2|𝑡𝑡 (Substituting for 𝑥𝑥�𝑡𝑡+2|𝑡𝑡)
= 𝐶𝐶𝐴𝐴𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐶𝐶𝐵𝐵𝑃𝑃𝑡𝑡+1|𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑡𝑡+2|𝑡𝑡
.
.
.
𝑦𝑦�𝑡𝑡+𝑗𝑗|𝑡𝑡 = 𝐶𝐶𝐴𝐴𝑗𝑗−1𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝐶𝐶�∑ 𝐴𝐴𝑗𝑗−𝑘𝑘−1𝐵𝐵𝑃𝑃𝑡𝑡+𝑘𝑘|𝑡𝑡𝑗𝑗−1𝑘𝑘=1 �+ 𝐷𝐷𝑃𝑃𝑡𝑡+𝑗𝑗|𝑡𝑡
This series of output prediction equations can be stated in an equivalent but more convenient manner using
matrix vector notation. Let
𝑌𝑌𝑡𝑡 ≜ �
𝑦𝑦�𝑡𝑡+1|𝑡𝑡..
𝑦𝑦�𝑡𝑡+𝑁𝑁|𝑡𝑡
�, 𝑈𝑈𝑡𝑡 ≜ �
𝑃𝑃𝑡𝑡+1|𝑡𝑡..
𝑃𝑃𝑡𝑡+𝑁𝑁|𝑡𝑡
�,
and
Λ =
⎣⎢⎢⎢⎢⎡
𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶𝐴𝐴2
.
.𝐶𝐶𝐴𝐴𝑁𝑁−1⎦
⎥⎥⎥⎥⎤
, Φ =
⎣⎢⎢⎢⎢⎡ 𝐷𝐷
𝐶𝐶𝐵𝐵 𝐷𝐷𝐶𝐶𝐴𝐴𝐵𝐵 𝐶𝐶𝐵𝐵 𝐷𝐷
. .
.𝐶𝐶𝐴𝐴𝑁𝑁−2𝐵𝐵 . . 𝐶𝐶𝐵𝐵 𝐷𝐷⎦
⎥⎥⎥⎥⎤
.
Then,
𝑌𝑌𝑡𝑡 = Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + Φ𝑈𝑈𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … … … … . (2.5)
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Master of Engineering (Industrial Automation)
MPC is really no different from many other forms of feedback control in that the overarching goal is to reject
disturbances whilst tracking a reference signal and at the same time ensuring that input energy (used for
actuation) is sensible.
All control objectives can be represented mathematically using a cost function with a restricted domain, i.e. the
cost function obeys certain constraints (limits), or without a restricted domain, i.e. like the one below.
The cost function should contain a part whose dual effect is handling reference tracking and disturbance
rejection, that is, by penalizing any deviations of the output from the reference signal. The cost function should
also contain a part that handles minimization of actuator input energy, that is, by penalizing the actuator input
moves, since even within limits some actuation energy can be undesirable. See cost function below.
Note that constraints have not been explicitly included in the cost function below, because QUADPROG
handles them in a different format.
𝐽𝐽�𝑥𝑥�𝑡𝑡+1|𝑡𝑡,𝑈𝑈𝑡𝑡� ≜ 12∑ �𝑦𝑦�𝑡𝑡+𝑘𝑘|𝑡𝑡 − 𝑓𝑓𝑡𝑡+𝑘𝑘�𝑄𝑄
2 + �𝑃𝑃𝑡𝑡+𝑘𝑘|𝑡𝑡 − 𝑃𝑃𝑡𝑡+𝑘𝑘−1|𝑡𝑡�𝑃𝑃2𝑁𝑁
𝑘𝑘=1 … … … … … … … (2.6)
Now, the aim is to represent this cost function in a format that QUADPROG will understand, which is
𝐽𝐽�𝑥𝑥�𝑡𝑡+1|𝑡𝑡,𝑈𝑈𝑡𝑡� = 12𝑈𝑈𝑡𝑡𝑇𝑇𝐻𝐻𝑈𝑈𝑡𝑡 + 𝑈𝑈𝑡𝑡𝑇𝑇𝑓𝑓 + 𝐶𝐶3 … … … … … … … … … … … … … … … … … … . . (2.7)
Where 12𝑈𝑈𝑡𝑡𝑇𝑇𝐻𝐻𝑈𝑈𝑡𝑡 and 𝑈𝑈𝑡𝑡𝑇𝑇𝑓𝑓 are the quadratic and linear parts of equation (2.7) respectively. C3 can be safely
ignored.
Now, taking the first part of equation (2.6), and using vectors Yt, alongside Rt given below
𝑅𝑅𝑡𝑡 ≜ �
𝑓𝑓𝑡𝑡+1..
𝑓𝑓𝑡𝑡+𝑁𝑁
�
12∑ �𝑦𝑦�𝑡𝑡+𝑘𝑘|𝑡𝑡 − 𝑓𝑓𝑡𝑡+𝑘𝑘�𝑄𝑄
2𝑁𝑁𝑘𝑘=1 = 1
2‖𝑌𝑌𝑡𝑡 − 𝑅𝑅𝑡𝑡‖𝑄𝑄2 (Substituting for 𝑌𝑌𝑡𝑡 = Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + Φ𝑈𝑈𝑡𝑡)
12
[(𝑌𝑌𝑡𝑡 − 𝑅𝑅𝑡𝑡)𝑇𝑇𝑄𝑄�(𝑌𝑌𝑡𝑡 − 𝑅𝑅𝑡𝑡)𝑇𝑇] = 12��Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + Φ𝑈𝑈𝑡𝑡 − 𝑅𝑅𝑡𝑡�
𝑇𝑇𝑄𝑄��Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + Φ𝑈𝑈𝑡𝑡 − 𝑅𝑅𝑡𝑡�𝑇𝑇�
12𝑈𝑈𝑡𝑡𝑇𝑇Φ𝑇𝑇𝑄𝑄�Φ𝑈𝑈𝑡𝑡 + 𝑈𝑈𝑡𝑡𝑇𝑇�Φ𝑇𝑇𝑄𝑄�Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 − Φ𝑇𝑇𝑄𝑄�𝑅𝑅𝑡𝑡�+ 𝐶𝐶1 … … … … … … … … … … … … . . (2.8)
Note that C1 is a constant term that is not dependent on Yt or Rt.
𝑄𝑄� ≜
⎣⎢⎢⎢⎡𝑄𝑄 𝑄𝑄
..
𝑄𝑄⎦⎥⎥⎥⎤
Now, considering the second part of the cost function above 12∑ �𝑃𝑃𝑡𝑡+𝑘𝑘|𝑡𝑡 − 𝑃𝑃𝑡𝑡+𝑘𝑘−1|𝑡𝑡�𝑃𝑃
2𝑁𝑁𝑘𝑘=1 = 1
2𝑈𝑈𝑡𝑡𝑇𝑇𝑃𝑃�𝑈𝑈𝑡𝑡 − 𝑃𝑃𝑡𝑡+1|𝑡𝑡
𝑇𝑇𝑃𝑃𝑃𝑃𝑡𝑡 + 𝐶𝐶2 … … … … … … … … … (2.9)
Note that C2 is a constant term that is not dependent on Ut.
Where 𝑃𝑃� ≜
⎣⎢⎢⎢⎢⎡2𝑃𝑃 −𝑃𝑃−𝑝𝑝 2𝑃𝑃 −𝑃𝑃
. . .. . .
−𝑃𝑃 2𝑃𝑃 −𝑃𝑃−𝑃𝑃 𝑃𝑃 ⎦
⎥⎥⎥⎥⎤
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Master of Engineering (Industrial Automation)
Next, for 12∑ �𝑦𝑦�𝑡𝑡+𝑘𝑘|𝑡𝑡 − 𝑓𝑓𝑡𝑡+𝑘𝑘�𝑄𝑄
2𝑁𝑁𝑘𝑘=1 and 1
2∑ �𝑃𝑃𝑡𝑡+𝑘𝑘|𝑡𝑡 − 𝑃𝑃𝑡𝑡+𝑘𝑘−1|𝑡𝑡�𝑃𝑃
2𝑁𝑁𝑘𝑘=1 in equation (2.6), substitute equation (2.8)
and (2.9) respectively to get,
𝐽𝐽�𝑥𝑥�𝑡𝑡+1|𝑡𝑡,𝑈𝑈𝑡𝑡� ≜ 12𝑈𝑈𝑡𝑡𝑇𝑇Φ𝑇𝑇𝑄𝑄�Φ𝑈𝑈𝑡𝑡 + 1
2𝑈𝑈𝑡𝑡𝑇𝑇𝑃𝑃�𝑈𝑈𝑡𝑡 + 𝑈𝑈𝑡𝑡𝑇𝑇�Φ𝑇𝑇𝑄𝑄�Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 − Φ𝑇𝑇𝑄𝑄�𝑅𝑅𝑡𝑡� − 𝑃𝑃𝑡𝑡+1|𝑡𝑡
𝑇𝑇𝑃𝑃𝑃𝑃𝑡𝑡 + 𝐶𝐶3
The above equation now becomes equation (2.10), where C3 is a summation of C1 and C2, and can be safely
ignored.
Comparing equation (2.7) and equation (2.10),
𝐻𝐻 = Φ𝑇𝑇𝑄𝑄�Φ + 𝑃𝑃� … … … … … … … … … … … … … … … … … … … … … … … … … … . . . (2.11)
𝑓𝑓 = Γ �𝑥𝑥�𝑡𝑡+1|𝑡𝑡𝑅𝑅𝑡𝑡
� −
⎣⎢⎢⎢⎡𝑃𝑃𝑃𝑃𝑡𝑡
0..0 ⎦⎥⎥⎥⎤
… … … … … … … … … … … … … … … … … … … … … . . … … . . (2.12)
Γ = �Φ𝑇𝑇𝑄𝑄�Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 − Φ𝑇𝑇𝑄𝑄�𝑅𝑅𝑡𝑡�… … … … … … … … … … … … … … … … … … … … … . . (2.13)
It is easy to see that H can be computed off-line since it is made of matrices that do not change frequently. Since
H is symmetric by construction, only half of it needs to be stored. Looking at f, it can be seen that only some
part of it can be computed off-line since 𝑥𝑥�𝑡𝑡+1|𝑡𝑡 ,𝑃𝑃𝑡𝑡 , and 𝑅𝑅𝑡𝑡 change often due to MPC computation.
Next, we look at constraint handling technique using QUADPROG.
Input Constraints
The input constraints can be modelled using the linear inequality given below.
𝑏𝑏𝑙𝑙 ≤ 𝑈𝑈𝑡𝑡 ≤ 𝑏𝑏𝑢𝑢
Which means that 𝑈𝑈𝑡𝑡 ≤ 𝑏𝑏𝑢𝑢 and 𝑈𝑈𝑡𝑡 ≥ 𝑏𝑏𝑙𝑙. If 𝑈𝑈𝑡𝑡 ≥ 𝑏𝑏𝑙𝑙 is re-written as −𝑈𝑈𝑡𝑡 ≤ 𝑏𝑏𝑙𝑙, then
� 𝐼𝐼−𝐼𝐼�𝑈𝑈𝑡𝑡 ≤ �𝑏𝑏𝑢𝑢𝑏𝑏𝑙𝑙� ≡ 𝐿𝐿𝑈𝑈𝑡𝑡 ≤ 𝑏𝑏… … … … … … … … … … … … … … … … … … … … … … (2.14)
Output Constraints
The output constraints can be modelled in terms of 𝑈𝑈𝑡𝑡 using the linear inequality given below.
𝑀𝑀𝑌𝑌𝑡𝑡 ≤ 𝐶𝐶
Following equation (3.26), 𝑌𝑌𝑡𝑡 can be substituted as Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + Φ𝑈𝑈𝑡𝑡 to get
𝑀𝑀Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 + 𝑀𝑀Φ𝑈𝑈𝑡𝑡 ≤ 𝐶𝐶
∴ 𝑀𝑀Φ𝑈𝑈𝑡𝑡 ≤ 𝐶𝐶 − 𝑀𝑀Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … . . … . (2.15)
Combine equation (3.35) and equation (3.36) to get
� 𝐿𝐿𝑀𝑀Φ�𝑈𝑈𝑡𝑡 ≤ �𝑏𝑏
𝐶𝐶 − 𝑀𝑀Λ𝑥𝑥�𝑡𝑡+1|𝑡𝑡� ≡ 𝐿𝐿𝑖𝑖𝑖𝑖𝑈𝑈𝑡𝑡 ≤ 𝑏𝑏𝑖𝑖𝑖𝑖 … … … … … … … … … … … … … … … (2.16)
Thus, with constraints decided and computed, the optimal trajectory, denoted 𝑈𝑈𝑡𝑡∗, is obtained from the
QUADPROG solution of the quadratic programming problem:
𝑈𝑈𝑡𝑡∗ ≜ 𝐽𝐽�𝑥𝑥�𝑡𝑡+1|𝑡𝑡,𝑈𝑈𝑡𝑡�𝑠𝑠. 𝑡𝑡 𝐿𝐿𝑖𝑖𝑖𝑖𝑈𝑈𝑡𝑡 ≤ 𝑏𝑏𝑖𝑖𝑖𝑖
… … … … … … … … … … … … … … … … … … … … … … … … … . . (2.17)
MPC incorporating Integral Control
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A common objective shared by both case studies, discussed earlier, is zero offset tracking. In practice, the best
way to achieve this is to introduce integral action to the closed-loop system. Note that introducing integral
action to the closed-loop system will not be possible without reconsidering the model of the plant.
Considering the model with output noise and state noise below,
𝑥𝑥𝑡𝑡+1 = 𝐴𝐴𝑥𝑥𝑡𝑡 + 𝐵𝐵𝑃𝑃𝑡𝑡 + 𝑤𝑤𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … . . (2.18)
𝑦𝑦𝑡𝑡 = 𝐶𝐶𝑥𝑥𝑡𝑡 + 𝐷𝐷𝑃𝑃𝑡𝑡 + 𝑑𝑑𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … … … . . (2.19)
Now to design the observer for the controller, state noise is not considered, while the estimated model
disturbance �̂�𝑑𝑡𝑡+1|𝑡𝑡 is to be considered constant, with value equal to the difference between the actual outputs
and the observer outputs.
∴ �̂�𝑑𝑡𝑡+1|𝑡𝑡 = 𝑦𝑦𝑡𝑡+1 − 𝑦𝑦�𝑡𝑡+1|𝑡𝑡 ≡ 𝑑𝑑𝑡𝑡 … … … … … … … … … … … … … … … … … … … . . (2.20)
The model for the observer is designed considering 𝑑𝑑𝑡𝑡, therefore, the matrices that make up the observer are
augmented, thus inducing integral modes. Note that the optimal control move is also considered for zero offset
tracking, and thus included in the augmented matrices.
The augmented states thus are
�̅�𝑥𝑡𝑡 = �𝑥𝑥𝑡𝑡𝑑𝑑𝑡𝑡𝑃𝑃𝑡𝑡−1
� �̅�𝐴 = �𝐴𝐴 0 𝐵𝐵0 𝐼𝐼 00 0 𝐼𝐼
� 𝐵𝐵� = �𝐵𝐵0𝐼𝐼�
𝑦𝑦�𝑡𝑡 = �𝑦𝑦𝑡𝑡𝑃𝑃𝑡𝑡� 𝐶𝐶̅ = �𝐶𝐶 𝐼𝐼 𝐷𝐷
0 0 𝐼𝐼 � 𝐷𝐷� = �𝐷𝐷𝐼𝐼 �
Thus, the model for the observer is given below as
�̅�𝑥𝑡𝑡+1 = �̅�𝐴�̅�𝑥𝑡𝑡 + 𝐵𝐵�𝑃𝑃�𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … … … . . (2.21)
𝑦𝑦�𝑡𝑡 = 𝐶𝐶̅�̅�𝑥𝑡𝑡 + 𝐷𝐷�𝑃𝑃�𝑡𝑡 … … … … … … … … … … … … … … … … … … … … … … … … … . . (2.22)
[20] and [21] have also considered similar approaches in the course of their research. By modelling the plant
disturbances as integrated white noise, integral states are added into the controller which guarantees zero offset
tracking, provided the closed-loop system is asymptotically stable.
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CHAPTER 3 METHODOLOGY
3.0 Modelling of Plant Dynamic System Using Historical Data
Implementation of MPC will follow a well-defined path starting from selection of variables through to
controller design and commissioning. Since model predictive control uses the internal model of the plant to be
controlled, the MPC controller depends on how good the model is, hence, a great deal of effort is invested into
developing a model that accurately captures the plant dynamics. There are two major ways of obtaining the
dynamic model of a process. Plant model can be built from first principles if the mathematical formula that
represents the plant is known. This is known as first principle modelling. There are several commercially
available software applications that can be used in developing the process models. However, due to the
complexity of most processes, it is often difficult to build such mathematical models. Alternatively, we can
develop plant dynamic model by using input and output data collected from the plant step response test and use
any available system identification software application to obtain a linear model describing the behaviour of the
plant. This is referred to as data-driven modelling.
For this research, the process model of the hydrocracker reactor was developed using historical data of the
process. Data was collected from the Process Information (PI) historian. The historical data ranging for a period
of about nine (9) months was carefully analysed and used for system identification. The following sections will
outline the methodology for the model identification and controller design.
3.1 Step 1: Selection of variables The first step in MPC design is to select the controlled variables and the manipulated variables. These choices
determine the structure of the MPC control system and should be based on process knowledge and control
objectives. For the hydrocracker reactor, three (3) manipulated variables and four (4) controlled variables were
selected. The three manipulated variables were the DCS setpoints for the three bed inlet temperatures. The four
controlled variables were the reactor WABT and the three bed ABTs.
3x Manipulated Variables (MVs):
- Bed 1 Inlet Temperature Setpoint (MV1)
- Bed 2 Inlet Temperature Setpoint (MV2)
- Bed 3 Inlet Temperature Setpoint (MV3)
4x Controlled Variables (CVs):
- Reactor WABT (CV1)
- Bed 1 ABT (CV2)
- Bed 2 ABT (CV3)
- Bed 3 ABT (CV4)
To simplify data Analysis and limit the transfer function matrix for this process to a 2x2 matrix, only MV1,
MV2, CV1 and CV2 were considered for analysis.
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Table 3.1: Raw process data from PI historian showing interactions between MV1 and CVs
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Table 3.2: Raw process data from PI historian showing interactions between MV2 and CVs
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Table 3.3: Raw process data from PI historian showing interactions between MV3 and CVs
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3.2 Step 2: Plant Test Typically, step response tests are performed on live plant to gather plant input and output data. However, for
this thesis, since the researcher used plant historical data, the raw plant data collected in step 1 above was
carefully analysed to identify points where a step change was made on the manipulated variables and what
effects it had on all the controlled variables. This will help us establish the degree of interactions that exist
between the MV and the CVs.
To test for the interaction between MV1 and CV1 and CV2, the researcher analysed the raw data for a region
where MV2 is constant to isolate the effect of MV2 on the controlled variables. In the same vein, to test for
interaction between MV2 and CV1 and CV2, the researcher analysed the raw data for a region where MV2 is
constant. A trend of the interactions between the manipulated variables and the controlled variables are shown
in the figures below:
Figure 3.1: Plant historical data showing interactions between MV1 and CV1 and CV2
Figure 3.2: Plant historical data showing interactions between MV2 and CV1 and CV2
From figure 3.1 we observed strong interactions between MV1 and CV1 and CV2. When we have a step change
in the bed 1 inlet temperature setpoint (MV1), we can see the reactor WABT (CV1) and the bed 1 ABT (CV2)
closely tracking MV1. However, we can see the limitations of using sets of conventional PID loops to control a
highly interactive process like this. Hence, there is significant offset between the MVs and CVs. In the next
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step, we will analyse the data to extract useful input-output relationship between the manipulated variables and
the controlled variables using the MATLAB system identification toolbox.
3.3 Step 3: Model Estimation There are commercial software packages available that can be used for system identification. For this work, we
used MATLAB System Identification Toolbox for the dynamic model estimation. With the System
Identification Toolbox, it is easy to create mathematical models of dynamic systems from measured input-
output data especially useful for systems that cannot be easily modelled from first principles. This allows the
use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer
functions, process models, and state-space models [22] [28]. Below are the steps taken to estimate the model:
Import time-domain data
Analyze and process data
Determine suitable model structure and order, and estimate model parameters
Validate model accuracy
Import time-domain Data: The first step was to import the input-output data from experiment into MATLAB
workspace as column vectors. MV1, CV11 and CV21 were imported as 1593x1 column vectors. Where CV11
is the change in CV1 resulting from step change in MV1 and CV21 is the change in CV2 resulting from step
change in MV1. Similarly, MV2, CV12 and CV22 were imported as 2049x1 column vectors. Where CV12 is
the change in CV1 resulting from step change in MV2 and CV22 is the change in CV2 resulting from step
change in MV2. These input-output vectors are now imported into the System Identification app as time-
domain data for further processing. When importing data for identifying models, the input-output channel
names and the sampling time (sampling rate = 1 sample per 30s) were specified. The researcher created four
data objects from various input-output combinations to depict the interactions between the manipulated
variables and the controlled variables.
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Figure 3.3: Time domain input-output data for MV1 and CV1.
Figure 3.4: Time domain input-output data for MV1 and CV2.
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Figure 3.5: Time domain input-output data for MV2 and CV1.
Figure 3.6: Time domain input-output data for MV2 and CV2.
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Analyze and Process Data: Measured data often has offsets, slow drifts, outliers, missing values, and other
anomalies. The toolbox removes such anomalies by performing operations such as detrending, filtering,
resampling, and reconstruction of missing data. The toolbox can analyze the suitability of data for identification
and provide diagnostics on the persistence of excitation, existence of feedback loops, and presence of
nonlinearities.
Figure 3.7: System Identification Toolbox showing imported data and estimated models.
MV1CV1: State-space model representing interactions between MV1 and CV1
MV1CV2: State-space model representing interactions between MV1 and CV2
MV2CV1: State-space model representing interactions between MV2 and CV1
MV2CV1: State-space model representing interactions between MV2 and CV2
Estimate Model Parameters: Parametric models, such as transfer functions or state-space models, use a small
number of parameters to capture system dynamics. System Identification Toolbox estimates model parameters
and their uncertainties from time-response and frequency-response data. You can analyze these models using
time-response and frequency-response plots, such as step, impulse, Bode plots, and pole-zero maps. [22]
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For this work, the researcher estimated the internal plant model using state-space model due it’s robustness and
better fit to estimation data compared to the single pole transfer function model. For instance, for MV1CV1, the
state space model has a fit to estimation data of 99.42% compared to transfer function model’s fit to estimation
data of 74.75%. the table below summarizes the comparison between state-space model and transfer function
model in terms of fit to estimation data.
Table 3.4: Comparison between State-space mode and transfer function model fit to estimate data
State-Space Model Transfer Function Model
MV1CV1 99.42% 74.75%
MV1CV2 99.41% 75.12%
MV2CV1 97.73% 54.71%
MV2CV2 99.67% 65.29%
Continuous time identified state space model for MV1CV1:
dxdt
= A x(t) + B u(t) + k e(t)
y(t) = C x(t) + D u(t) + e(t)
ssMV1CV1 =
A = �−0.006453 −0.021490.009955 −0.04279�
B = � 0.000487−0.001513�
C = [9.293 −0.07935]
D = [0]
K = �0.07329−1.005 �
Status:
Estimated using N4SID on time domain data "MV1CV11".
Fit to estimation data: 99.42% (prediction focus)
FPE: 2.871e-05, MSE: 2.843e-05
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Figure 3.8: Step response plot for MV1CV1. (Settling time to steady state = 340s)
Continuous time identified state space model for MV1CV2:
dxdt
= A x(t) + B u(t) + k e(t)
y(t) = C x(t) + D u(t) + e(t)
ssMV1CV2 =
A = �−0.004084 −0.031370.02183 −0.04543�
B = �0.0001111−0.002964�
C = [8.973 −0.1189]
D = [0]
K = �0.0647−1.082�
Status:
Estimated using N4SID on time domain data "MV1CV31".
Fit to estimation data: 99.41% (prediction focus)
FPE: 2.998e-05, MSE: 2.968e-05 27
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Figure 3.9: Step response plot for MV1CV2. (Settling time to steady state = 146s)
Continuous time identified state space model for MV2CV1:
dxdt
= A x(t) + B u(t) + k e(t)
y(t) = C x(t) + D u(t) + e(t)
ssMV2CV1 =
A = �−0.02172 −0.037280.02884 −0.2332 �
B = �0.0002648−0.0224 �
C = [6.868 −0.02036]
D = [0]
K = � 0.1327−0.1209�
Status:
Estimated using N4SID on time domain data "MV1CV21".
Fit to estimation data: 97.73% (prediction focus)
FPE: 0.0003934, MSE: 0.0003895
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Figure 3.10: Step response plot for MV2CV1. (Settling time to steady state = 197s)
Continuous time identified state space model for MV2CV2:
dxdt
= A x(t) + B u(t) + k e(t)
y(t) = C x(t) + D u(t) + e(t)
ssMV2CV2 =
A = �−0.003512 −0.019640.01637 −0.02136�
B = �0.0001918−0.001292�
C = [13.69 −0.1244]
D = [0]
K = �0.02972−1.244 �
Status:
Estimated using N4SID on time domain data "MV1CV41".
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Fit to estimation data: 99.67% (prediction focus)
FPE: 1.055e-05, MSE: 1.045e-05
Figure 3.11: Step response plot for MV1CV1. (Settling time to steady state = 390s)
Overall Plant Response Model:
The overall plant response model is a combination of all the individual interactions between the manipulated
variables and the controlled variables – that is, a combination of MV1CV1, MV1CV2, MV2CV1 and
MV2CV2.
Overall Continuous time state space model:
ssMVCV_P_C = �ssMV1CV1 ssMV1CV2ssMV2CV1 ssMV2CV2�
ssMVCV_P_C =
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A =
x1 x2 x3 x4 x5 x6 x7 x8
x1 -0.006453 -0.02149 0 0 0 0 0 0
x2 0.009955 -0.04279 0 0 0 0 0 0
x3 0 0 -0.02172 -0.03728 0 0 0 0
x4 0 0 0.02884 -0.2332 0 0 0 0
x5 0 0 0 0 -0.004084 -0.03137 0 0
x6 0 0 0 0 0.02183 -0.04543 0 0
x7 0 0 0 0 0 0 -0.003512 -0.01964
x8 0 0 0 0 0 0 0.01637 -0.02136
B =
u1 u1
x1 0.000487 0
x2 -0.001513 0
x3 0 0.0002648
x4 0 -0.0224
x5 0.0001111 0
x6 -0.002964 0
x7 0 0.0001918
x8 0 -0.001292
C =
x1 x2 x3 x4 x5 x6 x7 x8
y1 9.293 -0.07935 6.868 -0.02036 0 0 0 0
y1 0 0 0 0 8.973 -0.1189 13.69 -0.1244
D =
u1 u1
y1 0 0
y1 0 0
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Figure 3.12b: Combined plant step response model.
Validate Model Accuracy: System Identification Toolbox helps validate the accuracy of identified
models using independent sets of measured data from a real system. For a given set of input data, the toolbox
computes the output of the identified model and lets you compare that output with the measured output from a
real system. You can also view the prediction error and produce time-response and frequency-response plots
with confidence bounds to visualize the effect of parameter uncertainties on model responses. [22]
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Figure 3.13: Model validation plot showing Best Fits for MV1CV1 (Best Fits = 84.48%)
Figure 3.14: Model validation plot showing Best Fits for MV1CV2 (Best Fits = 79.2%)
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Figure 3.15: Model validation plot showing Best Fits fir MV2CV1 (Best Fits = 89.08%)
Figure 3.16: Model validation plot showing Best Fits fir MV2CV2 (Best Fits = 71.2%)
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3.4 Step 4: Design of MPC Controller The controller design was done using MPC Designer in MATLAB Model Predictive Control Toolbox. The
MPC design is based on the control and optimization objectives, process constraints, and the dynamic model of
the process. The MPC design parameters will be selected, including the sampling periods, weighting factors,
and control and prediction horizons. Next, the closed-loop system is simulated using the identified process
model and a wide variety of process conditions to evaluate control system performance. The MPC design
parameters are adjusted, if necessary, to obtain satisfactory control system performance and robustness over the
specified range of operating conditions [21] [30]. The next chapter will delve deep into controller design using
MPC designer. MPC controller will be tested using various simulated scenarios.
3.5 Step 5: Commissioning of MPC Controller The commissioning of the MPC controller can be done in two stages. In the first stage, the just completed MPC
controller is implemented on a simulator. A simulator is basically an imitation of the actual process/plant, which
is usually the prediction model used in the MPC controller. It is expected that since there is no model mismatch
between the simulator and the MPC controller, satisfactory performance will be obtained once the tuning
parameters are chosen well. During this stage, several scenarios are tested for set-point tracking and disturbance
rejection capabilities, during which infeasibility issues are handled by prioritizing and/or softening constraints
on the MVs and CVs.
In the second stage, after the controller has been satisfactorily evaluated on the simulator, then the controller is
implemented on the actual process. At this stage, there is little or no confidence in the controller capability;
therefore, the nominal values of the CVs are used to initialize the MPC controller. This action should not cause
any abrupt changes to the behaviour of the plant/process. Gradually, as confidence in the MPC controller
increases, the setpoints are changed so that the controller can make corrective actions to regulate the process to
its setpoints. When the confidence in the MPC controller is satisfactorily high enough, the final thorough
assessment of the controller is carried out by implementing setpoints of significantly greater magnitudes to
induce more rapid and abrupt control moves for analysis. For the scope of this thesis, we focused only on
testing the MPC controller using various simulated scenarios.
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CHAPTER 4 MPC CONTROLLER DESIGN AND SIMULATIONS
4.1 Specifying MPC Controller Parameters
Once the internal plant model is identified, the next step will be to use the identified plant model to complete
the design of model predictive controller. For this thesis, we shall design the controller by using the MPC
designer app in MATLAB. Generally, to design the MPC controller, there are several key parameters that we
need to carefully select and specify. If we do not select these parameters properly, it could lead to poor
performance of our MPC controller. The parameters are listed below:
Specify tuning parameter – Sample time, prediction horizon and control horizon
Specify constraints - Hard and Soft constraints on manipulated variables and output variables
Specify weights on manipulated variables and output variables
specify Models for measurement noise and for unmeasured input and output disturbances
4.1.1 Sample Time It is a general recommendation to use choose a sample time of between 0.1 and 0.25 of principle system
dynamic responses [23]. In this manner, the sample time is fast enough to respond to disturbances but not faster
than necessary to keep the optimization simpler. Qualitatively, as sample time decreases, rejection of unknown
disturbance usually improves. On the other hand, as sample time becomes small, the computational effort
increases dramatically. Thus, the optimal choice is a balance of performance and computational effort [23].
4.1.2 Prediction Horizon The prediction horizon, P, is the number of future control intervals the MPC controller must evaluate by
prediction when optimizing its MVs at control interval k [23]. Simply put, it refers to how far ahead the model
predicts the future. It is a recommended practice to always predict beyond the key dynamics of a process, that is
P > settling time.
4.1.3 Control Horizon The control horizon, M, is the number of control moves to be optimized at control interval k. it is generally
recommended that the control horizon falls between 1 and the prediction horizon P (M << P) [23]. During each
sampling instant, a sequence of M control moves is calculated but only the first move is implemented and all
others are discarded, then a new sequence is calculated at the next sampling instant, after new measurements
become available; again, only the first input move is implemented. This procedure is repeated at each sampling
instant. This key feature of MPC is referred to as receding horizon approach. [7].
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Figure 4.1: Basic concept of MPC – Prediction and Control horizons [7]
4.1.4 Constraints With MPC, we can specify certain constraints on the plant manipulated variable (MV) or the plant output
variable (OV) or MV increment. Constraints are usually classified as either hard or soft. Examples of hard
constraints are known physical limits on the plant MVs. For instance, the hydrocracker reactor temperature
setpoints must not exceed skin temperature of the reactor vessel. Hard constraints must be satisfied by the
quadratic programming (QP) solution. As a rule of thumb, whenever there is both hard MV bounds and hard
MV increment bounds on the same MV, one of them must be softened to prevent them from conflicting.
General recommendation is to soften all OV constraints. [23] [33].
4.1.5 Tuning Weights In addition to robust constraint handling of MPC, we can specify certain weights on the plant manipulated
variable (MV) or the plant output variable (OV) or MV increment. The weight refers to the relative importance
given to each of the parameters at each control interval when the model predictive controller solves the
optimization problem (QP). If more weight is assigned to the MV, the MPC will solve the optimization problem
and make MV adjustments that minimize the cost function while satisfying the constraints. In this mode, the
MPC would be tracking the MV. If maintaining the OV is assigned more weight, the MPC would solve the
optimization problem in such a way to track the OV while satisfying the constraints. We can also assign tuning
weight in a way to track the MV increment rate [29] [32].
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4.2 Controller Design Using MPC Designer
We will follow the guidelines in section 4.1 above to design MPC controller for the hydrocracker process using
the internal plant model identified chapter 3. We shall implement the design using the MPC designer app in
MATLAB.
4.2.1 Import Plant and Define MPC Structure We launch the MPC designer app by using the command “mpcDesigner” on MATLAB workspace. Once the
MPC designer app is opened, we import the HYDROCRACKER model and define the MPC structure. Since
HYDROCRACKER is a stable, continuous-time LTI system, MPC Designer sets the controller sample time to
0.1 Tr, where Tr is the average rise time of HYDROCRACKER.
By default, all plant inputs are defined as manipulated variables and all plant outputs as measured outputs. From
the MPC structure, we have 2 manipulated variables and 2 measured outputs. In the Assign plant i/o channels
section, assign the input and output channel indices such that [23]:
The first input, Bed 1 Inlet Temperature Setpoint (MV1), is a manipulated variable.
The second input, Bed 2 Inlet Temperature Setpoint (MV2), is a manipulated variable.
The first output, Reactor WABT (CV1), is a measured output.
The second output, Bed 1 ABT (CV2), is a measured output.
Figure 4.2: MPC designer – defining MPC structure from imported plant model.
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Once we finish defining the MPC structure and import the plant model, the app runs a default simulation
scenario and updates the Input Response and Output Response plots.
Figure 4.3: MPC designer – Input and Output Channel Specifications.
Figure 4.4: MPC designer – default simulation scenario using default MPC controller created using imported plant model.
The default scenario is configured to simulate a step change of 1 degrees each in both Bed 1 Inlet Temperature
and Bed 2 Inlet Temperature at time of 10 seconds. There are no output disturbances (added at MO channels)
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and there are no load disturbances (added at MV channels). See figure 4.4 for input-output response plots. The
simulation duration is 3000 seconds.
4.2.2 Case 1: Open-Loop Simulation – Verifying Interactions between MVs and CVs This section verifies the interactions between the manipulated variables and controlled variables via a series of
open-loop simulation tests. In figure 4.5, a step change of 5 degrees was applied to Bed 1 Inlet Temperature at a
time of 300 seconds while keeping Bed 2 Inlet Temperature constant. From figure 4.6, it is obvious that there
are very strong interactions between Bed 1 Inlet temperature, Reactor WABT and Bed 1 ABT.
In the same way, figure 4.7 shows a step change of -4 degrees on the Bed 2 Inlet Temperature at a time of 100
seconds while keeping Bed 1 Inlet Temperature constant. Again, from figure 4.8, it is obvious that there are
very strong interactions between Bed 2 Inlet temperature, Reactor WABT and Bed 1 ABT.
Figure 4.5: MPC designer – Interactions Between MV1 and CV1 and CV2 (MV2 is constant).
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Figure 4.6: MPC designer – Input and Output Response between MV1 and CV1 and CV2 (MV2 is constant).
Figure 4.7: MPC designer – Interactions Between MV2 and CV1 and CV2 (MV1 is constant).
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Figure 4.8: MPC designer – Input and Output Response between MV2 and CV1 and CV2 (MV1 is constant).
Figure 4.9 simulate combined step changes on both MV1 and MV2 at different times. A step change of 5
degrees in Bed 1 Inlet Temperature at a time of 50 seconds and a step change of -2 degrees (temperature
decrease) in Bed 2 Inlet Temperature at a time of 50 seconds. As in the case of the default scenario, there are no
output disturbances (added at MO channels) and there are no load disturbances (added at MV channels). The
simulation duration is 3000 seconds.
Figure 4.9: MPC designer – Combined step changes on MV1 and MV2.
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Figure 4.10: MPC designer – Input and Output Response Plots for Combined step changes on MV1 and MV2
From figure 4.10 above, it is obvious that there are strong interactions between the two manipulated variables
(Bed 1 Inlet Temperature and Bed 2 Inlet Temperature) and the two controlled variables (Reactor WABT and
Bed 1 ABT). When Bed 1 inlet temperature changed by 5 degrees at 50 seconds, both the reactor WABT and
Bed 1 ABT changed accordingly from a nominal value of 360 degrees to 365 degrees. Similarly, when Bed 2
inlet temperature was decreased by 2 degrees at 650 seconds, again, both the reactor WABT and Bed 1 ABT
changed accordingly from a nominal value of 365 degrees to 363 degrees.
4.2.3 Case 2: Closed-Loop Simulations Case Study 1: Step Change in Bed 1 Inlet Temperature Only: for this case study, a step change of 5 degrees
is applied to Bed 1 Inlet temperature at time 200 seconds while Bed 2 Inlet temperature is kept constant. This is
to simulate how the MPC controller responds to the step changes in manipulated variables. In the tuning tab of
MPC designer app, Sample time for the MPC controller is set to 1 second, the prediction horizon is 15 and the
control horizon is 3. The simulation duration is 500 seconds.
\
Figure 4.11: MPC designer – Tuning parameters configuration.
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Figure 4.12: MPC designer – Simulation Settings for Case Study 1
Figure 4.13: MPC designer – Input and Output Response Plots for Case Study 1.
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The input and output response plots in figure 4.13 shows how the MPC controller quickly rejects the
disturbance on CV2 resulting from the step change of 5 degrees in MV1. Hence, the MPC controller returns
CV2 (Bed 1 ABT) to its nominal value of 360 degrees in about 50 seconds. However, MPC controller adjust the
CV1 (Reactor WABT) to its new setpoint of 365 degrees because of step change of 5 degrees in MV1.
Case Study 2: Step Change in Bed 2 Inlet Temperature Only: for this case study, a step change of -3 degrees
(temperature decrease) is applied to Bed 2 Inlet temperature at time 50 seconds while Bed 1 Inlet temperature is
kept constant. This is to simulate how the MPC controller responds to the step changes in manipulated
variables. The Sample time for the MPC controller is set to 1 second, the prediction horizon is 15 and the
control horizon is 3. The simulation duration is 500 seconds.
Figure 4.14: MPC designer – Simulation Settings for Case Study 2
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Figure 4.15: MPC designer – Input and Output Response Plots for Case Study 2.
The input and output response plots in figure 4.15 shows how the MPC controller quickly rejects the
disturbance on CV1 resulting from the step change of -3 degrees in MV2. Hence, the MPC controller returns
CV1 (Reactor WABT) to its nominal value of 360 degrees in about 50 seconds. However, MPC controller
adjust the CV2 (Bed 1 ABT) to its new setpoint of 357 degrees because of step change of -3 degrees in MV2.
Case Study 3: Combined Step Changes in MV1 and MV2: This case study combines step changes in MV1
and MV2 using the same specifications as in case study 1 and 2 above. A step change of 5 degrees in Bed 1
Inlet Temperature at a time of 200 seconds and a step change of -3 degrees (temperature decrease) in Bed 2
Inlet Temperature at a time of 50 seconds. As in the case of the default scenario, there are no output
disturbances (added at MO channels) and there are no load disturbances (added at MV channels). The Sample
time for the MPC controller is set to 1 second, the prediction horizon is 15 and the control horizon is 3. The
simulation duration is 500 seconds.
As expected from the input and output response plots (figure 4.17), the MPC controller responds quickly to
setpoint changes while effectively handling loop interactions that exist between manipulated variables and
controlled variables.
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Figure 4.16: MPC designer – Simulation Settings for Case Study 3
Figure 4.17: MPC designer – Input and Output Response Plots for Case Study 3.
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Case Study 4: Specifying Input Constraints on MV1 and MV2: This case study uses the same specifications
as in case study 3 but includes input constraints on MV1 and MV2 respectively for both the range (upper and
lower bounds) and rate of change limits (minimum and maximum). When input constraints are specified, at
each control interval, the MPC solves the optimization problem in such a way that constraints on manipulated
variables are not violated. For this simulation, MV1 (Bed 1 Inlet temperature) is configured to change between
0 and 380 degrees and the rate of change limit is from -2 to +2. MV2 (Bed 2 Inlet temperature) is configured to
change between 0 and 440 degrees and the rate of change limit is from -2 to +2. No soft constraints are
configured for MV1 and MV2. Refer to figure. The simulation duration is 2000 seconds.
Figure 4.18: MPC designer – Input Constraint Specification for Case Study 4
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Figure 4.19: MPC designer – Input and Output Response Plots for Case Study 4
As can be seen from the input and output response plots (figure 4.19), once the Bed 2 Inlet temperature reaches
440 degrees, the MPC imposes the MV2 temperature constraint on the manipulated variable at 600 seconds.
The controller makes a compromise between the two competing control objectives: Reactor temperature control
and constraint satisfaction. Because no soft constraint is configured for MV1 and MV2, the MPC controller
trades off reactor temperature control for input constraint satisfaction. A softer input constraint enables the
controller to sacrifice the constraint requirement more to achieve improved temperature control.
Case Study 5: Specifying Controller Tuning Weights on Measured Outputs: As can be seen in case study 4,
because of the hard constraints on MV1 and MV2, the controller in a bit to satisfy the input constraint,
sacrificed the output variables (temperature control). When there are several measured outputs, it is possible to
specify higher weight on the most important output (primary control objectives), so that it is always satisfied at
the expense of small violations on the other measured outputs. For the hydrocracker reactor, the primary control
objective is maintaining the reactor WABT which is an indication of the overall hydrocracker reaction.
This case study uses the same specifications as in case study 4 but includes specifying manipulated variable
(MV) rate weight for both MV1 and MV2. In addition, output weight is assigned to the Reactor WABT (CV1)
which is the primary control objective. Refer to figure 4.20 for tuning weight specifications for the input
weights and output weights respectively. Input MV rate weights for both MV1 and MV2 is set to 0.2. increasing
the MV rate weights penalizes large MV changes in the controller optimization cost function. Output weight for
CV1 is set to 1 while CV2 is set to 0. The simulation duration is 1000 seconds.
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Master of Engineering (Industrial Automation)
Figure 4.20: MPC designer – Tuning Weights Specification for Case Study 5
Figure 4.21: MPC designer – Input and Output Response Plots for Case Study 5
As can be seen from the input and output response plots (figure 4.21), because of the tuning weight on CV1, the
MPC controller tracks the Reactor WABT at the expense of small violations on Bed 1 ABT. Hence, the primary
control objective is always satisfied at each control interval when MPC solves the quadratic optimization
problem. Generally, increasing CV weights, makes the control tighter whereas increasing MV weights results in
smoother moves. [9].
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CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions This research has been able to simulate how to successfully solve the complex multiloop interaction problem
inherent in the hydrocracker reaction process in the Escravos Gas-to-Liquid (EGTL) plant which is in Delta
State, Nigeria using advanced process control strategy - dynamic matrix control (DMC) or model predictive
control (MPC). From the simulation results in section 4.2.3, MPC controller proved to be more robust in
handling defined process constraints (input or output constraints), tracking setpoints and optimizing control cost
function. Hence, MPC can overcome the limitations of the current single loop PID control applied to the
hydrocracker process.
This work also detailed how to develop the dynamic model of any process given the historical data of the plant
from which input-output data correlations can be deduced. This is particularly important for two reasons. First,
it is always not convenient to perform step response tests on live plants to gather input-output data due to
operational reasons. Secondly, for most complex processes, it is difficult to obtain the dynamic model of most
processes from first principles or by modelling from mathematical formula. Hence the data-driven modelling
approach presented in this research work can be used to reconstruct the dynamic of any plant provided the
historical data of that plant is available.
5.2 Recommendations for Future Work Taking into consideration the aims and objectives of this research and the time constraint, this work reduced the
order of interactions in the system to a 2x2 MIMO to avoid too much complexity. To capture all the key
dynamics and multi-loop interactions in the hydrocracker process, this work can be expanded to a at least 5x6
MIMO or even higher order MIMO. In addition, other constraints like the bed temperature delta (bed ∆T) and
hydrogen quench valve limits can be considered.
This work focused on linear time invariant systems, however, most practical MIMO processes are nonlinear,
future study on how to incorporate the MPC controller for nonlinear systems should be considered. Also,
advanced system identification methods for nonlinear systems like using neural networks can be considered for
future work to capture the non-linearities of the hydrocracker process.
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REFERENCES [1] Willis, M. J. and Tham, M. T., “Advanced Process Control”, (2009).
http://ncl.ac.uk/UofNew(AdvancedControl).htm
[2] Seborg, D. E., Edgar, T. F., and Mellichamp, D. A. Process Dynamics and Control, 2nd ed. New Jersey:
John Wiley & Sons Inc, 2003, pp. 411 – 435.
[3] Jean-Plerre Gagnepaln’ and Dale E. Seborg, Analysis of Process Interactions with Applications to Multiloop
Control System Design ‘Ind. Eng. Chem. Process, Des. Dev. 1982, 21, 5-1 1.
[4] Hsiao-Ping H., Masahiro O., lori H., Dynamic interaction and multiloop control system design, J. Proc.
Cont. 1994, Volume 4, Number I.
[5] Cutler, C. R., and B. L. Ramaker, Dynamic Matrix Control-A Computer Control Algorithm, Proc. Joint
Auto. Control Conf, Paper WP5-B, San Francisco (1980).
[6] Quin, S. J., and T. A. Badgwell, A Survey of Model Predictive Control Technology, Control Eng. Practice,
11, 733 (2003).
[7] D.E. Seborg, T.F. Edgar and F.J. Doyle, Process Dynamics and Control, Third Edition, John Wiley, 2011.
[8] S.J. Kelly, M.D. Rogers and D.W. Hoffman, Quadratic Dynamic Matrix Control of Hydrocracking
Reactors, American Control Conference, Atlanta, GA, USA (1988).
[9] G. Dila, Model predictive controller design of hydrocracker reactors, Turk J Elec Eng & Comp Sci, Vol.19,
No.5, 2011.
[10] Arno de Klerk, Fischer-Tropsch Refining Chapter 12, WILEY-VCH Verlag GmbH & Co. KGaA, Aug 29,
2011.
[11] Gregory, W. H., and Paul R. R. Controlling Hydrocracker Temperature Excursions, NPRA Q&A and
Technology Forum, Plant Automation & Decision Support, October 9-12, 2011.
[12] Chevron Nigeria Limited, EGTL Unit 50 Plant Operations Manual.
[13] Douglas J. Cooper, Practical Process Control Using LOOP-PRO Software, Control Station, Inc. 2005.
[14] S.J. Kelly, M.D. Rogers and D.W. Hoffman, Quadratic Dynamic Matrix Control of Hydrocracking
Reactors, American Control Conference, Atlanta, GA, USA (1988).
[15] S.J. Kelly, M.D. Rogers and D.W. Hoffman, Quadratic Dynamic Matrix Control of Hydrocracking
Reactors, American Control Conference, Atlanta, GA, USA (1988).
[16] J. Drgona, Model Predictive Control with Applications in Building, Bratislava (2015).
[17] García, C. E., Prett, D. M. and Morari, M., “Model Predictive Control: Theory and Practice – A survey,”
Automatica, vol. 25, no. 3, pp. 335 – 348, 1989.
[18] Roberts, P.D., “A Brief Overview of Model Predictive Control: Model Predictive Control: Techniques and
Applications - Day 1,” Colloquium organized by Professional Group B1 (Control systems theory and
design) and B2 (Applied control techniques) IEE, Savoy Place, London, April 1999.
[19] Rossiter, J.A., “Reducing the Computational Burden in Predictive Control: Model Predictive Control:
Techniques and Applications - Day 1,” Colloquium organized by Professional Group B1 (Control systems
theory and design) and B2 (Applied control techniques) IEE, Savoy Place, London, April 1999.
[20] Wills, A. G., Technical Report EE04025 – Notes on Linear Model Predictive Control, pp. 1 – 15, 2004.
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[21] Maciejowski, J. M., Predictive Control with Constraints, Essex: Pearson Education Limited, 2002, pp. 248
– 275.
[22] L. Ljung, System Identification Toolbox – MATLAB & SIMULINK, MathWorks, Inc. R2015a (2015).
[23] A. Bemporad, M. Morari, and N. L. Ricker, Model Predictive Control Toolbox – MATLAB, MathWorks,
Inc. R2015a (2015).
[24] Nikolaou, M., “Model Predictive Controllers: A Critical Synthesis of Theory and Industrial Needs,”
Automatica, vol. 43, no. 5, pp. 885 – 891, May 2007.
[25] Roberts, P.D., “A Brief Overview of Model Predictive Control: Model Predictive Control: Techniques and
Applications - Day 1,” Colloquium organized by Professional Group B1 (Control systems theory and
design) and B2 (Applied control techniques) IEE, Savoy Place, London, April 1999.
[26] Rossiter, J.A., “Reducing the Computational Burden in Predictive Control: Model Predictive Control:
Techniques and Applications - Day 1,” Colloquium organized by Professional Group B1 (Control systems
theory and design) and B2 (Applied control techniques) IEE, Savoy Place, London, April 1999.
[27] Marjanovic, O., Industrial Control Systems, University of Manchester, pp. 32 – 50, 2010.
[28] Ljung, L., System Identification: Theory for the User, New Jersey: Prentice-Hall, 1987
[29] Wills, A. G., Technical Report EE04025 – Notes on Linear Model Predictive Control, pp. 1 – 15, 2004.
[30] Muske, K. R., and Rawlings, J. B., “Model Predictive Control with Linear Models,” American Institute for
Chemical Engineers, vol. 39, no. 2, pp. 262 – 287, 1993.
[31] Wang, L., Advances in Industrial Control: Model Predictive Control System Design and Implementation
Using MATLAB, London: Springer-Verlag, 2009, pp. 1 – 42.
[32] Zhou, K., and Doyle, J. C., Essentials of Robust Control, New Jersey: Prentice Hall Inc., 1998, pp. 129 –
131.
[33] Zafiriou, E., “Robust Model Predictive Control of Processes with Hard Constraints,” Computers in
Chemical Engineering, vol. 14, no. 4, pp. 359 – 371, 1990.
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APPENDIX A MATLAB Simulations
Open-Loop Simulations
%% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_2, 11); %% specify prediction horizon MPC1.PredictionHorizon = 10; %% specify control horizon MPC1.ControlHorizon = 2; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_1; options.MVSignal = MPC1_MVSignal_2; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'on'; %% run simulation sim(MPC1, 91, MPC1_RefSignal_2, MPC1_MDSignal_2, options); %% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_4, 11); %% specify prediction horizon MPC1.PredictionHorizon = 10; %% specify control horizon MPC1.ControlHorizon = 2; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_3; options.MVSignal = MPC1_MVSignal_4; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'on'; %% run simulation sim(MPC1, 91, MPC1_RefSignal_4, MPC1_MDSignal_4, options);
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%% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_5, 11); %% specify prediction horizon MPC1.PredictionHorizon = 10; %% specify control horizon MPC1.ControlHorizon = 2; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_4; options.MVSignal = MPC1_MVSignal_5; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'on'; %% run simulation sim(MPC1, 273, MPC1_RefSignal_5, MPC1_MDSignal_5, options); Closed-Loop Simulations: Case Study 1 %% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_6, 1); %% specify prediction horizon MPC1.PredictionHorizon = 15; %% specify control horizon MPC1.ControlHorizon = 3; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_5; options.MVSignal = MPC1_MVSignal_6; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'off'; %% run simulation sim(MPC1, 501, MPC1_RefSignal_6, MPC1_MDSignal_6, options);
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Closed-Loop Simulations: Case Study 2 %% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_7, 1); %% specify prediction horizon MPC1.PredictionHorizon = 15; %% specify control horizon MPC1.ControlHorizon = 3; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_6; options.MVSignal = MPC1_MVSignal_7; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'off'; %% run simulation sim(MPC1, 501, MPC1_RefSignal_7, MPC1_MDSignal_7, options); Closed-Loop Simulations: Case Study 3 %% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_8, 1); %% specify prediction horizon MPC1.PredictionHorizon = 15; %% specify control horizon MPC1.ControlHorizon = 3; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_7; options.MVSignal = MPC1_MVSignal_8; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'off'; %% run simulation sim(MPC1, 501, MPC1_RefSignal_8, MPC1_MDSignal_8, options);
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Closed-Loop Simulations: Case Study 4 %% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_9, 1); %% specify prediction horizon MPC1.PredictionHorizon = 15; %% specify control horizon MPC1.ControlHorizon = 3; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify constraints for MV and MV Rate MPC1.MV(1).Min = 0; MPC1.MV(1).Max = 380; MPC1.MV(1).RateMin = -2; MPC1.MV(1).RateMax = 2; MPC1.MV(2).Min = 0; MPC1.MV(2).Max = 420; MPC1.MV(2).RateMin = -2; MPC1.MV(2).RateMax = 2; %% specify weights MPC1.Weights.MV = [0 0]; MPC1.Weights.MVRate = [0.1 0.1]; MPC1.Weights.OV = [1 1]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_8; options.MVSignal = MPC1_MVSignal_9; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'off'; %% run simulation sim(MPC1, 2001, MPC1_RefSignal_9, MPC1_MDSignal_9, options); Closed-Loop Simulations: Case Study 5 %% create MPC controller object with sample time MPC1 = mpc(HYDROCRACKER_C_10, 1); %% specify prediction horizon MPC1.PredictionHorizon = 15; %% specify control horizon MPC1.ControlHorizon = 3; %% specify nominal values for inputs and outputs MPC1.Model.Nominal.U = [340;355]; MPC1.Model.Nominal.Y = [360;360]; %% specify constraints for MV and MV Rate MPC1.MV(1).Min = 0; MPC1.MV(1).Max = 380; MPC1.MV(1).RateMin = -2; MPC1.MV(1).RateMax = 2; MPC1.MV(2).Min = 0; MPC1.MV(2).Max = 420; MPC1.MV(2).RateMin = -2; MPC1.MV(2).RateMax = 2; %% specify weights MPC1.Weights.MV = [0 0];
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MPC1.Weights.MVRate = [0.2 0.2]; MPC1.Weights.OV = [1 0]; MPC1.Weights.ECR = 100000; %% specify simulation options options = mpcsimopt(); options.Model = HYDROCRACKER_S_9; options.MVSignal = MPC1_MVSignal_10; options.RefLookAhead = 'off'; options.MDLookAhead = 'off'; options.Constraints = 'on'; options.OpenLoop = 'off'; %% run simulation sim(MPC1, 1001, MPC1_RefSignal_10, MPC1_MDSignal_10, options);
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APPENDIX B Plant Historical Data Used for Model Estimation
Time MV1 CV1 CV2
Time MV2 CV1 CV2 01-Oct-17 21:12:30 329.00 332.89 330.49
09-Oct-17 01:11:30 348.00 349.05 344.36
01-Oct-17 21:13:00 329.00 332.89 330.50
09-Oct-17 01:12:00 348.00 349.06 344.40 01-Oct-17 21:13:30 329.00 332.88 330.50
09-Oct-17 01:12:30 348.00 349.08 344.46
01-Oct-17 21:14:00 329.00 332.88 330.48
09-Oct-17 01:13:00 348.00 349.09 344.49 01-Oct-17 21:14:30 329.00 332.88 330.51
09-Oct-17 01:13:30 348.00 349.09 344.49
01-Oct-17 21:15:00 329.00 332.88 330.52
09-Oct-17 01:14:00 348.00 349.09 344.50 01-Oct-17 21:15:30 329.00 332.88 330.52
09-Oct-17 01:14:30 348.00 349.10 344.50
01-Oct-17 21:16:00 329.00 332.88 330.52
09-Oct-17 01:15:00 348.00 349.10 344.52 01-Oct-17 21:16:30 329.00 332.88 330.55
09-Oct-17 01:15:30 348.00 349.10 344.56
01-Oct-17 21:17:00 329.00 332.89 330.56
09-Oct-17 01:16:00 348.00 349.12 344.56 01-Oct-17 21:17:30 329.00 332.89 330.59
09-Oct-17 01:16:30 348.00 349.12 344.53
01-Oct-17 21:18:00 329.00 332.88 330.57
09-Oct-17 01:17:00 348.00 349.14 344.56 01-Oct-17 21:18:30 329.00 332.88 330.57
09-Oct-17 01:17:30 348.00 349.15 344.57
01-Oct-17 21:19:00 329.00 332.87 330.54
09-Oct-17 01:18:00 348.00 349.16 344.59 01-Oct-17 21:19:30 329.00 332.88 330.57
09-Oct-17 01:18:30 348.00 349.17 344.61
01-Oct-17 21:20:00 329.00 332.88 330.58
09-Oct-17 01:19:00 348.00 349.19 344.59 01-Oct-17 21:20:30 329.00 332.87 330.57
09-Oct-17 01:19:30 348.00 349.20 344.60
01-Oct-17 21:21:00 329.00 332.88 330.57
09-Oct-17 01:20:00 348.00 349.22 344.60 01-Oct-17 21:21:30 329.00 332.88 330.57
09-Oct-17 01:20:30 348.00 349.24 344.60
01-Oct-17 21:22:00 329.00 332.87 330.57
09-Oct-17 01:21:00 348.00 349.26 344.68 01-Oct-17 21:22:30 329.00 332.88 330.60
09-Oct-17 01:21:30 348.00 349.26 344.66
01-Oct-17 21:23:00 329.00 332.88 330.59
09-Oct-17 01:22:00 348.00 349.27 344.66 01-Oct-17 21:23:30 329.00 332.88 330.60
09-Oct-17 01:22:30 348.00 349.28 344.62
01-Oct-17 21:24:00 329.00 332.88 330.60
09-Oct-17 01:23:00 348.00 349.30 344.66 01-Oct-17 21:24:30 329.00 332.88 330.58
09-Oct-17 01:23:30 348.00 349.31 344.64
01-Oct-17 21:25:00 329.00 332.88 330.59
09-Oct-17 01:24:00 348.00 349.32 344.64 01-Oct-17 21:25:30 329.00 332.88 330.59
09-Oct-17 01:24:30 348.00 349.32 344.64
01-Oct-17 21:26:00 329.00 332.89 330.61
09-Oct-17 01:25:00 348.00 349.35 344.65 01-Oct-17 21:26:30 329.00 332.88 330.59
09-Oct-17 01:25:30 348.00 349.36 344.65
01-Oct-17 21:27:00 329.00 332.88 330.59
09-Oct-17 01:26:00 348.00 349.37 344.66 01-Oct-17 21:27:30 329.00 332.89 330.60
09-Oct-17 01:26:30 348.00 349.38 344.63
01-Oct-17 21:28:00 329.00 332.88 330.57
09-Oct-17 01:27:00 348.00 349.38 344.59 01-Oct-17 21:28:30 329.00 332.87 330.56
09-Oct-17 01:27:30 348.00 349.38 344.57
01-Oct-17 21:29:00 329.00 332.87 330.56
09-Oct-17 01:28:00 348.00 349.39 344.57 01-Oct-17 21:29:30 329.00 332.88 330.56
09-Oct-17 01:28:30 348.00 349.40 344.58
01-Oct-17 21:30:00 329.00 332.86 330.52
09-Oct-17 01:29:00 348.00 349.40 344.56 01-Oct-17 21:30:30 329.00 332.87 330.52
09-Oct-17 01:29:30 348.00 349.40 344.54
01-Oct-17 21:31:00 329.00 332.87 330.52
09-Oct-17 01:30:00 348.00 349.40 344.52 01-Oct-17 21:31:30 329.00 332.87 330.53
09-Oct-17 01:30:30 348.00 349.41 344.52
01-Oct-17 21:32:00 329.00 332.87 330.54
09-Oct-17 01:31:00 348.00 349.41 344.54 01-Oct-17 21:32:30 329.00 332.87 330.53
09-Oct-17 01:31:30 348.00 349.42 344.52
01-Oct-17 21:33:00 329.00 332.88 330.54
09-Oct-17 01:32:00 348.00 349.42 344.52 01-Oct-17 21:33:30 329.00 332.87 330.53
09-Oct-17 01:32:30 348.00 349.42 344.50
01-Oct-17 21:34:00 329.00 332.88 330.54
09-Oct-17 01:33:00 348.00 349.44 344.56 01-Oct-17 21:34:30 329.00 332.88 330.56
09-Oct-17 01:33:30 348.00 349.45 344.57
01-Oct-17 21:35:00 329.00 332.88 330.53
09-Oct-17 01:34:00 348.00 349.45 344.60 01-Oct-17 21:35:30 329.00 332.88 330.55
09-Oct-17 01:34:30 348.00 349.45 344.57
01-Oct-17 21:36:00 329.00 332.90 330.60
09-Oct-17 01:35:00 348.00 349.45 344.56 01-Oct-17 21:36:30 329.00 332.89 330.57
09-Oct-17 01:35:30 348.00 349.45 344.53
01-Oct-17 21:37:00 329.00 332.89 330.60
09-Oct-17 01:36:00 348.00 349.45 344.52 01-Oct-17 21:37:30 329.00 332.90 330.59
09-Oct-17 01:36:30 348.00 349.45 344.51
01-Oct-17 21:38:00 329.00 332.90 330.59
09-Oct-17 01:37:00 348.00 349.46 344.55 01-Oct-17 21:38:30 329.00 332.91 330.64
09-Oct-17 01:37:30 348.00 349.46 344.54
60
Master of Engineering (Industrial Automation)
01-Oct-17 21:39:00 329.00 332.90 330.63
09-Oct-17 01:38:00 348.00 349.45 344.53 01-Oct-17 21:39:30 329.00 332.91 330.64
09-Oct-17 01:38:30 348.00 349.45 344.50
01-Oct-17 21:40:00 329.00 332.90 330.62
09-Oct-17 01:39:00 348.50 349.44 344.48 01-Oct-17 21:40:30 329.00 332.90 330.63
09-Oct-17 01:39:30 348.50 349.43 344.44
01-Oct-17 21:41:00 329.00 332.90 330.61
09-Oct-17 01:40:00 348.50 349.43 344.44 01-Oct-17 21:41:30 329.00 332.89 330.59
09-Oct-17 01:40:30 348.50 349.43 344.46
01-Oct-17 21:42:00 329.00 332.89 330.57
09-Oct-17 01:41:00 348.50 349.45 344.52 01-Oct-17 21:42:30 329.00 332.88 330.55
09-Oct-17 01:41:30 348.50 349.45 344.52
01-Oct-17 21:43:00 329.00 332.88 330.55
09-Oct-17 01:42:00 348.50 349.43 344.47 01-Oct-17 21:43:30 329.00 332.87 330.56
09-Oct-17 01:42:30 348.50 349.43 344.46
01-Oct-17 21:44:00 329.00 332.87 330.54
09-Oct-17 01:43:00 348.50 349.43 344.48 01-Oct-17 21:44:30 329.00 332.87 330.55
09-Oct-17 01:43:30 348.50 349.44 344.54
01-Oct-17 21:45:00 329.00 332.86 330.51
09-Oct-17 01:44:00 348.50 349.44 344.57 01-Oct-17 21:45:30 329.00 332.87 330.51
09-Oct-17 01:44:30 348.50 349.43 344.57
01-Oct-17 21:46:00 329.00 332.86 330.49
09-Oct-17 01:45:00 348.50 349.43 344.56 01-Oct-17 21:46:30 329.00 332.86 330.52
09-Oct-17 01:45:30 348.50 349.42 344.56
01-Oct-17 21:47:00 329.00 332.87 330.54
09-Oct-17 01:46:00 348.50 349.42 344.57 01-Oct-17 21:47:30 329.00 332.87 330.55
09-Oct-17 01:46:30 348.50 349.43 344.62
01-Oct-17 21:48:00 329.00 332.87 330.54
09-Oct-17 01:47:00 348.50 349.42 344.63 01-Oct-17 21:48:30 329.00 332.87 330.56
09-Oct-17 01:47:30 348.50 349.42 344.66
01-Oct-17 21:49:00 329.00 332.87 330.52
09-Oct-17 01:48:00 348.50 349.42 344.65 01-Oct-17 21:49:30 329.00 332.87 330.51
09-Oct-17 01:48:30 348.50 349.41 344.65
01-Oct-17 21:50:00 329.00 332.87 330.51
09-Oct-17 01:49:00 348.50 349.41 344.65 01-Oct-17 21:50:30 329.00 332.87 330.52
09-Oct-17 01:49:30 348.50 349.40 344.71
01-Oct-17 21:51:00 329.00 332.88 330.57
09-Oct-17 01:50:00 348.50 349.41 344.76 01-Oct-17 21:51:30 329.00 332.88 330.55
09-Oct-17 01:50:30 348.50 349.40 344.78
01-Oct-17 21:52:00 329.00 332.89 330.57
09-Oct-17 01:51:00 348.50 349.40 344.78 01-Oct-17 21:52:30 329.00 332.89 330.55
09-Oct-17 01:51:30 348.50 349.40 344.78
01-Oct-17 21:53:00 329.00 332.89 330.56
09-Oct-17 01:52:00 348.50 349.40 344.77 01-Oct-17 21:53:30 329.00 332.90 330.55
09-Oct-17 01:52:30 348.50 349.41 344.77
01-Oct-17 21:54:00 329.00 332.89 330.55
09-Oct-17 01:53:00 348.50 349.41 344.81 01-Oct-17 21:54:30 329.00 332.90 330.53
09-Oct-17 01:53:30 348.50 349.42 344.85
01-Oct-17 21:55:00 329.00 332.89 330.53
09-Oct-17 01:54:00 348.50 349.43 344.85 01-Oct-17 21:55:30 329.00 332.90 330.55
09-Oct-17 01:54:30 348.50 349.43 344.82
01-Oct-17 21:56:00 329.00 332.90 330.52
09-Oct-17 01:55:00 348.50 349.42 344.81 01-Oct-17 21:56:30 329.00 332.90 330.52
09-Oct-17 01:55:30 348.50 349.43 344.84
01-Oct-17 21:57:00 329.00 332.90 330.54
09-Oct-17 01:56:00 348.50 349.44 344.90 01-Oct-17 21:57:30 329.00 332.90 330.52
09-Oct-17 01:56:30 348.50 349.45 344.90
01-Oct-17 21:58:00 329.00 332.89 330.48
09-Oct-17 01:57:00 348.50 349.46 344.94 01-Oct-17 21:58:30 329.00 332.89 330.52
09-Oct-17 01:57:30 348.50 349.46 344.95
01-Oct-17 21:59:00 329.00 332.89 330.53
09-Oct-17 01:58:00 348.50 349.47 344.95 01-Oct-17 21:59:30 329.00 332.89 330.54
09-Oct-17 01:58:30 348.50 349.47 344.94
01-Oct-17 22:00:00 329.00 332.89 330.54
09-Oct-17 01:59:00 348.50 349.49 345.00 01-Oct-17 22:00:30 329.00 332.89 330.52
09-Oct-17 01:59:30 348.50 349.51 345.04
01-Oct-17 22:01:00 329.00 332.88 330.53
09-Oct-17 02:00:00 348.50 349.51 345.01 01-Oct-17 22:01:30 329.00 332.89 330.52
09-Oct-17 02:00:30 348.50 349.51 345.00
01-Oct-17 22:02:00 329.00 332.88 330.53
09-Oct-17 02:01:00 348.50 349.52 345.05 01-Oct-17 22:02:30 329.00 332.89 330.54
09-Oct-17 02:01:30 348.50 349.53 345.08
01-Oct-17 22:03:00 329.00 332.89 330.54
09-Oct-17 02:02:00 348.50 349.52 345.07 01-Oct-17 22:03:30 329.00 332.89 330.54
09-Oct-17 02:02:30 348.50 349.53 345.07
01-Oct-17 22:04:00 329.00 332.89 330.57
09-Oct-17 02:03:00 348.50 349.54 345.09 01-Oct-17 22:04:30 329.00 332.89 330.55
09-Oct-17 02:03:30 348.50 349.55 345.10
01-Oct-17 22:05:00 329.00 332.89 330.57
09-Oct-17 02:04:00 348.50 349.56 345.11 01-Oct-17 22:05:30 329.00 332.89 330.56
09-Oct-17 02:04:30 348.50 349.56 345.11
01-Oct-17 22:06:00 329.00 332.88 330.54
09-Oct-17 02:05:00 348.50 349.56 345.06 01-Oct-17 22:06:30 329.00 332.89 330.58
09-Oct-17 02:05:30 348.50 349.57 345.09
01-Oct-17 22:07:00 329.00 332.89 330.56
09-Oct-17 02:06:00 348.50 349.59 345.14
61
Master of Engineering (Industrial Automation)
01-Oct-17 22:07:30 329.00 332.89 330.53
09-Oct-17 02:06:30 348.50 349.60 345.17 01-Oct-17 22:08:00 329.00 332.89 330.54
09-Oct-17 02:07:00 348.50 349.61 345.18
01-Oct-17 22:08:30 329.00 332.90 330.56
09-Oct-17 02:07:30 348.50 349.62 345.18 01-Oct-17 22:09:00 329.00 332.90 330.55
09-Oct-17 02:08:00 348.50 349.63 345.20
01-Oct-17 22:09:30 329.42 332.89 330.56
09-Oct-17 02:08:30 348.50 349.64 345.22 01-Oct-17 22:10:00 329.50 332.90 330.57
09-Oct-17 02:09:00 348.50 349.65 345.23
01-Oct-17 22:10:30 329.50 332.90 330.61
09-Oct-17 02:09:30 348.50 349.65 345.20 01-Oct-17 22:11:00 329.50 332.91 330.60
09-Oct-17 02:10:00 348.50 349.66 345.20
01-Oct-17 22:11:30 329.50 332.91 330.61
09-Oct-17 02:10:30 348.50 349.69 345.23 01-Oct-17 22:12:00 329.50 332.91 330.59
09-Oct-17 02:11:00 348.50 349.70 345.25
01-Oct-17 22:12:30 329.50 332.91 330.63
09-Oct-17 02:11:30 348.50 349.71 345.26 01-Oct-17 22:13:00 329.50 332.92 330.64
09-Oct-17 02:12:00 348.50 349.72 345.25
01-Oct-17 22:13:30 329.50 332.93 330.66
09-Oct-17 02:12:30 348.50 349.73 345.23 01-Oct-17 22:14:00 329.50 332.92 330.65
09-Oct-17 02:13:00 348.50 349.74 345.24
01-Oct-17 22:14:30 329.50 332.94 330.71
09-Oct-17 02:13:30 348.50 349.75 345.24 01-Oct-17 22:15:00 329.50 332.94 330.73
09-Oct-17 02:14:00 348.50 349.76 345.25
01-Oct-17 22:15:30 329.50 332.94 330.73
09-Oct-17 02:14:30 348.50 349.77 345.25 01-Oct-17 22:16:00 329.50 332.95 330.78
09-Oct-17 02:15:00 348.50 349.79 345.26
01-Oct-17 22:16:30 329.50 332.95 330.78
09-Oct-17 02:15:30 348.50 349.79 345.24 01-Oct-17 22:17:00 329.50 332.95 330.77
09-Oct-17 02:16:00 348.50 349.81 345.26
01-Oct-17 22:17:30 329.50 332.95 330.78
09-Oct-17 02:16:30 348.50 349.82 345.25 01-Oct-17 22:18:00 329.50 332.95 330.79
09-Oct-17 02:17:00 348.50 349.83 345.26
01-Oct-17 22:18:30 329.50 332.96 330.82
09-Oct-17 02:17:30 348.50 349.85 345.27 01-Oct-17 22:19:00 329.50 332.95 330.78
09-Oct-17 02:18:00 348.50 349.85 345.24
01-Oct-17 22:19:30 329.50 332.96 330.79
09-Oct-17 02:18:30 348.50 349.86 345.25 01-Oct-17 22:20:00 329.50 332.94 330.77
09-Oct-17 02:19:00 348.50 349.87 345.25
01-Oct-17 22:20:30 329.50 332.96 330.82
09-Oct-17 02:19:30 348.50 349.88 345.25 01-Oct-17 22:21:00 329.50 332.97 330.85
09-Oct-17 02:20:00 348.50 349.89 345.27
01-Oct-17 22:21:30 329.50 332.97 330.87
09-Oct-17 02:20:30 348.66 349.90 345.26 01-Oct-17 22:22:00 329.50 332.97 330.84
09-Oct-17 02:21:00 349.00 349.91 345.26
01-Oct-17 22:22:30 329.50 332.97 330.85
09-Oct-17 02:21:30 349.00 349.92 345.25 01-Oct-17 22:23:00 329.50 332.98 330.86
09-Oct-17 02:22:00 349.00 349.93 345.24
01-Oct-17 22:23:30 329.50 332.98 330.88
09-Oct-17 02:22:30 349.00 349.94 345.22 01-Oct-17 22:24:00 329.50 332.98 330.86
09-Oct-17 02:23:00 349.00 349.94 345.21
01-Oct-17 22:24:30 329.50 332.98 330.88
09-Oct-17 02:23:30 349.00 349.96 345.21 01-Oct-17 22:25:00 329.50 332.99 330.88
09-Oct-17 02:24:00 349.00 349.97 345.23
01-Oct-17 22:25:30 329.50 332.99 330.90
09-Oct-17 02:24:30 349.00 349.98 345.25 01-Oct-17 22:26:00 329.50 333.00 330.91
09-Oct-17 02:25:00 349.00 350.00 345.25
01-Oct-17 22:26:30 329.50 333.00 330.91
09-Oct-17 02:25:30 349.00 350.01 345.27 01-Oct-17 22:27:00 329.50 333.01 330.94
09-Oct-17 02:26:00 349.00 350.01 345.27
01-Oct-17 22:27:30 329.50 333.01 330.94
09-Oct-17 02:26:30 349.00 350.01 345.27 01-Oct-17 22:28:00 329.50 333.02 330.95
09-Oct-17 02:27:00 349.00 350.02 345.23
01-Oct-17 22:28:30 329.50 333.03 331.00
09-Oct-17 02:27:30 349.00 350.02 345.20 01-Oct-17 22:29:00 329.50 333.03 330.99
09-Oct-17 02:28:00 349.00 350.02 345.18
01-Oct-17 22:29:30 329.50 333.04 330.99
09-Oct-17 02:28:30 349.00 350.02 345.17 01-Oct-17 22:30:00 329.50 333.05 331.01
09-Oct-17 02:29:00 349.00 350.03 345.19
01-Oct-17 22:30:30 329.50 333.06 331.04
09-Oct-17 02:29:30 349.00 350.03 345.16 01-Oct-17 22:31:00 329.50 333.06 331.02
09-Oct-17 02:30:00 349.00 350.03 345.14
01-Oct-17 22:31:30 329.50 333.06 330.99
09-Oct-17 02:30:30 349.00 350.03 345.12 01-Oct-17 22:32:00 329.50 333.05 330.96
09-Oct-17 02:31:00 349.00 350.03 345.10
01-Oct-17 22:32:30 329.50 333.06 330.97
09-Oct-17 02:31:30 349.00 350.04 345.10 01-Oct-17 22:33:00 329.50 333.07 330.99
09-Oct-17 02:32:00 349.00 350.05 345.10
01-Oct-17 22:33:30 329.50 333.07 330.98
09-Oct-17 02:32:30 349.00 350.04 345.10 01-Oct-17 22:34:00 329.50 333.08 330.99
09-Oct-17 02:33:00 349.00 350.05 345.10
01-Oct-17 22:34:30 329.50 333.09 331.03
09-Oct-17 02:33:30 349.00 350.06 345.12 01-Oct-17 22:35:00 329.50 333.10 331.05
09-Oct-17 02:34:00 349.00 350.06 345.13
01-Oct-17 22:35:30 329.50 333.11 331.07
09-Oct-17 02:34:30 349.00 350.07 345.12
62
Master of Engineering (Industrial Automation)
01-Oct-17 22:36:00 329.50 333.12 331.11
09-Oct-17 02:35:00 349.00 350.06 345.11 01-Oct-17 22:36:30 329.50 333.12 331.10
09-Oct-17 02:35:30 349.00 350.06 345.09
01-Oct-17 22:37:00 329.50 333.13 331.11
09-Oct-17 02:36:00 349.00 350.07 345.09 01-Oct-17 22:37:30 329.50 333.13 331.11
09-Oct-17 02:36:30 349.00 350.06 345.07
01-Oct-17 22:38:00 329.50 333.13 331.07
09-Oct-17 02:37:00 349.00 350.07 345.10 01-Oct-17 22:38:30 329.50 333.14 331.10
09-Oct-17 02:37:30 349.00 350.07 345.09
01-Oct-17 22:39:00 329.50 333.15 331.13
09-Oct-17 02:38:00 349.00 350.07 345.07 01-Oct-17 22:39:30 329.50 333.15 331.11
09-Oct-17 02:38:30 349.00 350.05 345.02
01-Oct-17 22:40:00 329.50 333.16 331.12
09-Oct-17 02:39:00 349.00 350.04 344.95 01-Oct-17 22:40:30 329.50 333.16 331.09
09-Oct-17 02:39:30 349.00 350.05 344.95
01-Oct-17 22:41:00 329.50 333.17 331.09
09-Oct-17 02:40:00 349.00 350.05 344.95 01-Oct-17 22:41:30 329.50 333.17 331.09
09-Oct-17 02:40:30 349.00 350.04 344.95
01-Oct-17 22:42:00 329.50 333.18 331.13
09-Oct-17 02:41:00 349.00 350.02 344.90 01-Oct-17 22:42:30 329.50 333.18 331.09
09-Oct-17 02:41:30 349.00 350.01 344.86
01-Oct-17 22:43:00 329.50 333.19 331.10
09-Oct-17 02:42:00 349.00 350.00 344.87 01-Oct-17 22:43:30 329.50 333.20 331.09
09-Oct-17 02:42:30 349.00 350.01 344.88
01-Oct-17 22:44:00 329.50 333.20 331.09
09-Oct-17 02:43:00 349.00 349.99 344.83 01-Oct-17 22:44:30 329.50 333.20 331.08
09-Oct-17 02:43:30 349.00 349.99 344.84
01-Oct-17 22:45:00 329.50 333.21 331.07
09-Oct-17 02:44:00 349.00 349.99 344.84 01-Oct-17 22:45:30 329.50 333.22 331.07
09-Oct-17 02:44:30 349.00 349.98 344.85
01-Oct-17 22:46:00 329.50 333.23 331.07
09-Oct-17 02:45:00 349.00 349.97 344.84 01-Oct-17 22:46:30 329.50 333.22 331.05
09-Oct-17 02:45:30 349.00 349.97 344.85
01-Oct-17 22:47:00 329.50 333.23 331.05
09-Oct-17 02:46:00 349.00 349.96 344.84 01-Oct-17 22:47:30 329.97 333.24 331.08
09-Oct-17 02:46:30 349.00 349.95 344.80
01-Oct-17 22:48:00 330.00 333.25 331.08
09-Oct-17 02:47:00 349.00 349.94 344.84 01-Oct-17 22:48:30 330.00 333.26 331.10
09-Oct-17 02:47:30 349.00 349.94 344.86
01-Oct-17 22:49:00 330.00 333.27 331.13
09-Oct-17 02:48:00 349.00 349.94 344.84 01-Oct-17 22:49:30 330.00 333.28 331.13
09-Oct-17 02:48:30 349.00 349.94 344.84
01-Oct-17 22:50:00 330.00 333.30 331.16
09-Oct-17 02:49:00 349.00 349.94 344.85 01-Oct-17 22:50:30 330.00 333.30 331.17
09-Oct-17 02:49:30 349.00 349.94 344.89
01-Oct-17 22:51:00 330.00 333.32 331.21
09-Oct-17 02:50:00 349.00 349.94 344.89 01-Oct-17 22:51:30 330.00 333.33 331.21
09-Oct-17 02:50:30 349.00 349.93 344.90
01-Oct-17 22:52:00 330.00 333.34 331.23
09-Oct-17 02:51:00 349.00 349.93 344.91 01-Oct-17 22:52:30 330.00 333.34 331.21
09-Oct-17 02:51:30 349.00 349.92 344.91
01-Oct-17 22:53:00 330.00 333.36 331.24
09-Oct-17 02:52:00 349.00 349.90 344.84 01-Oct-17 22:53:30 330.00 333.37 331.24
09-Oct-17 02:52:30 349.00 349.89 344.83
01-Oct-17 22:54:00 330.00 333.37 331.24
09-Oct-17 02:53:00 349.00 349.88 344.84 01-Oct-17 22:54:30 330.00 333.39 331.28
09-Oct-17 02:53:30 349.00 349.88 344.85
01-Oct-17 22:55:00 330.00 333.40 331.29
09-Oct-17 02:54:00 349.00 349.86 344.84 01-Oct-17 22:55:30 330.00 333.40 331.29
09-Oct-17 02:54:30 349.00 349.86 344.87
01-Oct-17 22:56:00 330.00 333.41 331.30
09-Oct-17 02:55:00 349.00 349.85 344.89 01-Oct-17 22:56:30 330.00 333.42 331.33
09-Oct-17 02:55:30 349.00 349.84 344.89
01-Oct-17 22:57:00 330.00 333.43 331.36
09-Oct-17 02:56:00 349.50 349.84 344.91 01-Oct-17 22:57:30 330.00 333.44 331.37
09-Oct-17 02:56:30 350.50 349.84 344.93
01-Oct-17 22:58:00 330.00 333.45 331.39
09-Oct-17 02:57:00 350.50 349.83 344.92 01-Oct-17 22:58:30 330.00 333.46 331.40
09-Oct-17 02:57:30 350.50 349.82 344.94
01-Oct-17 22:59:00 330.00 333.47 331.44
09-Oct-17 02:58:00 350.50 349.82 344.96 01-Oct-17 22:59:30 330.00 333.47 331.43
09-Oct-17 02:58:30 350.50 349.81 344.98
01-Oct-17 23:00:00 330.00 333.47 331.42
09-Oct-17 02:59:00 350.50 349.81 345.01 01-Oct-17 23:00:30 330.00 333.48 331.44
09-Oct-17 02:59:30 350.50 349.81 345.05
01-Oct-17 23:01:00 330.00 333.49 331.46
09-Oct-17 03:00:00 350.50 349.81 345.15 01-Oct-17 23:01:30 330.00 333.50 331.49
09-Oct-17 03:00:30 350.50 349.81 345.16
01-Oct-17 23:02:00 330.00 333.50 331.49
09-Oct-17 03:01:00 350.50 349.80 345.17 01-Oct-17 23:02:30 330.00 333.51 331.48
09-Oct-17 03:01:30 350.50 349.80 345.21
01-Oct-17 23:03:00 330.00 333.52 331.51
09-Oct-17 03:02:00 350.50 349.82 345.25 01-Oct-17 23:03:30 330.00 333.52 331.51
09-Oct-17 03:02:30 350.50 349.82 345.29
01-Oct-17 23:04:00 330.00 333.53 331.52
09-Oct-17 03:03:00 350.50 349.84 345.39
63
Master of Engineering (Industrial Automation)
01-Oct-17 23:04:30 330.00 333.54 331.56
09-Oct-17 03:03:30 350.50 349.84 345.42 01-Oct-17 23:05:00 330.00 333.55 331.58
09-Oct-17 03:04:00 350.50 349.85 345.46
01-Oct-17 23:05:30 330.00 333.56 331.57
09-Oct-17 03:04:30 350.50 349.85 345.51 01-Oct-17 23:06:00 330.00 333.55 331.55
09-Oct-17 03:05:00 350.50 349.86 345.57
01-Oct-17 23:06:30 330.00 333.56 331.55
09-Oct-17 03:05:30 350.50 349.86 345.56 01-Oct-17 23:07:00 330.00 333.57 331.54
09-Oct-17 03:06:00 350.50 349.86 345.63
01-Oct-17 23:07:30 330.00 333.59 331.58
09-Oct-17 03:06:30 350.50 349.85 345.63 01-Oct-17 23:08:00 330.00 333.59 331.57
09-Oct-17 03:07:00 350.50 349.85 345.63
01-Oct-17 23:08:30 330.00 333.59 331.55
09-Oct-17 03:07:30 350.50 349.85 345.69 01-Oct-17 23:09:00 330.00 333.60 331.55
09-Oct-17 03:08:00 350.50 349.85 345.68
01-Oct-17 23:09:30 330.00 333.62 331.59
09-Oct-17 03:08:30 350.50 349.84 345.65 01-Oct-17 23:10:00 330.00 333.63 331.61
09-Oct-17 03:09:00 350.50 349.83 345.62
01-Oct-17 23:10:30 330.00 333.64 331.64
09-Oct-17 03:09:30 350.50 349.83 345.65 01-Oct-17 23:11:00 330.00 333.64 331.61
09-Oct-17 03:10:00 350.50 349.85 345.68
01-Oct-17 23:11:30 330.00 333.65 331.62
09-Oct-17 03:10:30 350.50 349.85 345.72 01-Oct-17 23:12:00 330.00 333.66 331.64
09-Oct-17 03:11:00 350.50 349.86 345.75
01-Oct-17 23:12:30 329.80 333.67 331.66
09-Oct-17 03:11:30 350.50 349.86 345.75 01-Oct-17 23:13:00 329.80 333.69 331.68
09-Oct-17 03:12:00 350.50 349.87 345.78
01-Oct-17 23:13:30 329.80 333.69 331.70
09-Oct-17 03:12:30 350.50 349.90 345.87 01-Oct-17 23:14:00 329.80 333.69 331.67
09-Oct-17 03:13:00 350.50 349.93 345.97
01-Oct-17 23:14:30 329.80 333.71 331.70
09-Oct-17 03:13:30 350.50 349.94 346.01 01-Oct-17 23:15:00 329.80 333.71 331.70
09-Oct-17 03:14:00 350.50 349.95 346.06
01-Oct-17 23:15:30 329.80 333.72 331.70
09-Oct-17 03:14:30 350.50 349.96 346.06 01-Oct-17 23:16:00 329.80 333.72 331.68
09-Oct-17 03:15:00 350.50 350.00 346.14
01-Oct-17 23:16:30 329.80 333.73 331.68
09-Oct-17 03:15:30 350.50 350.02 346.19 01-Oct-17 23:17:00 329.80 333.72 331.65
09-Oct-17 03:16:00 350.50 350.06 346.27
01-Oct-17 23:17:30 329.80 333.73 331.67
09-Oct-17 03:16:30 350.50 350.09 346.35 01-Oct-17 23:18:00 329.80 333.72 331.61
09-Oct-17 03:17:00 350.50 350.11 346.39
01-Oct-17 23:18:30 329.80 333.73 331.61
09-Oct-17 03:17:30 350.50 350.13 346.41 01-Oct-17 23:19:00 329.80 333.73 331.61
09-Oct-17 03:18:00 350.50 350.13 346.38
01-Oct-17 23:19:30 329.80 333.73 331.61
09-Oct-17 03:18:30 350.50 350.16 346.44 01-Oct-17 23:20:00 329.80 333.75 331.62
09-Oct-17 03:19:00 350.50 350.20 346.49
01-Oct-17 23:20:30 329.80 333.75 331.61
09-Oct-17 03:19:30 350.50 350.20 346.48 01-Oct-17 23:21:00 329.68 333.75 331.61
09-Oct-17 03:20:00 350.50 350.22 346.47
01-Oct-17 23:21:30 329.70 333.76 331.60
09-Oct-17 03:20:30 350.50 350.23 346.49 01-Oct-17 23:22:00 329.70 333.76 331.59
09-Oct-17 03:21:00 350.50 350.26 346.54
01-Oct-17 23:22:30 329.70 333.77 331.59
09-Oct-17 03:21:30 350.50 350.28 346.57 01-Oct-17 23:23:00 329.70 333.77 331.59
09-Oct-17 03:22:00 350.50 350.30 346.59
01-Oct-17 23:23:30 329.70 333.78 331.58
09-Oct-17 03:22:30 350.50 350.33 346.61 01-Oct-17 23:24:00 329.70 333.79 331.58
09-Oct-17 03:23:00 350.50 350.34 346.63
01-Oct-17 23:24:30 329.70 333.78 331.54
09-Oct-17 03:23:30 350.50 350.37 346.65 01-Oct-17 23:25:00 329.70 333.79 331.54
09-Oct-17 03:24:00 350.50 350.39 346.65
01-Oct-17 23:25:30 329.80 333.80 331.54
09-Oct-17 03:24:30 350.50 350.41 346.64 01-Oct-17 23:26:00 329.80 333.80 331.53
09-Oct-17 03:25:00 350.50 350.43 346.63
01-Oct-17 23:26:30 329.80 333.81 331.52
09-Oct-17 03:25:30 350.50 350.47 346.68 01-Oct-17 23:27:00 329.80 333.81 331.50
09-Oct-17 03:26:00 350.50 350.51 346.69
01-Oct-17 23:27:30 330.00 333.82 331.50
09-Oct-17 03:26:30 350.50 350.54 346.69 01-Oct-17 23:28:00 330.00 333.84 331.52
09-Oct-17 03:27:00 350.50 350.58 346.75
01-Oct-17 23:28:30 330.00 333.85 331.51
09-Oct-17 03:27:30 350.50 350.61 346.79 01-Oct-17 23:29:00 330.00 333.85 331.50
09-Oct-17 03:28:00 350.50 350.64 346.76
01-Oct-17 23:29:30 330.00 333.85 331.48
09-Oct-17 03:28:30 350.50 350.68 346.77 01-Oct-17 23:30:00 330.00 333.86 331.46
09-Oct-17 03:29:00 350.50 350.72 346.79
01-Oct-17 23:30:30 330.00 333.87 331.47
09-Oct-17 03:29:30 350.50 350.75 346.81 01-Oct-17 23:31:00 330.00 333.88 331.48
09-Oct-17 03:30:00 350.50 350.79 346.82
01-Oct-17 23:31:30 330.00 333.88 331.45
09-Oct-17 03:30:30 350.50 350.82 346.83 01-Oct-17 23:32:00 330.00 333.88 331.43
09-Oct-17 03:31:00 350.50 350.86 346.85
01-Oct-17 23:32:30 330.00 333.89 331.44
09-Oct-17 03:31:30 350.50 350.89 346.86
64
Master of Engineering (Industrial Automation)
01-Oct-17 23:33:00 330.00 333.90 331.42
09-Oct-17 03:32:00 350.50 350.92 346.84 01-Oct-17 23:33:30 330.00 333.91 331.48
09-Oct-17 03:32:30 350.50 350.95 346.85
01-Oct-17 23:34:00 330.00 333.91 331.48
09-Oct-17 03:33:00 350.50 350.98 346.84 01-Oct-17 23:34:30 330.00 333.92 331.48
09-Oct-17 03:33:30 350.50 351.00 346.84
01-Oct-17 23:35:00 330.00 333.92 331.46
09-Oct-17 03:34:00 350.50 351.03 346.88 01-Oct-17 23:35:30 330.00 333.92 331.45
09-Oct-17 03:34:30 350.50 351.06 346.88
01-Oct-17 23:36:00 330.00 333.93 331.47
09-Oct-17 03:35:00 350.50 351.10 346.90 01-Oct-17 23:36:30 330.00 333.95 331.51
09-Oct-17 03:35:30 350.50 351.12 346.88
01-Oct-17 23:37:00 330.00 333.95 331.50
09-Oct-17 03:36:00 350.50 351.14 346.87 01-Oct-17 23:37:30 330.00 333.95 331.49
09-Oct-17 03:36:30 350.50 351.17 346.89
01-Oct-17 23:38:00 330.00 333.94 331.47
09-Oct-17 03:37:00 350.50 351.20 346.92 01-Oct-17 23:38:30 330.00 333.95 331.46
09-Oct-17 03:37:30 350.50 351.23 346.93
01-Oct-17 23:39:00 330.00 333.96 331.48
09-Oct-17 03:38:00 351.00 351.26 346.93 01-Oct-17 23:39:30 330.00 333.96 331.51
09-Oct-17 03:38:30 351.00 351.29 346.96
01-Oct-17 23:40:00 330.00 333.96 331.51
09-Oct-17 03:39:00 351.00 351.32 346.95 01-Oct-17 23:40:30 330.00 333.96 331.51
09-Oct-17 03:39:30 351.00 351.34 346.95
01-Oct-17 23:41:00 330.00 333.97 331.50
09-Oct-17 03:40:00 351.00 351.37 346.94 01-Oct-17 23:41:30 330.00 333.96 331.49
09-Oct-17 03:40:30 351.00 351.39 346.94
01-Oct-17 23:42:00 330.00 333.97 331.48
09-Oct-17 03:41:00 351.00 351.42 346.92 01-Oct-17 23:42:30 330.00 333.97 331.49
09-Oct-17 03:41:30 351.00 351.45 346.92
01-Oct-17 23:43:00 330.00 333.97 331.48
09-Oct-17 03:42:00 351.00 351.47 346.90 01-Oct-17 23:43:30 330.00 333.97 331.45
09-Oct-17 03:42:30 351.00 351.50 346.94
01-Oct-17 23:44:00 330.00 333.97 331.49
09-Oct-17 03:43:00 351.00 351.52 346.92 01-Oct-17 23:44:30 330.00 333.97 331.44
09-Oct-17 03:43:30 351.00 351.54 346.91
01-Oct-17 23:45:00 330.00 333.97 331.45
09-Oct-17 03:44:00 351.00 351.56 346.89 01-Oct-17 23:45:30 330.00 333.98 331.48
09-Oct-17 03:44:30 351.00 351.58 346.89
01-Oct-17 23:46:00 330.00 333.98 331.48
09-Oct-17 03:45:00 351.00 351.60 346.87 01-Oct-17 23:46:30 330.00 333.98 331.48
09-Oct-17 03:45:30 351.00 351.63 346.90
01-Oct-17 23:47:00 330.00 333.98 331.49
09-Oct-17 03:46:00 351.00 351.66 346.91 01-Oct-17 23:47:30 330.00 333.98 331.51
09-Oct-17 03:46:30 351.00 351.67 346.92
01-Oct-17 23:48:00 330.00 333.99 331.55
09-Oct-17 03:47:00 351.00 351.70 346.93 01-Oct-17 23:48:30 330.00 333.99 331.54
09-Oct-17 03:47:30 351.00 351.72 346.94
01-Oct-17 23:49:00 330.00 333.99 331.54
09-Oct-17 03:48:00 351.00 351.74 346.97 01-Oct-17 23:49:30 330.00 333.99 331.54
09-Oct-17 03:48:30 351.00 351.77 347.01
01-Oct-17 23:50:00 330.00 333.99 331.54
09-Oct-17 03:49:00 351.00 351.79 347.05 01-Oct-17 23:50:30 330.00 333.99 331.55
09-Oct-17 03:49:30 351.00 351.82 347.09
01-Oct-17 23:51:00 330.00 333.99 331.57
09-Oct-17 03:50:00 351.00 351.84 347.12 01-Oct-17 23:51:30 330.00 334.00 331.59
09-Oct-17 03:50:30 351.00 351.87 347.15
01-Oct-17 23:52:00 330.00 334.00 331.61
09-Oct-17 03:51:00 351.00 351.90 347.24 01-Oct-17 23:52:30 330.00 334.00 331.60
09-Oct-17 03:51:30 351.00 351.92 347.26
01-Oct-17 23:53:00 330.00 334.00 331.61
09-Oct-17 03:52:00 351.00 351.94 347.28 01-Oct-17 23:53:30 330.00 334.01 331.65
09-Oct-17 03:52:30 351.00 351.95 347.30
01-Oct-17 23:54:00 330.00 334.01 331.67
09-Oct-17 03:53:00 351.00 351.97 347.32 01-Oct-17 23:54:30 330.00 334.01 331.69
09-Oct-17 03:53:30 351.00 351.99 347.34
01-Oct-17 23:55:00 330.00 334.01 331.69
09-Oct-17 03:54:00 351.00 352.01 347.36 01-Oct-17 23:55:30 330.00 334.01 331.69
09-Oct-17 03:54:30 351.00 352.02 347.37
01-Oct-17 23:56:00 330.00 334.02 331.72
09-Oct-17 03:55:00 351.00 352.03 347.37 01-Oct-17 23:56:30 330.00 334.02 331.74
09-Oct-17 03:55:30 351.00 352.05 347.39
01-Oct-17 23:57:00 330.00 334.01 331.72
09-Oct-17 03:56:00 351.00 352.07 347.43 01-Oct-17 23:57:30 330.00 334.00 331.67
09-Oct-17 03:56:30 351.00 352.09 347.47
01-Oct-17 23:58:00 330.00 333.99 331.67
09-Oct-17 03:57:00 351.00 352.11 347.49 01-Oct-17 23:58:30 330.00 333.99 331.67
09-Oct-17 03:57:30 351.00 352.13 347.53
01-Oct-17 23:59:00 330.00 333.99 331.68
09-Oct-17 03:58:00 351.00 352.15 347.58 01-Oct-17 23:59:30 330.00 334.00 331.70
09-Oct-17 03:58:30 351.00 352.16 347.56
02-Oct-17 00:00:00 330.00 333.99 331.68
09-Oct-17 03:59:00 351.00 352.18 347.56 02-Oct-17 00:00:30 330.00 333.98 331.67
09-Oct-17 03:59:30 351.00 352.19 347.59
02-Oct-17 00:01:00 330.00 333.99 331.68
09-Oct-17 04:00:00 351.00 352.22 347.64
65
Master of Engineering (Industrial Automation)
02-Oct-17 00:01:30 330.00 333.98 331.68
09-Oct-17 04:00:30 351.00 352.24 347.67 02-Oct-17 00:02:00 330.00 333.98 331.69
09-Oct-17 04:01:00 351.00 352.26 347.72
02-Oct-17 00:02:30 330.00 333.99 331.70
09-Oct-17 04:01:30 351.00 352.29 347.77 02-Oct-17 00:03:00 330.00 333.98 331.70
09-Oct-17 04:02:00 351.00 352.32 347.81
02-Oct-17 00:03:30 330.00 333.98 331.67
09-Oct-17 04:02:30 351.00 352.33 347.82 02-Oct-17 00:04:00 330.00 333.98 331.69
09-Oct-17 04:03:00 351.00 352.35 347.83
02-Oct-17 00:04:30 330.00 333.99 331.70
09-Oct-17 04:03:30 351.00 352.36 347.88 02-Oct-17 00:05:00 330.00 333.99 331.69
09-Oct-17 04:04:00 351.00 352.40 347.92
02-Oct-17 00:05:30 330.00 333.99 331.68
09-Oct-17 04:04:30 351.00 352.43 347.98 02-Oct-17 00:06:00 330.00 334.00 331.71
09-Oct-17 04:05:00 351.00 352.45 347.98
02-Oct-17 00:06:30 330.00 334.00 331.72
09-Oct-17 04:05:30 351.00 352.45 347.93 02-Oct-17 00:07:00 330.00 334.00 331.70
09-Oct-17 04:06:00 351.00 352.45 347.89
02-Oct-17 00:07:30 330.00 334.00 331.69
09-Oct-17 04:06:30 351.00 352.47 347.88 02-Oct-17 00:08:00 330.00 334.01 331.73
09-Oct-17 04:07:00 351.00 352.48 347.88
02-Oct-17 00:08:30 330.00 334.01 331.71
09-Oct-17 04:07:30 351.00 352.50 347.88 02-Oct-17 00:09:00 330.00 334.01 331.71
09-Oct-17 04:08:00 351.00 352.51 347.84
02-Oct-17 00:09:30 330.00 334.02 331.73
09-Oct-17 04:08:30 351.00 352.51 347.76 02-Oct-17 00:10:00 330.00 334.03 331.74
09-Oct-17 04:09:00 351.00 352.51 347.70
02-Oct-17 00:10:30 330.00 334.04 331.74
09-Oct-17 04:09:30 351.00 352.51 347.64 02-Oct-17 00:11:00 330.00 334.04 331.72
09-Oct-17 04:10:00 351.00 352.53 347.62
02-Oct-17 00:11:30 330.00 334.04 331.74
09-Oct-17 04:10:30 351.00 352.54 347.61 02-Oct-17 00:12:00 330.00 334.04 331.73
09-Oct-17 04:11:00 351.00 352.55 347.61
02-Oct-17 00:12:30 330.00 334.03 331.69
09-Oct-17 04:11:30 351.00 352.57 347.52 02-Oct-17 00:13:00 330.00 334.04 331.67
09-Oct-17 04:12:00 351.00 352.60 347.59
02-Oct-17 00:13:30 330.00 334.03 331.67
09-Oct-17 04:12:30 351.00 352.62 347.63 02-Oct-17 00:14:00 330.00 334.03 331.66
09-Oct-17 04:13:00 351.00 352.64 347.62
02-Oct-17 00:14:30 330.00 334.04 331.67
09-Oct-17 04:13:30 351.00 352.65 347.65 02-Oct-17 00:15:00 330.00 334.04 331.67
09-Oct-17 04:14:00 351.00 352.68 347.66
02-Oct-17 00:15:30 330.00 334.04 331.66
09-Oct-17 04:14:30 351.00 352.71 347.72 02-Oct-17 00:16:00 330.00 334.04 331.66
09-Oct-17 04:15:00 351.00 352.74 347.75
02-Oct-17 00:16:30 330.00 334.04 331.65
09-Oct-17 04:15:30 351.00 352.75 347.75 02-Oct-17 00:17:00 330.00 334.04 331.65
09-Oct-17 04:16:00 351.00 352.77 347.75
02-Oct-17 00:17:30 330.00 334.05 331.65
09-Oct-17 04:16:30 351.00 352.80 347.80 02-Oct-17 00:18:00 330.00 334.05 331.67
09-Oct-17 04:17:00 351.00 352.82 347.81
02-Oct-17 00:18:30 330.00 334.05 331.66
09-Oct-17 04:17:30 351.00 352.84 347.81 02-Oct-17 00:19:00 330.00 334.06 331.68
09-Oct-17 04:18:00 351.00 352.84 347.76
02-Oct-17 00:19:30 330.00 334.06 331.65
09-Oct-17 04:18:30 351.00 352.85 347.74 02-Oct-17 00:20:00 330.00 334.06 331.66
09-Oct-17 04:19:00 351.00 352.87 347.75
02-Oct-17 00:20:30 330.00 334.06 331.65
09-Oct-17 04:19:30 351.00 352.89 347.77 02-Oct-17 00:21:00 330.00 334.06 331.64
09-Oct-17 04:20:00 351.00 352.90 347.80
02-Oct-17 00:21:30 330.00 334.07 331.64
09-Oct-17 04:20:30 351.00 352.89 347.78 02-Oct-17 00:22:00 330.00 334.07 331.65
09-Oct-17 04:21:00 351.00 352.87 347.76
02-Oct-17 00:22:30 330.00 334.07 331.62
09-Oct-17 04:21:30 351.00 352.87 347.77 02-Oct-17 00:23:00 330.00 334.08 331.64
09-Oct-17 04:22:00 351.00 352.87 347.77
02-Oct-17 00:23:30 330.00 334.08 331.64
09-Oct-17 04:22:30 351.00 352.87 347.75 02-Oct-17 00:24:00 330.00 334.09 331.63
09-Oct-17 04:23:00 351.00 352.87 347.74
02-Oct-17 00:24:30 330.00 334.08 331.62
09-Oct-17 04:23:30 351.00 352.87 347.73 02-Oct-17 00:25:00 330.00 334.09 331.62
09-Oct-17 04:24:00 351.00 352.88 347.74
02-Oct-17 00:25:30 330.00 334.10 331.64
09-Oct-17 04:24:30 351.00 352.89 347.75 02-Oct-17 00:26:00 330.00 334.10 331.61
09-Oct-17 04:25:00 351.00 352.91 347.77
02-Oct-17 00:26:30 330.00 334.10 331.63
09-Oct-17 04:25:30 351.00 352.93 347.77 02-Oct-17 00:27:00 330.24 334.11 331.63
09-Oct-17 04:26:00 351.00 352.95 347.86
02-Oct-17 00:27:30 330.30 334.11 331.60
09-Oct-17 04:26:30 351.00 352.96 347.86 02-Oct-17 00:28:00 330.30 334.11 331.59
09-Oct-17 04:27:00 351.00 352.97 347.86
02-Oct-17 00:28:30 330.30 334.12 331.63
09-Oct-17 04:27:30 351.00 352.99 347.94 02-Oct-17 00:29:00 330.30 334.13 331.63
09-Oct-17 04:28:00 351.00 353.00 347.95
02-Oct-17 00:29:30 330.30 334.13 331.65
09-Oct-17 04:28:30 351.00 353.01 347.97
66
Master of Engineering (Industrial Automation)
02-Oct-17 00:30:00 330.30 334.13 331.63
09-Oct-17 04:29:00 351.00 353.02 348.03 02-Oct-17 00:30:30 330.30 334.12 331.60
09-Oct-17 04:29:30 351.00 353.04 348.12
02-Oct-17 00:31:00 330.30 334.13 331.61
09-Oct-17 04:30:00 351.00 353.04 348.16 02-Oct-17 00:31:30 330.30 334.13 331.63
09-Oct-17 04:30:30 351.00 353.04 348.18
02-Oct-17 00:32:00 330.30 334.13 331.66
09-Oct-17 04:31:00 351.00 353.03 348.22 02-Oct-17 00:32:30 330.30 334.13 331.64
09-Oct-17 04:31:30 351.00 353.03 348.28
02-Oct-17 00:33:00 330.30 334.13 331.66
09-Oct-17 04:32:00 351.00 353.03 348.33 02-Oct-17 00:33:30 330.30 334.15 331.69
09-Oct-17 04:32:30 351.00 353.03 348.37
02-Oct-17 00:34:00 330.30 334.15 331.71
09-Oct-17 04:33:00 351.00 353.03 348.40 02-Oct-17 00:34:30 330.30 334.15 331.72
09-Oct-17 04:33:30 351.00 353.02 348.44
02-Oct-17 00:35:00 330.30 334.16 331.74
09-Oct-17 04:34:00 351.00 353.03 348.50 02-Oct-17 00:35:30 330.30 334.16 331.73
09-Oct-17 04:34:30 351.00 353.04 348.54
02-Oct-17 00:36:00 330.30 334.16 331.74
09-Oct-17 04:35:00 351.00 353.04 348.58 02-Oct-17 00:36:30 330.30 334.16 331.74
09-Oct-17 04:35:30 351.00 353.04 348.59
02-Oct-17 00:37:00 330.30 334.17 331.77
09-Oct-17 04:36:00 351.00 353.05 348.63 02-Oct-17 00:37:30 330.30 334.17 331.76
09-Oct-17 04:36:30 351.00 353.06 348.67
02-Oct-17 00:38:00 330.30 334.17 331.77
09-Oct-17 04:37:00 351.00 353.07 348.70 02-Oct-17 00:38:30 330.30 334.17 331.75
09-Oct-17 04:37:30 351.00 353.09 348.70
02-Oct-17 00:39:00 330.30 334.17 331.75
09-Oct-17 04:38:00 351.00 353.09 348.71 02-Oct-17 00:39:30 330.30 334.18 331.78
09-Oct-17 04:38:30 351.00 353.11 348.74
02-Oct-17 00:40:00 330.30 334.18 331.82
09-Oct-17 04:39:00 351.00 353.13 348.76 02-Oct-17 00:40:30 330.30 334.18 331.80
09-Oct-17 04:39:30 351.00 353.14 348.77
02-Oct-17 00:41:00 330.30 334.18 331.79
09-Oct-17 04:40:00 351.00 353.15 348.79 02-Oct-17 00:41:30 330.30 334.18 331.81
09-Oct-17 04:40:30 351.00 353.16 348.79
02-Oct-17 00:42:00 330.30 334.17 331.80
09-Oct-17 04:41:00 351.00 353.18 348.79 02-Oct-17 00:42:30 330.30 334.18 331.81
09-Oct-17 04:41:30 351.00 353.20 348.85
02-Oct-17 00:43:00 330.30 334.18 331.81
09-Oct-17 04:42:00 351.00 353.23 348.87 02-Oct-17 00:43:30 330.30 334.18 331.84
09-Oct-17 04:42:30 351.00 353.25 348.90
02-Oct-17 00:44:00 330.30 334.18 331.82
09-Oct-17 04:43:00 351.00 353.26 348.86 02-Oct-17 00:44:30 330.30 334.19 331.85
09-Oct-17 04:43:30 351.00 353.28 348.90
02-Oct-17 00:45:00 330.30 334.19 331.87
09-Oct-17 04:44:00 351.00 353.30 348.92 02-Oct-17 00:45:30 330.30 334.19 331.88
09-Oct-17 04:44:30 351.00 353.31 348.90
02-Oct-17 00:46:00 330.30 334.19 331.88
09-Oct-17 04:45:00 351.00 353.33 348.89 02-Oct-17 00:46:30 330.30 334.20 331.88
09-Oct-17 04:45:30 351.00 353.35 348.93
02-Oct-17 00:47:00 330.30 334.20 331.90
09-Oct-17 04:46:00 351.00 353.37 348.90 02-Oct-17 00:47:30 330.30 334.20 331.90
09-Oct-17 04:46:30 351.00 353.38 348.86
02-Oct-17 00:48:00 330.30 334.21 331.94
09-Oct-17 04:47:00 351.00 353.39 348.84 02-Oct-17 00:48:30 330.30 334.22 331.97
09-Oct-17 04:47:30 351.00 353.42 348.88
02-Oct-17 00:49:00 330.30 334.22 331.96
09-Oct-17 04:48:00 351.00 353.45 348.90 02-Oct-17 00:49:30 330.30 334.21 331.93
09-Oct-17 04:48:30 351.00 353.46 348.84
02-Oct-17 00:50:00 330.30 334.22 331.93
09-Oct-17 04:49:00 351.00 353.46 348.77 02-Oct-17 00:50:30 330.30 334.22 331.93
09-Oct-17 04:49:30 351.00 353.48 348.78
02-Oct-17 00:51:00 330.30 334.23 331.94
09-Oct-17 04:50:00 351.00 353.48 348.73 02-Oct-17 00:51:30 330.30 334.23 331.95
09-Oct-17 04:50:30 351.00 353.48 348.70
02-Oct-17 00:52:00 330.30 334.24 331.94
09-Oct-17 04:51:00 351.00 353.49 348.68 02-Oct-17 00:52:30 330.30 334.24 331.93
09-Oct-17 04:51:30 351.00 353.50 348.68
02-Oct-17 00:53:00 330.30 334.23 331.89
09-Oct-17 04:52:00 351.00 353.51 348.63 02-Oct-17 00:53:30 330.30 334.24 331.93
09-Oct-17 04:52:30 351.00 353.50 348.59
02-Oct-17 00:54:00 330.30 334.25 331.96
09-Oct-17 04:53:00 351.00 353.51 348.62 02-Oct-17 00:54:30 330.30 334.25 331.95
09-Oct-17 04:53:30 351.00 353.51 348.60
02-Oct-17 00:55:00 330.30 334.26 331.96
09-Oct-17 04:54:00 351.00 353.51 348.60 02-Oct-17 00:55:30 330.30 334.26 331.96
09-Oct-17 04:54:30 351.00 353.51 348.59
02-Oct-17 00:56:00 330.30 334.27 331.97
09-Oct-17 04:55:00 351.00 353.52 348.64 02-Oct-17 00:56:30 330.30 334.26 331.96
09-Oct-17 04:55:30 351.00 353.53 348.62
02-Oct-17 00:57:00 330.30 334.27 331.98
09-Oct-17 04:56:00 351.00 353.51 348.60 02-Oct-17 00:57:30 330.30 334.28 331.99
09-Oct-17 04:56:30 351.00 353.50 348.56
02-Oct-17 00:58:00 330.30 334.28 331.98
09-Oct-17 04:57:00 351.00 353.48 348.51
67
Master of Engineering (Industrial Automation)
02-Oct-17 00:58:30 330.30 334.27 331.97
09-Oct-17 04:57:30 351.00 353.48 348.53 02-Oct-17 00:59:00 330.30 334.28 331.97
09-Oct-17 04:58:00 351.00 353.46 348.51
02-Oct-17 00:59:30 330.30 334.29 332.01
09-Oct-17 04:58:30 351.00 353.44 348.47 02-Oct-17 01:00:00 330.30 334.29 331.99
09-Oct-17 04:59:00 351.00 353.43 348.44
02-Oct-17 01:00:30 330.30 334.29 332.00
09-Oct-17 04:59:30 351.00 353.40 348.42 02-Oct-17 01:01:00 330.30 334.29 332.00
09-Oct-17 05:00:00 351.00 353.39 348.41
02-Oct-17 01:01:30 330.30 334.30 332.01
09-Oct-17 05:00:30 351.00 353.38 348.42 02-Oct-17 01:02:00 330.30 334.30 332.01
09-Oct-17 05:01:00 351.00 353.36 348.41
02-Oct-17 01:02:30 330.30 334.30 331.97
09-Oct-17 05:01:30 351.00 353.34 348.40 02-Oct-17 01:03:00 330.30 334.30 331.97
09-Oct-17 05:02:00 351.00 353.31 348.37
02-Oct-17 01:03:30 330.30 334.31 331.96
09-Oct-17 05:02:30 351.00 353.30 348.41 02-Oct-17 01:04:00 330.30 334.31 331.96
09-Oct-17 05:03:00 351.00 353.29 348.43
02-Oct-17 01:04:30 330.30 334.31 331.94
09-Oct-17 05:03:30 351.00 353.27 348.44 02-Oct-17 01:05:00 330.30 334.32 331.93
09-Oct-17 05:04:00 351.00 353.25 348.45
02-Oct-17 01:05:30 330.30 334.31 331.93
09-Oct-17 05:04:30 351.00 353.23 348.48 02-Oct-17 01:06:00 330.30 334.32 331.95
09-Oct-17 05:05:00 351.00 353.22 348.51
02-Oct-17 01:06:30 330.30 334.33 331.94
09-Oct-17 05:05:30 351.00 353.20 348.52 02-Oct-17 01:07:00 330.30 334.33 331.91
09-Oct-17 05:06:00 351.00 353.18 348.54
02-Oct-17 01:07:30 330.30 334.33 331.88
09-Oct-17 05:06:30 351.00 353.16 348.57 02-Oct-17 01:08:00 330.30 334.33 331.91
09-Oct-17 05:07:00 351.00 353.13 348.50
02-Oct-17 01:08:30 330.30 334.33 331.92
09-Oct-17 05:07:30 351.00 353.12 348.51 02-Oct-17 01:09:00 330.30 334.35 331.93
09-Oct-17 05:08:00 351.00 353.11 348.53
02-Oct-17 01:09:30 330.30 334.36 331.96
09-Oct-17 05:08:30 351.00 353.09 348.54 02-Oct-17 01:10:00 330.30 334.36 331.94
09-Oct-17 05:09:00 351.00 353.06 348.54
02-Oct-17 01:10:30 330.30 334.38 331.97
09-Oct-17 05:09:30 351.00 353.02 348.54 02-Oct-17 01:11:00 330.30 334.38 331.96
09-Oct-17 05:10:00 351.00 353.01 348.40
02-Oct-17 01:11:30 330.30 334.39 331.98
09-Oct-17 05:10:30 351.00 353.01 348.40 02-Oct-17 01:12:00 330.30 334.38 331.97
09-Oct-17 05:11:00 351.00 352.98 348.40
02-Oct-17 01:12:30 330.30 334.39 331.99
09-Oct-17 05:11:30 351.00 352.96 348.40 02-Oct-17 01:13:00 330.30 334.40 331.98
09-Oct-17 05:12:00 351.00 352.94 348.39
02-Oct-17 01:13:30 330.30 334.40 331.97
09-Oct-17 05:12:30 351.00 352.92 348.38 02-Oct-17 01:14:00 330.30 334.40 331.96
09-Oct-17 05:13:00 351.00 352.91 348.40
02-Oct-17 01:14:30 330.30 334.41 331.96
09-Oct-17 05:13:30 351.00 352.90 348.36 02-Oct-17 01:15:00 330.30 334.42 331.99
09-Oct-17 05:14:00 351.00 352.89 348.38
02-Oct-17 01:15:30 330.30 334.43 332.00
09-Oct-17 05:14:30 351.00 352.87 348.34 02-Oct-17 01:16:00 330.30 334.42 331.99
09-Oct-17 05:15:00 351.00 352.85 348.32
02-Oct-17 01:16:30 330.30 334.43 331.98
09-Oct-17 05:15:30 351.00 352.85 348.34 02-Oct-17 01:17:00 330.30 334.43 331.99
09-Oct-17 05:16:00 351.00 352.85 348.35
02-Oct-17 01:17:30 330.30 334.43 332.01
09-Oct-17 05:16:30 351.00 352.84 348.34 02-Oct-17 01:18:00 330.30 334.43 332.01
09-Oct-17 05:17:00 351.00 352.83 348.33
02-Oct-17 01:18:30 330.30 334.43 332.00
09-Oct-17 05:17:30 351.00 352.83 348.34 02-Oct-17 01:19:00 330.30 334.43 331.98
09-Oct-17 05:18:00 351.00 352.83 348.34
02-Oct-17 01:19:30 330.30 334.44 332.00
09-Oct-17 05:18:30 351.00 352.82 348.29 02-Oct-17 01:20:00 330.30 334.43 331.98
09-Oct-17 05:19:00 351.00 352.83 348.34
02-Oct-17 01:20:30 330.30 334.44 331.99
09-Oct-17 05:19:30 351.00 352.82 348.28 02-Oct-17 01:21:00 330.30 334.44 332.00
09-Oct-17 05:20:00 351.00 352.81 348.25
02-Oct-17 01:21:30 330.30 334.43 331.97
09-Oct-17 05:20:30 351.00 352.80 348.19 02-Oct-17 01:22:00 330.30 334.43 331.96
09-Oct-17 05:21:00 351.00 352.81 348.22
02-Oct-17 01:22:30 330.30 334.44 331.98
09-Oct-17 05:21:30 351.00 352.81 348.24 02-Oct-17 01:23:00 330.30 334.45 332.01
09-Oct-17 05:22:00 351.00 352.79 348.24
02-Oct-17 01:23:30 330.30 334.45 332.00
09-Oct-17 05:22:30 351.00 352.78 348.20 02-Oct-17 01:24:00 330.30 334.45 331.99
09-Oct-17 05:23:00 351.00 352.80 348.16
02-Oct-17 01:24:30 330.30 334.45 332.03
09-Oct-17 05:23:30 351.00 352.80 348.14 02-Oct-17 01:25:00 330.30 334.46 332.02
09-Oct-17 05:24:00 351.00 352.80 348.14
02-Oct-17 01:25:30 330.30 334.46 332.02
09-Oct-17 05:24:30 351.00 352.79 348.10 02-Oct-17 01:26:00 330.30 334.46 332.03
09-Oct-17 05:25:00 351.00 352.79 348.08
02-Oct-17 01:26:30 330.30 334.47 332.06
09-Oct-17 05:25:30 351.00 352.79 348.07
68
Master of Engineering (Industrial Automation)
02-Oct-17 01:27:00 330.30 334.48 332.06
09-Oct-17 05:26:00 351.00 352.78 348.06 02-Oct-17 01:27:30 330.30 334.48 332.07
09-Oct-17 05:26:30 351.00 352.79 348.05
02-Oct-17 01:28:00 330.30 334.49 332.07
09-Oct-17 05:27:00 351.00 352.79 348.05 02-Oct-17 01:28:30 330.30 334.49 332.09
09-Oct-17 05:27:30 351.00 352.79 348.05
02-Oct-17 01:29:00 330.30 334.49 332.08
09-Oct-17 05:28:00 351.00 352.80 348.09 02-Oct-17 01:29:30 330.30 334.50 332.10
09-Oct-17 05:28:30 351.00 352.81 348.07
02-Oct-17 01:30:00 330.30 334.50 332.09
09-Oct-17 05:29:00 351.00 352.80 348.04 02-Oct-17 01:30:30 330.30 334.49 332.08
09-Oct-17 05:29:30 351.00 352.82 348.06
02-Oct-17 01:31:00 330.30 334.50 332.08
09-Oct-17 05:30:00 351.00 352.84 348.10 02-Oct-17 01:31:30 330.30 334.49 332.05
09-Oct-17 05:30:30 351.00 352.85 348.13
02-Oct-17 01:32:00 330.30 334.49 332.04
09-Oct-17 05:31:00 351.00 352.86 348.15 02-Oct-17 01:32:30 330.30 334.50 332.06
09-Oct-17 05:31:30 351.00 352.87 348.19
02-Oct-17 01:33:00 330.30 334.50 332.04
09-Oct-17 05:32:00 351.00 352.88 348.19 02-Oct-17 01:33:30 330.30 334.50 332.07
09-Oct-17 05:32:30 351.00 352.90 348.26
02-Oct-17 01:34:00 330.30 334.50 332.06
09-Oct-17 05:33:00 351.00 352.92 348.28 02-Oct-17 01:34:30 330.30 334.50 332.06
09-Oct-17 05:33:30 351.00 352.92 348.29
02-Oct-17 01:35:00 330.30 334.50 332.07
09-Oct-17 05:34:00 351.00 352.92 348.27 02-Oct-17 01:35:30 330.30 334.50 332.08
09-Oct-17 05:34:30 351.00 352.92 348.26
02-Oct-17 01:36:00 330.30 334.50 332.07
09-Oct-17 05:35:00 351.00 352.92 348.27 02-Oct-17 01:36:30 330.30 334.49 332.05
09-Oct-17 05:35:30 351.00 352.91 348.22
02-Oct-17 01:37:00 330.30 334.49 332.02
09-Oct-17 05:36:00 351.00 352.90 348.21 02-Oct-17 01:37:30 330.30 334.50 332.04
09-Oct-17 05:36:30 351.00 352.88 348.17
02-Oct-17 01:38:00 330.30 334.50 332.07
09-Oct-17 05:37:00 351.00 352.89 348.19 02-Oct-17 01:38:30 330.30 334.50 332.07
09-Oct-17 05:37:30 351.00 352.88 348.16
02-Oct-17 01:39:00 330.30 334.49 332.05
09-Oct-17 05:38:00 351.00 352.88 348.16 02-Oct-17 01:39:30 330.30 334.50 332.07
09-Oct-17 05:38:30 351.00 352.88 348.22
02-Oct-17 01:40:00 330.30 334.49 332.04
09-Oct-17 05:39:00 351.00 352.87 348.20 02-Oct-17 01:40:30 330.30 334.50 332.06
09-Oct-17 05:39:30 351.00 352.85 348.21
02-Oct-17 01:41:00 330.30 334.50 332.04
09-Oct-17 05:40:00 351.00 352.88 348.25 02-Oct-17 01:41:30 330.30 334.50 332.04
09-Oct-17 05:40:30 351.00 352.89 348.23
02-Oct-17 01:42:00 330.30 334.51 332.06
09-Oct-17 05:41:00 351.00 352.91 348.28 02-Oct-17 01:42:30 330.30 334.50 332.02
09-Oct-17 05:41:30 351.00 352.93 348.31
02-Oct-17 01:43:00 330.30 334.50 332.01
09-Oct-17 05:42:00 351.00 352.93 348.30 02-Oct-17 01:43:30 330.30 334.51 332.02
09-Oct-17 05:42:30 351.00 352.94 348.35
02-Oct-17 01:44:00 330.30 334.51 332.01
09-Oct-17 05:43:00 351.00 352.95 348.33 02-Oct-17 01:44:30 330.30 334.50 331.99
09-Oct-17 05:43:30 351.00 352.97 348.40
02-Oct-17 01:45:00 330.30 334.51 332.00
09-Oct-17 05:44:00 351.00 352.99 348.47 02-Oct-17 01:45:30 330.30 334.51 332.00
09-Oct-17 05:44:30 351.00 353.00 348.51
02-Oct-17 01:46:00 330.30 334.51 331.99
09-Oct-17 05:45:00 351.00 353.00 348.49 02-Oct-17 01:46:30 330.30 334.51 331.99
09-Oct-17 05:45:30 351.00 353.01 348.51
02-Oct-17 01:47:00 330.30 334.51 331.98
09-Oct-17 05:46:00 351.00 353.02 348.53 02-Oct-17 01:47:30 330.30 334.51 331.97
09-Oct-17 05:46:30 351.00 353.02 348.50
02-Oct-17 01:48:00 330.30 334.51 331.99
09-Oct-17 05:47:00 351.00 353.02 348.48 02-Oct-17 01:48:30 330.30 334.52 332.01
09-Oct-17 05:47:30 351.00 353.01 348.43
02-Oct-17 01:49:00 330.30 334.52 332.00
09-Oct-17 05:48:00 351.00 353.01 348.39 02-Oct-17 01:49:30 330.30 334.52 331.98
09-Oct-17 05:48:30 351.00 353.01 348.41
02-Oct-17 01:50:00 330.30 334.52 332.00
09-Oct-17 05:49:00 351.00 353.01 348.41 02-Oct-17 01:50:30 330.30 334.52 332.00
09-Oct-17 05:49:30 351.00 353.00 348.41
02-Oct-17 01:51:00 330.30 334.52 331.99
09-Oct-17 05:50:00 351.00 353.00 348.41 02-Oct-17 01:51:30 330.30 334.52 331.96
09-Oct-17 05:50:30 351.00 352.99 348.32
02-Oct-17 01:52:00 330.30 334.51 331.97
09-Oct-17 05:51:00 351.00 352.99 348.27 02-Oct-17 01:52:30 330.30 334.52 331.97
09-Oct-17 05:51:30 351.00 352.99 348.29
02-Oct-17 01:53:00 330.30 334.52 331.97
09-Oct-17 05:52:00 351.00 353.00 348.30 02-Oct-17 01:53:30 330.30 334.51 331.95
09-Oct-17 05:52:30 351.00 353.01 348.26
02-Oct-17 01:54:00 330.30 334.52 331.95
09-Oct-17 05:53:00 351.00 353.01 348.26 02-Oct-17 01:54:30 330.30 334.51 331.94
09-Oct-17 05:53:30 351.00 353.02 348.26
02-Oct-17 01:55:00 330.30 334.51 331.94
09-Oct-17 05:54:00 351.00 353.04 348.27
69
Master of Engineering (Industrial Automation)
02-Oct-17 01:55:30 330.30 334.52 331.98
09-Oct-17 05:54:30 351.00 353.05 348.24 02-Oct-17 01:56:00 330.30 334.52 331.97
09-Oct-17 05:55:00 351.00 353.04 348.22
02-Oct-17 01:56:30 330.30 334.52 331.95
09-Oct-17 05:55:30 351.00 353.05 348.22 02-Oct-17 01:57:00 330.30 334.52 331.96
09-Oct-17 05:56:00 351.00 353.05 348.18
02-Oct-17 01:57:30 330.30 334.52 331.96
09-Oct-17 05:56:30 351.00 353.07 348.21 02-Oct-17 01:58:00 330.30 334.51 331.94
09-Oct-17 05:57:00 351.00 353.07 348.21
02-Oct-17 01:58:30 330.30 334.52 331.94
09-Oct-17 05:57:30 351.00 353.07 348.17 02-Oct-17 01:59:00 330.30 334.52 331.95
09-Oct-17 05:58:00 351.00 353.06 348.12
02-Oct-17 01:59:30 330.30 334.52 331.96
09-Oct-17 05:58:30 351.00 353.06 348.08 02-Oct-17 02:00:00 330.30 334.52 331.96
09-Oct-17 05:59:00 351.00 353.06 348.07
02-Oct-17 02:00:30 330.30 334.52 331.96
09-Oct-17 05:59:30 351.00 353.06 348.07 02-Oct-17 02:01:00 330.30 334.52 331.96
09-Oct-17 06:00:00 351.00 353.05 348.07
02-Oct-17 02:01:30 330.30 334.52 331.97
09-Oct-17 06:00:30 351.00 353.04 348.04 02-Oct-17 02:02:00 330.30 334.52 331.98
09-Oct-17 06:01:00 351.00 353.02 348.01
02-Oct-17 02:02:30 330.30 334.52 331.96
09-Oct-17 06:01:30 351.00 353.02 348.04 02-Oct-17 02:03:00 330.30 334.52 331.97
09-Oct-17 06:02:00 351.00 353.00 347.96
02-Oct-17 02:03:30 330.30 334.51 331.98
09-Oct-17 06:02:30 351.00 352.97 347.87 02-Oct-17 02:04:00 330.30 334.52 331.98
09-Oct-17 06:03:00 351.00 352.95 347.85
02-Oct-17 02:04:30 330.30 334.52 331.96
09-Oct-17 06:03:30 351.00 352.93 347.82 02-Oct-17 02:05:00 330.30 334.50 331.95
09-Oct-17 06:04:00 351.00 352.92 347.81
02-Oct-17 02:05:30 330.30 334.51 331.94
09-Oct-17 06:04:30 351.00 352.89 347.74 02-Oct-17 02:06:00 330.30 334.51 331.94
09-Oct-17 06:05:00 351.00 352.87 347.70
02-Oct-17 02:06:30 330.30 334.51 331.96
09-Oct-17 06:05:30 351.00 352.85 347.66 02-Oct-17 02:07:00 330.30 334.51 331.97
09-Oct-17 06:06:00 351.00 352.83 347.60
02-Oct-17 02:07:30 330.30 334.50 331.96
09-Oct-17 06:06:30 351.00 352.81 347.55 02-Oct-17 02:08:00 330.30 334.50 331.94
09-Oct-17 06:07:00 351.00 352.80 347.53
02-Oct-17 02:08:30 330.30 334.49 331.93
09-Oct-17 06:07:30 351.00 352.78 347.53 02-Oct-17 02:09:00 330.30 334.50 331.94
09-Oct-17 06:08:00 351.00 352.76 347.38
02-Oct-17 02:09:30 330.30 334.50 331.94
09-Oct-17 06:08:30 351.00 352.73 347.29 02-Oct-17 02:10:00 330.30 334.50 331.97
09-Oct-17 06:09:00 351.00 352.73 347.30
02-Oct-17 02:10:30 330.30 334.50 331.96
09-Oct-17 06:09:30 351.00 352.75 347.40 02-Oct-17 02:11:00 330.30 334.50 331.98
09-Oct-17 06:10:00 351.00 352.76 347.47
02-Oct-17 02:11:30 330.30 334.49 331.95
09-Oct-17 06:10:30 351.00 352.77 347.51 02-Oct-17 02:12:00 330.30 334.50 332.00
09-Oct-17 06:11:00 351.00 352.76 347.55
02-Oct-17 02:12:30 330.30 334.50 331.98
09-Oct-17 06:11:30 351.00 352.76 347.61 02-Oct-17 02:13:00 330.30 334.50 332.01
09-Oct-17 06:12:00 351.00 352.76 347.60
02-Oct-17 02:13:30 330.30 334.51 332.00
09-Oct-17 06:12:30 351.00 352.75 347.63 02-Oct-17 02:14:00 330.30 334.50 332.00
09-Oct-17 06:13:00 351.00 352.73 347.63
02-Oct-17 02:14:30 330.30 334.50 331.97
09-Oct-17 06:13:30 351.00 352.73 347.64 02-Oct-17 02:15:00 330.30 334.49 331.99
09-Oct-17 06:14:00 351.00 352.71 347.65
02-Oct-17 02:15:30 330.30 334.49 331.98
09-Oct-17 06:14:30 351.00 352.70 347.63 02-Oct-17 02:16:00 330.30 334.49 331.97
09-Oct-17 06:15:00 351.00 352.69 347.62
02-Oct-17 02:16:30 330.30 334.49 331.98
09-Oct-17 06:15:30 351.00 352.67 347.58 02-Oct-17 02:17:00 330.30 334.49 331.96
09-Oct-17 06:16:00 351.00 352.66 347.58
02-Oct-17 02:17:30 330.30 334.49 331.98
09-Oct-17 06:16:30 351.00 352.65 347.52 02-Oct-17 02:18:00 330.30 334.49 331.98
09-Oct-17 06:17:00 351.00 352.61 347.41
02-Oct-17 02:18:30 330.30 334.48 331.97
09-Oct-17 06:17:30 351.00 352.58 347.32 02-Oct-17 02:19:00 330.30 334.48 331.97
09-Oct-17 06:18:00 351.00 352.55 347.22
02-Oct-17 02:19:30 330.30 334.48 331.97
09-Oct-17 06:18:30 351.00 352.52 347.10 02-Oct-17 02:20:00 330.30 334.48 331.98
09-Oct-17 06:19:00 351.00 352.48 347.00
02-Oct-17 02:20:30 330.47 334.48 331.98
09-Oct-17 06:19:30 351.00 352.45 346.90 02-Oct-17 02:21:00 330.50 334.47 331.98
09-Oct-17 06:20:00 351.00 352.42 346.82
02-Oct-17 02:21:30 330.50 334.47 331.96
09-Oct-17 06:20:30 351.00 352.40 346.74 02-Oct-17 02:22:00 330.50 334.47 331.96
09-Oct-17 06:21:00 351.00 352.38 346.68
02-Oct-17 02:22:30 330.50 334.47 331.95
09-Oct-17 06:21:30 351.00 352.35 346.60 02-Oct-17 02:23:00 330.50 334.46 331.91
09-Oct-17 06:22:00 351.00 352.35 346.59
02-Oct-17 02:23:30 330.50 334.46 331.93
09-Oct-17 06:22:30 351.00 352.34 346.59
70
Master of Engineering (Industrial Automation)
02-Oct-17 02:24:00 330.50 334.46 331.93
09-Oct-17 06:23:00 351.00 352.34 346.62 02-Oct-17 02:24:30 330.50 334.46 331.94
09-Oct-17 06:23:30 351.00 352.34 346.60
02-Oct-17 02:25:00 330.50 334.46 331.95
09-Oct-17 06:24:00 351.00 352.33 346.56 02-Oct-17 02:25:30 330.50 334.46 331.95
09-Oct-17 06:24:30 351.00 352.31 346.50
02-Oct-17 02:26:00 330.50 334.46 331.97
09-Oct-17 06:25:00 351.00 352.31 346.49 02-Oct-17 02:26:30 330.50 334.47 331.98
09-Oct-17 06:25:30 351.00 352.32 346.51
02-Oct-17 02:27:00 330.50 334.47 331.97
09-Oct-17 06:26:00 352.00 352.32 346.53 02-Oct-17 02:27:30 330.50 334.47 332.01
09-Oct-17 06:26:30 352.00 352.32 346.52
02-Oct-17 02:28:00 330.50 334.48 332.04
09-Oct-17 06:27:00 352.00 352.34 346.50 02-Oct-17 02:28:30 330.50 334.48 332.05
09-Oct-17 06:27:30 352.00 352.36 346.51
02-Oct-17 02:29:00 330.50 334.48 332.06
09-Oct-17 06:28:00 352.00 352.38 346.52 02-Oct-17 02:29:30 330.50 334.47 332.03
09-Oct-17 06:28:30 352.00 352.39 346.54
02-Oct-17 02:30:00 330.50 334.47 332.01
09-Oct-17 06:29:00 352.00 352.39 346.54 02-Oct-17 02:30:30 330.50 334.47 331.99
09-Oct-17 06:29:30 352.00 352.36 346.51
02-Oct-17 02:31:00 330.50 334.47 332.00
09-Oct-17 06:30:00 352.00 352.33 346.43 02-Oct-17 02:31:30 330.50 334.48 332.03
09-Oct-17 06:30:30 352.00 352.31 346.38
02-Oct-17 02:32:00 330.50 334.47 332.03
09-Oct-17 06:31:00 352.00 352.30 346.39 02-Oct-17 02:32:30 330.50 334.48 332.03
09-Oct-17 06:31:30 352.00 352.28 346.39
02-Oct-17 02:33:00 330.50 334.48 332.02
09-Oct-17 06:32:00 352.00 352.26 346.40 02-Oct-17 02:33:30 330.50 334.48 332.04
09-Oct-17 06:32:30 352.00 352.24 346.36
02-Oct-17 02:34:00 330.50 334.48 332.04
09-Oct-17 06:33:00 352.00 352.21 346.33 02-Oct-17 02:34:30 330.50 334.49 332.06
09-Oct-17 06:33:30 352.00 352.19 346.29
02-Oct-17 02:35:00 330.50 334.49 332.07
09-Oct-17 06:34:00 352.00 352.17 346.26 02-Oct-17 02:35:30 330.50 334.49 332.07
09-Oct-17 06:34:30 352.00 352.18 346.32
02-Oct-17 02:36:00 330.50 334.49 332.08
09-Oct-17 06:35:00 352.00 352.18 346.31 02-Oct-17 02:36:30 330.50 334.48 332.06
09-Oct-17 06:35:30 352.00 352.17 346.30
02-Oct-17 02:37:00 330.50 334.49 332.10
09-Oct-17 06:36:00 352.00 352.17 346.31 02-Oct-17 02:37:30 330.50 334.49 332.08
09-Oct-17 06:36:30 352.00 352.18 346.30
02-Oct-17 02:38:00 330.50 334.49 332.06
09-Oct-17 06:37:00 352.00 352.18 346.30 02-Oct-17 02:38:30 330.50 334.48 332.02
09-Oct-17 06:37:30 352.00 352.19 346.25
02-Oct-17 02:39:00 330.50 334.48 332.03
09-Oct-17 06:38:00 352.00 352.19 346.22 02-Oct-17 02:39:30 330.50 334.48 332.07
09-Oct-17 06:38:30 352.00 352.22 346.26
02-Oct-17 02:40:00 330.50 334.49 332.07
09-Oct-17 06:39:00 352.00 352.23 346.30 02-Oct-17 02:40:30 330.50 334.49 332.05
09-Oct-17 06:39:30 352.00 352.25 346.34
02-Oct-17 02:41:00 330.50 334.49 332.07
09-Oct-17 06:40:00 352.00 352.27 346.39 02-Oct-17 02:41:30 330.50 334.50 332.13
09-Oct-17 06:40:30 352.00 352.28 346.42
02-Oct-17 02:42:00 330.50 334.50 332.12
09-Oct-17 06:41:00 352.00 352.28 346.42 02-Oct-17 02:42:30 330.50 334.50 332.13
09-Oct-17 06:41:30 352.00 352.26 346.44
02-Oct-17 02:43:00 330.50 334.50 332.09
09-Oct-17 06:42:00 352.00 352.26 346.49 02-Oct-17 02:43:30 330.50 334.50 332.07
09-Oct-17 06:42:30 352.00 352.26 346.56
02-Oct-17 02:44:00 330.50 334.50 332.11
09-Oct-17 06:43:00 352.00 352.26 346.60 02-Oct-17 02:44:30 330.50 334.51 332.12
09-Oct-17 06:43:30 352.00 352.25 346.62
02-Oct-17 02:45:00 330.50 334.51 332.12
09-Oct-17 06:44:00 352.00 352.22 346.63 02-Oct-17 02:45:30 330.50 334.51 332.11
09-Oct-17 06:44:30 352.00 352.22 346.74
02-Oct-17 02:46:00 330.50 334.51 332.11
09-Oct-17 06:45:00 352.00 352.23 346.83 02-Oct-17 02:46:30 330.50 334.51 332.11
09-Oct-17 06:45:30 352.00 352.24 346.95
02-Oct-17 02:47:00 330.50 334.51 332.11
09-Oct-17 06:46:00 352.00 352.23 347.00 02-Oct-17 02:47:30 330.50 334.51 332.14
09-Oct-17 06:46:30 352.00 352.23 347.04
02-Oct-17 02:48:00 330.50 334.52 332.16
09-Oct-17 06:47:00 352.00 352.22 347.09 02-Oct-17 02:48:30 330.50 334.53 332.17
09-Oct-17 06:47:30 352.00 352.21 347.09
02-Oct-17 02:49:00 330.50 334.53 332.18
09-Oct-17 06:48:00 352.00 352.19 347.08 02-Oct-17 02:49:30 330.50 334.53 332.17
09-Oct-17 06:48:30 352.00 352.19 347.10
02-Oct-17 02:50:00 330.50 334.52 332.12
09-Oct-17 06:49:00 352.00 352.20 347.21 02-Oct-17 02:50:30 330.50 334.52 332.14
09-Oct-17 06:49:30 352.00 352.22 347.29
02-Oct-17 02:51:00 330.50 334.52 332.14
09-Oct-17 06:50:00 352.00 352.23 347.33 02-Oct-17 02:51:30 330.50 334.52 332.14
09-Oct-17 06:50:30 352.00 352.22 347.32
02-Oct-17 02:52:00 330.50 334.52 332.13
09-Oct-17 06:51:00 352.00 352.23 347.37
71
Master of Engineering (Industrial Automation)
02-Oct-17 02:52:30 330.50 334.54 332.17
09-Oct-17 06:51:30 352.00 352.24 347.41 02-Oct-17 02:53:00 330.50 334.54 332.18
09-Oct-17 06:52:00 352.00 352.26 347.46
02-Oct-17 02:53:30 330.50 334.54 332.17
09-Oct-17 06:52:30 352.00 352.27 347.51 02-Oct-17 02:54:00 330.50 334.54 332.17
09-Oct-17 06:53:00 352.00 352.28 347.55
02-Oct-17 02:54:30 330.50 334.55 332.18
09-Oct-17 06:53:30 352.00 352.29 347.57 02-Oct-17 02:55:00 330.50 334.55 332.19
09-Oct-17 06:54:00 352.00 352.30 347.62
02-Oct-17 02:55:30 330.50 334.54 332.16
09-Oct-17 06:54:30 352.00 352.33 347.72 02-Oct-17 02:56:00 330.50 334.55 332.17
09-Oct-17 06:55:00 352.00 352.36 347.77
02-Oct-17 02:56:30 330.50 334.54 332.14
09-Oct-17 06:55:30 352.00 352.38 347.77 02-Oct-17 02:57:00 330.50 334.54 332.11
09-Oct-17 06:56:00 352.00 352.42 348.04
02-Oct-17 02:57:30 330.50 334.54 332.10
09-Oct-17 06:56:30 352.00 352.42 348.06 02-Oct-17 02:58:00 330.50 334.54 332.10
09-Oct-17 06:57:00 352.00 352.44 348.11
02-Oct-17 02:58:30 330.50 334.54 332.08
09-Oct-17 06:57:30 352.00 352.46 348.15 02-Oct-17 02:59:00 330.50 334.54 332.08
09-Oct-17 06:58:00 352.00 352.48 348.22
02-Oct-17 02:59:30 330.50 334.54 332.07
09-Oct-17 06:58:30 352.00 352.50 348.22 02-Oct-17 03:00:00 330.50 334.54 332.03
09-Oct-17 06:59:00 352.00 352.50 348.21
02-Oct-17 03:00:30 330.50 334.55 332.05
09-Oct-17 06:59:30 352.00 352.52 348.22 02-Oct-17 03:01:00 330.50 334.54 332.06
09-Oct-17 07:00:00 352.00 352.54 348.26
02-Oct-17 03:01:30 330.50 334.55 332.07
09-Oct-17 07:00:30 352.00 352.55 348.26 02-Oct-17 03:02:00 330.50 334.56 332.07
09-Oct-17 07:01:00 352.00 352.56 348.25
02-Oct-17 03:02:30 330.50 334.56 332.07
09-Oct-17 07:01:30 352.00 352.58 348.24 02-Oct-17 03:03:00 330.50 334.56 332.07
09-Oct-17 07:02:00 352.00 352.59 348.24
02-Oct-17 03:03:30 330.50 334.57 332.07
09-Oct-17 07:02:30 352.00 352.59 348.17 02-Oct-17 03:04:00 330.50 334.57 332.09
09-Oct-17 07:03:00 352.00 352.62 348.17
02-Oct-17 03:04:30 330.50 334.58 332.11
09-Oct-17 07:03:30 352.00 352.64 348.19 02-Oct-17 03:05:00 330.50 334.58 332.11
09-Oct-17 07:04:00 352.00 352.67 348.22
02-Oct-17 03:05:30 330.50 334.58 332.10
09-Oct-17 07:04:30 352.00 352.69 348.18 02-Oct-17 03:06:00 330.50 334.58 332.11
09-Oct-17 07:05:00 352.00 352.70 348.10
02-Oct-17 03:06:30 330.50 334.59 332.14
09-Oct-17 07:05:30 352.00 352.72 348.11 02-Oct-17 03:07:00 330.50 334.60 332.15
09-Oct-17 07:06:00 352.00 352.77 348.15
02-Oct-17 03:07:30 330.50 334.60 332.16
09-Oct-17 07:06:30 352.00 352.80 348.18 02-Oct-17 03:08:00 330.50 334.60 332.16
09-Oct-17 07:07:00 352.00 352.84 348.19
02-Oct-17 03:08:30 330.50 334.61 332.18
09-Oct-17 07:07:30 352.00 352.87 348.16 02-Oct-17 03:09:00 330.50 334.61 332.18
09-Oct-17 07:08:00 352.00 352.91 348.16
02-Oct-17 03:09:30 330.50 334.62 332.17
09-Oct-17 07:08:30 352.00 352.95 348.19 02-Oct-17 03:10:00 330.50 334.61 332.15
09-Oct-17 07:09:00 352.00 352.99 348.20
02-Oct-17 03:10:30 330.50 334.61 332.12
09-Oct-17 07:09:30 352.00 353.04 348.22 02-Oct-17 03:11:00 330.50 334.61 332.10
09-Oct-17 07:10:00 352.00 353.07 348.23
02-Oct-17 03:11:30 330.50 334.61 332.11
09-Oct-17 07:10:30 352.00 353.10 348.18 02-Oct-17 03:12:00 330.50 334.61 332.09
09-Oct-17 07:11:00 352.00 353.14 348.18
02-Oct-17 03:12:30 330.50 334.61 332.11
09-Oct-17 07:11:30 352.00 353.17 348.20 02-Oct-17 03:13:00 330.50 334.62 332.12
09-Oct-17 07:12:00 352.00 353.20 348.20
02-Oct-17 03:13:30 330.50 334.62 332.10
09-Oct-17 07:12:30 352.00 353.22 348.17 02-Oct-17 03:14:00 330.50 334.62 332.13
09-Oct-17 07:13:00 352.00 353.25 348.17
02-Oct-17 03:14:30 330.50 334.62 332.12
09-Oct-17 07:13:30 352.00 353.27 348.17 02-Oct-17 03:15:00 330.50 334.62 332.11
09-Oct-17 07:14:00 352.00 353.29 348.10
02-Oct-17 03:15:30 330.50 334.62 332.13
09-Oct-17 07:14:30 352.00 353.32 348.09 02-Oct-17 03:16:00 330.50 334.62 332.12
09-Oct-17 07:15:00 352.00 353.34 348.12
02-Oct-17 03:16:30 330.50 334.61 332.11
09-Oct-17 07:15:30 352.00 353.33 348.08 02-Oct-17 03:17:00 330.50 334.62 332.12
09-Oct-17 07:16:00 352.00 353.31 348.03
02-Oct-17 03:17:30 330.50 334.63 332.16
09-Oct-17 07:16:30 352.00 353.32 348.01 02-Oct-17 03:18:00 330.50 334.63 332.17
09-Oct-17 07:17:00 352.00 353.33 348.00
02-Oct-17 03:18:30 330.50 334.62 332.11
09-Oct-17 07:17:30 352.00 353.33 347.98 02-Oct-17 03:19:00 330.50 334.62 332.10
09-Oct-17 07:18:00 352.00 353.35 347.95
02-Oct-17 03:19:30 330.50 334.63 332.12
09-Oct-17 07:18:30 352.00 353.36 347.96 02-Oct-17 03:20:00 330.50 334.63 332.13
09-Oct-17 07:19:00 352.00 353.40 347.96
02-Oct-17 03:20:30 330.50 334.62 332.11
09-Oct-17 07:19:30 352.00 353.41 348.02
72
Master of Engineering (Industrial Automation)
02-Oct-17 03:21:00 330.50 334.63 332.14
09-Oct-17 07:20:00 352.00 353.43 348.01 02-Oct-17 03:21:30 330.50 334.63 332.14
09-Oct-17 07:20:30 352.00 353.44 347.99
02-Oct-17 03:22:00 330.50 334.62 332.11
09-Oct-17 07:21:00 352.00 353.50 348.01 02-Oct-17 03:22:30 330.50 334.63 332.12
09-Oct-17 07:21:30 352.00 353.48 347.99
02-Oct-17 03:23:00 330.50 334.63 332.11
09-Oct-17 07:22:00 352.00 353.48 347.98 02-Oct-17 03:23:30 330.50 334.63 332.13
09-Oct-17 07:22:30 352.00 353.49 347.96
02-Oct-17 03:24:00 330.50 334.63 332.11
09-Oct-17 07:23:00 352.00 353.48 347.96 02-Oct-17 03:24:30 330.50 334.64 332.15
09-Oct-17 07:23:30 352.00 353.49 347.94
02-Oct-17 03:25:00 330.50 334.64 332.16
09-Oct-17 07:24:00 352.00 353.49 347.94 02-Oct-17 03:25:30 330.50 334.64 332.15
09-Oct-17 07:24:30 352.00 353.48 347.92
02-Oct-17 03:26:00 330.50 334.64 332.16
09-Oct-17 07:25:00 352.00 353.48 347.96 02-Oct-17 03:26:30 330.50 334.64 332.14
09-Oct-17 07:25:30 352.00 353.47 347.96
02-Oct-17 03:27:00 330.50 334.64 332.17
09-Oct-17 07:26:00 352.00 353.45 347.95 02-Oct-17 03:27:30 330.50 334.64 332.18
09-Oct-17 07:26:30 352.00 353.43 347.96
02-Oct-17 03:28:00 330.50 334.64 332.20
09-Oct-17 07:27:00 352.00 353.42 347.96 02-Oct-17 03:28:30 330.50 334.64 332.18
09-Oct-17 07:27:30 352.00 353.39 347.93
02-Oct-17 03:29:00 330.50 334.64 332.20
09-Oct-17 07:28:00 352.00 353.37 347.94 02-Oct-17 03:29:30 330.50 334.64 332.17
09-Oct-17 07:28:30 352.00 353.36 347.98
02-Oct-17 03:30:00 330.50 334.63 332.14
09-Oct-17 07:29:00 352.00 353.34 347.98 02-Oct-17 03:30:30 330.50 334.63 332.15
09-Oct-17 07:29:30 352.00 353.32 347.94
02-Oct-17 03:31:00 330.50 334.64 332.18
09-Oct-17 07:30:00 352.00 353.31 347.95 02-Oct-17 03:31:30 330.50 334.64 332.20
09-Oct-17 07:30:30 352.00 353.30 347.95
02-Oct-17 03:32:00 330.50 334.64 332.21
09-Oct-17 07:31:00 352.00 353.21 347.95 02-Oct-17 03:32:30 330.50 334.64 332.20
09-Oct-17 07:31:30 352.00 353.29 347.95
02-Oct-17 03:33:00 330.50 334.64 332.21
09-Oct-17 07:32:00 352.00 353.28 347.95 02-Oct-17 03:33:30 330.50 334.64 332.22
09-Oct-17 07:32:30 352.00 353.28 347.96
02-Oct-17 03:34:00 330.50 334.63 332.16
09-Oct-17 07:33:00 352.00 353.27 347.96 02-Oct-17 03:34:30 330.50 334.63 332.19
09-Oct-17 07:33:30 352.00 353.25 347.94
02-Oct-17 03:35:00 330.50 334.64 332.21
09-Oct-17 07:34:00 352.00 353.24 347.92 02-Oct-17 03:35:30 330.50 334.64 332.21
09-Oct-17 07:34:30 352.00 353.24 347.92
02-Oct-17 03:36:00 330.50 334.63 332.19
09-Oct-17 07:35:00 352.00 353.24 347.93 02-Oct-17 03:36:30 330.50 334.63 332.19
09-Oct-17 07:35:30 352.00 353.24 347.91
02-Oct-17 03:37:00 330.50 334.65 332.21
09-Oct-17 07:36:00 352.00 353.22 347.88 02-Oct-17 03:37:30 330.50 334.65 332.20
09-Oct-17 07:36:30 352.00 353.21 347.84
02-Oct-17 03:38:00 330.50 334.64 332.19
09-Oct-17 07:37:00 352.00 353.21 347.85 02-Oct-17 03:38:30 330.50 334.65 332.20
09-Oct-17 07:37:30 352.00 353.21 347.85
02-Oct-17 03:39:00 330.50 334.65 332.19
09-Oct-17 07:38:00 352.00 353.21 347.85 02-Oct-17 03:39:30 330.50 334.65 332.17
09-Oct-17 07:38:30 352.00 353.20 347.84
02-Oct-17 03:40:00 330.50 334.65 332.18
09-Oct-17 07:39:00 352.00 353.19 347.81 02-Oct-17 03:40:30 330.50 334.65 332.17
09-Oct-17 07:39:30 352.00 353.19 347.84
02-Oct-17 03:41:00 330.50 334.66 332.20
09-Oct-17 07:40:00 352.00 353.19 347.85 02-Oct-17 03:41:30 330.50 334.66 332.19
09-Oct-17 07:40:30 352.00 353.19 347.85
02-Oct-17 03:42:00 330.50 334.66 332.19
09-Oct-17 07:41:00 352.00 353.18 347.82 02-Oct-17 03:42:30 330.50 334.65 332.17
09-Oct-17 07:41:30 352.00 353.18 347.79
02-Oct-17 03:43:00 330.50 334.65 332.15
09-Oct-17 07:42:00 352.00 353.18 347.79 02-Oct-17 03:43:30 330.50 334.66 332.17
09-Oct-17 07:42:30 352.00 353.18 347.81
02-Oct-17 03:44:00 330.50 334.66 332.16
09-Oct-17 07:43:00 352.00 353.19 347.80 02-Oct-17 03:44:30 330.50 334.66 332.15
09-Oct-17 07:43:30 352.00 353.19 347.79
02-Oct-17 03:45:00 330.50 334.66 332.19
09-Oct-17 07:44:00 352.00 353.20 347.80 02-Oct-17 03:45:30 330.50 334.67 332.18
09-Oct-17 07:44:30 352.00 353.21 347.81
02-Oct-17 03:46:00 330.50 334.67 332.19
09-Oct-17 07:45:00 352.00 353.20 347.80 02-Oct-17 03:46:30 330.50 334.67 332.19
09-Oct-17 07:45:30 352.00 353.20 347.78
02-Oct-17 03:47:00 330.50 334.68 332.20
09-Oct-17 07:46:00 352.00 353.20 347.76 02-Oct-17 03:47:30 330.50 334.67 332.19
09-Oct-17 07:46:30 352.00 353.21 347.77
02-Oct-17 03:48:00 330.50 334.67 332.18
09-Oct-17 07:47:00 352.00 353.21 347.80 02-Oct-17 03:48:30 330.50 334.67 332.17
09-Oct-17 07:47:30 352.00 353.20 347.74
02-Oct-17 03:49:00 330.50 334.67 332.19
09-Oct-17 07:48:00 352.00 353.21 347.73
73
Master of Engineering (Industrial Automation)
02-Oct-17 03:49:30 330.50 334.67 332.16
09-Oct-17 07:48:30 352.00 353.21 347.73 02-Oct-17 03:50:00 330.50 334.67 332.17
09-Oct-17 07:49:00 352.00 353.20 347.73
02-Oct-17 03:50:30 330.50 334.67 332.20
09-Oct-17 07:49:30 352.00 353.20 347.72 02-Oct-17 03:51:00 330.50 334.68 332.20
09-Oct-17 07:50:00 352.00 353.21 347.75
02-Oct-17 03:51:30 330.50 334.67 332.17
09-Oct-17 07:50:30 352.00 353.20 347.72 02-Oct-17 03:52:00 330.50 334.67 332.20
09-Oct-17 07:51:00 352.00 353.20 347.71
02-Oct-17 03:52:30 330.50 334.68 332.20
09-Oct-17 07:51:30 352.00 353.20 347.72 02-Oct-17 03:53:00 330.50 334.68 332.19
09-Oct-17 07:52:00 352.00 353.20 347.71
02-Oct-17 03:53:30 330.50 334.67 332.18
09-Oct-17 07:52:30 352.00 353.20 347.71 02-Oct-17 03:54:00 330.50 334.67 332.16
09-Oct-17 07:53:00 352.00 353.21 347.74
02-Oct-17 03:54:30 330.50 334.66 332.14
09-Oct-17 07:53:30 352.00 353.21 347.73 02-Oct-17 03:55:00 330.50 334.66 332.14
09-Oct-17 07:54:00 352.00 353.19 347.68
02-Oct-17 03:55:30 330.50 334.66 332.11
09-Oct-17 07:54:30 352.00 353.20 347.69 02-Oct-17 03:56:00 330.50 334.66 332.10
09-Oct-17 07:55:00 352.00 353.20 347.69
02-Oct-17 03:56:30 330.50 334.67 332.10
09-Oct-17 07:55:30 352.00 353.20 347.66 02-Oct-17 03:57:00 330.50 334.67 332.13
09-Oct-17 07:56:00 352.00 353.20 347.67
02-Oct-17 03:57:30 330.50 334.68 332.15
09-Oct-17 07:56:30 352.00 353.20 347.69 02-Oct-17 03:58:00 330.50 334.67 332.14
09-Oct-17 07:57:00 352.00 353.20 347.68
02-Oct-17 03:58:30 330.50 334.68 332.15
09-Oct-17 07:57:30 352.00 353.20 347.67 02-Oct-17 03:59:00 330.50 334.68 332.17
09-Oct-17 07:58:00 352.00 353.19 347.69
02-Oct-17 03:59:30 330.50 334.68 332.17
09-Oct-17 07:58:30 352.00 353.19 347.67 02-Oct-17 04:00:00 330.50 334.68 332.17
09-Oct-17 07:59:00 352.00 353.20 347.70
02-Oct-17 04:00:30 330.50 334.68 332.14
09-Oct-17 07:59:30 352.00 353.19 347.66 02-Oct-17 04:01:00 330.50 334.68 332.16
09-Oct-17 08:00:00 352.00 353.18 347.64
02-Oct-17 04:01:30 330.50 334.67 332.14
09-Oct-17 08:00:30 352.00 353.18 347.66 02-Oct-17 04:02:00 330.50 334.68 332.14
09-Oct-17 08:01:00 352.00 353.17 347.66
02-Oct-17 04:02:30 330.50 334.68 332.13
09-Oct-17 08:01:30 352.00 353.17 347.66 02-Oct-17 04:03:00 330.50 334.69 332.16
09-Oct-17 08:02:00 352.00 353.16 347.61
02-Oct-17 04:03:30 330.50 334.68 332.14
09-Oct-17 08:02:30 352.00 353.15 347.62 02-Oct-17 04:04:00 330.50 334.68 332.11
09-Oct-17 08:03:00 352.00 353.15 347.62
02-Oct-17 04:04:30 330.50 334.68 332.13
09-Oct-17 08:03:30 352.00 353.15 347.65 02-Oct-17 04:05:00 330.50 334.69 332.14
09-Oct-17 08:04:00 352.00 353.14 347.64
02-Oct-17 04:05:30 330.50 334.68 332.12
09-Oct-17 08:04:30 352.00 353.13 347.61 02-Oct-17 04:06:00 330.50 334.68 332.13
09-Oct-17 08:05:00 352.00 353.13 347.58
02-Oct-17 04:06:30 330.50 334.68 332.14
09-Oct-17 08:05:30 352.00 353.14 347.65 02-Oct-17 04:07:00 330.50 334.69 332.15
09-Oct-17 08:06:00 352.00 353.13 347.64
02-Oct-17 04:07:30 330.50 334.68 332.16
09-Oct-17 08:06:30 352.00 353.13 347.61 02-Oct-17 04:08:00 330.50 334.68 332.14
09-Oct-17 08:07:00 352.00 353.13 347.61
02-Oct-17 04:08:30 330.50 334.68 332.14
09-Oct-17 08:07:30 352.00 353.12 347.63 02-Oct-17 04:09:00 330.50 334.68 332.14
09-Oct-17 08:08:00 352.00 353.14 347.63
02-Oct-17 04:09:30 330.50 334.68 332.11
09-Oct-17 08:08:30 352.00 353.14 347.70 02-Oct-17 04:10:00 330.50 334.68 332.14
09-Oct-17 08:09:00 352.00 353.13 347.71
02-Oct-17 04:10:30 330.50 334.68 332.16
09-Oct-17 08:09:30 352.00 353.13 347.70 02-Oct-17 04:11:00 330.50 334.67 332.13
09-Oct-17 08:10:00 352.00 353.13 347.69
02-Oct-17 04:11:30 330.50 334.67 332.11
09-Oct-17 08:10:30 352.00 353.14 347.76 02-Oct-17 04:12:00 330.50 334.68 332.14
09-Oct-17 08:11:00 352.00 353.14 347.77
02-Oct-17 04:12:30 330.50 334.67 332.12
09-Oct-17 08:11:30 352.00 353.13 347.72 02-Oct-17 04:13:00 330.50 334.67 332.12
09-Oct-17 08:12:00 352.00 353.12 347.72
02-Oct-17 04:13:30 330.50 334.68 332.11
09-Oct-17 08:12:30 352.00 353.12 347.69 02-Oct-17 04:14:00 330.86 334.67 332.11
09-Oct-17 08:13:00 352.00 353.12 347.69
02-Oct-17 04:14:30 331.00 334.66 332.08
09-Oct-17 08:13:30 352.00 353.12 347.70 02-Oct-17 04:15:00 331.00 334.66 332.06
09-Oct-17 08:14:00 352.00 353.12 347.73
02-Oct-17 04:15:30 331.00 334.65 332.05
09-Oct-17 08:14:30 352.00 353.12 347.72 02-Oct-17 04:16:00 331.00 334.66 332.07
09-Oct-17 08:15:00 352.00 353.11 347.66
02-Oct-17 04:16:30 331.00 334.66 332.07
09-Oct-17 08:15:30 352.00 353.12 347.68 02-Oct-17 04:17:00 331.00 334.66 332.09
09-Oct-17 08:16:00 352.00 353.11 347.70
02-Oct-17 04:17:30 331.00 334.67 332.10
09-Oct-17 08:16:30 352.00 353.11 347.71
74
Master of Engineering (Industrial Automation)
02-Oct-17 04:18:00 331.00 334.67 332.15
09-Oct-17 08:17:00 352.00 353.11 347.68 02-Oct-17 04:18:30 331.00 334.67 332.14
09-Oct-17 08:17:30 352.00 353.10 347.64
02-Oct-17 04:19:00 331.00 334.68 332.20
09-Oct-17 08:18:00 352.00 353.10 347.65 02-Oct-17 04:19:30 331.00 334.69 332.19
09-Oct-17 08:18:30 352.00 353.11 347.67
02-Oct-17 04:20:00 331.00 334.69 332.20
09-Oct-17 08:19:00 352.00 353.11 347.64 02-Oct-17 04:20:30 331.00 334.68 332.19
09-Oct-17 08:19:30 352.00 353.11 347.65
02-Oct-17 04:21:00 331.00 334.68 332.23
09-Oct-17 08:20:00 352.00 353.11 347.64 02-Oct-17 04:21:30 331.00 334.69 332.28
09-Oct-17 08:20:30 352.00 353.11 347.66
02-Oct-17 04:22:00 331.00 334.70 332.30
09-Oct-17 08:21:00 352.00 353.13 347.69 02-Oct-17 04:22:30 331.00 334.70 332.32
09-Oct-17 08:21:30 352.00 353.14 347.72
02-Oct-17 04:23:00 331.00 334.70 332.32
09-Oct-17 08:22:00 352.00 353.14 347.74 02-Oct-17 04:23:30 331.00 334.70 332.32
09-Oct-17 08:22:30 352.00 353.14 347.71
02-Oct-17 04:24:00 331.00 334.70 332.33
09-Oct-17 08:23:00 352.00 353.13 347.69 02-Oct-17 04:24:30 331.00 334.70 332.35
09-Oct-17 08:23:30 352.00 353.14 347.71
02-Oct-17 04:25:00 331.00 334.70 332.35
09-Oct-17 08:24:00 352.00 353.15 347.73 02-Oct-17 04:25:30 331.00 334.70 332.33
09-Oct-17 08:24:30 352.00 353.14 347.71
02-Oct-17 04:26:00 331.00 334.70 332.34
09-Oct-17 08:25:00 352.00 353.14 347.69 02-Oct-17 04:26:30 331.00 334.71 332.37
09-Oct-17 08:25:30 352.00 353.13 347.65
02-Oct-17 04:27:00 331.00 334.70 332.38
09-Oct-17 08:26:00 352.00 353.12 347.62 02-Oct-17 04:27:30 331.00 334.70 332.36
09-Oct-17 08:26:30 352.00 353.12 347.63
02-Oct-17 04:28:00 331.00 334.70 332.38
09-Oct-17 08:27:00 352.00 353.12 347.63 02-Oct-17 04:28:30 331.00 334.70 332.36
09-Oct-17 08:27:30 352.00 353.12 347.60
02-Oct-17 04:29:00 331.00 334.71 332.40
09-Oct-17 08:28:00 352.00 353.11 347.56 02-Oct-17 04:29:30 331.00 334.71 332.41
09-Oct-17 08:28:30 352.00 353.10 347.54
02-Oct-17 04:30:00 331.00 334.71 332.40
09-Oct-17 08:29:00 352.00 353.09 347.53 02-Oct-17 04:30:30 331.00 334.71 332.40
09-Oct-17 08:29:30 352.00 353.09 347.48
02-Oct-17 04:31:00 331.00 334.70 332.39
09-Oct-17 08:30:00 352.00 353.09 347.47 02-Oct-17 04:31:30 331.00 334.71 332.41
09-Oct-17 08:30:30 352.00 353.08 347.45
02-Oct-17 04:32:00 331.00 334.72 332.44
09-Oct-17 08:31:00 352.00 353.07 347.42 02-Oct-17 04:32:30 331.00 334.72 332.45
09-Oct-17 08:31:30 352.00 353.06 347.38
02-Oct-17 04:33:00 331.00 334.73 332.46
09-Oct-17 08:32:00 352.00 353.07 347.43 02-Oct-17 04:33:30 331.00 334.74 332.48
09-Oct-17 08:32:30 352.00 353.07 347.42
02-Oct-17 04:34:00 331.00 334.75 332.53
09-Oct-17 08:33:00 352.00 353.07 347.43 02-Oct-17 04:34:30 331.00 334.75 332.52
09-Oct-17 08:33:30 352.00 353.08 347.46
02-Oct-17 04:35:00 331.00 334.76 332.54
09-Oct-17 08:34:00 352.00 353.07 347.43 02-Oct-17 04:35:30 331.00 334.76 332.57
09-Oct-17 08:34:30 352.00 353.07 347.42
02-Oct-17 04:36:00 331.00 334.77 332.55
09-Oct-17 08:35:00 352.00 353.07 347.43 02-Oct-17 04:36:30 331.00 334.78 332.60
09-Oct-17 08:35:30 352.00 353.08 347.45
02-Oct-17 04:37:00 331.00 334.78 332.58
09-Oct-17 08:36:00 352.00 353.07 347.45 02-Oct-17 04:37:30 331.00 334.79 332.59
09-Oct-17 08:36:30 352.00 353.07 347.46
02-Oct-17 04:38:00 331.00 334.79 332.58
09-Oct-17 08:37:00 352.00 353.06 347.44 02-Oct-17 04:38:30 331.00 334.80 332.58
09-Oct-17 08:37:30 352.00 353.06 347.43
02-Oct-17 04:39:00 331.00 334.80 332.61
09-Oct-17 08:38:00 352.00 353.06 347.45 02-Oct-17 04:39:30 331.00 334.80 332.59
09-Oct-17 08:38:30 352.00 353.06 347.47
02-Oct-17 04:40:00 331.00 334.80 332.56
09-Oct-17 08:39:00 352.00 353.05 347.46 02-Oct-17 04:40:30 331.00 334.81 332.58
09-Oct-17 08:39:30 352.00 353.04 347.44
02-Oct-17 04:41:00 331.00 334.81 332.58
09-Oct-17 08:40:00 352.00 353.04 347.45 02-Oct-17 04:41:30 331.00 334.81 332.58
09-Oct-17 08:40:30 352.00 353.04 347.46
02-Oct-17 04:42:00 331.00 334.81 332.57
09-Oct-17 08:41:00 352.00 353.03 347.46 02-Oct-17 04:42:30 331.00 334.81 332.56
09-Oct-17 08:41:30 352.00 353.02 347.46
02-Oct-17 04:43:00 331.00 334.82 332.57
09-Oct-17 08:42:00 352.00 353.01 347.46 02-Oct-17 04:43:30 331.00 334.83 332.60
09-Oct-17 08:42:30 352.00 353.00 347.44
02-Oct-17 04:44:00 331.00 334.83 332.60
09-Oct-17 08:43:00 352.00 352.99 347.44 02-Oct-17 04:44:30 331.00 334.83 332.59
09-Oct-17 08:43:30 352.00 352.98 347.44
02-Oct-17 04:45:00 331.00 334.84 332.62
09-Oct-17 08:44:00 352.00 352.98 347.41 02-Oct-17 04:45:30 331.00 334.84 332.62
09-Oct-17 08:44:30 352.00 352.98 347.40
02-Oct-17 04:46:00 331.00 334.85 332.62
09-Oct-17 08:45:00 352.00 352.97 347.39
75
Master of Engineering (Industrial Automation)
02-Oct-17 04:46:30 331.00 334.86 332.66
09-Oct-17 08:45:30 352.00 352.97 347.43 02-Oct-17 04:47:00 331.00 334.87 332.66
09-Oct-17 08:46:00 352.00 352.96 347.43
02-Oct-17 04:47:30 331.00 334.88 332.70
09-Oct-17 08:46:30 352.00 352.96 347.43 02-Oct-17 04:48:00 331.00 334.88 332.68
09-Oct-17 08:47:00 352.00 352.96 347.42
02-Oct-17 04:48:30 331.00 334.88 332.69
09-Oct-17 08:47:30 352.00 352.96 347.40 02-Oct-17 04:49:00 331.00 334.89 332.72
09-Oct-17 08:48:00 352.00 352.95 347.40
02-Oct-17 04:49:30 331.00 334.90 332.71
09-Oct-17 08:48:30 352.00 352.95 347.41 02-Oct-17 04:50:00 331.00 334.91 332.72
09-Oct-17 08:49:00 352.00 352.96 347.44
02-Oct-17 04:50:30 331.00 334.91 332.72
09-Oct-17 08:49:30 352.00 352.96 347.45 02-Oct-17 04:51:00 331.00 334.93 332.72
09-Oct-17 08:50:00 352.00 352.96 347.45
02-Oct-17 04:51:30 331.00 334.92 332.69
09-Oct-17 08:50:30 352.00 352.96 347.45 02-Oct-17 04:52:00 331.00 334.93 332.67
09-Oct-17 08:51:00 352.00 352.96 347.46
02-Oct-17 04:52:30 331.00 334.93 332.65
09-Oct-17 08:51:30 352.00 352.96 347.49 02-Oct-17 04:53:00 331.00 334.94 332.67
09-Oct-17 08:52:00 352.00 352.97 347.51
02-Oct-17 04:53:30 331.00 334.94 332.67
09-Oct-17 08:52:30 352.00 352.98 347.52 02-Oct-17 04:54:00 331.00 334.95 332.68
09-Oct-17 08:53:00 352.00 352.96 347.51
02-Oct-17 04:54:30 331.00 334.96 332.67
09-Oct-17 08:53:30 352.00 352.96 347.50 02-Oct-17 04:55:00 331.00 334.97 332.68
09-Oct-17 08:54:00 352.00 352.96 347.54
02-Oct-17 04:55:30 331.00 334.97 332.66
09-Oct-17 08:54:30 352.00 352.96 347.54 02-Oct-17 04:56:00 331.00 334.97 332.66
09-Oct-17 08:55:00 352.00 352.95 347.54
02-Oct-17 04:56:30 331.00 334.99 332.66
09-Oct-17 08:55:30 352.00 352.95 347.51 02-Oct-17 04:57:00 331.00 334.99 332.64
09-Oct-17 08:56:00 352.00 352.95 347.52
02-Oct-17 04:57:30 331.00 334.99 332.65
09-Oct-17 08:56:30 352.00 352.95 347.56 02-Oct-17 04:58:00 331.00 335.00 332.66
09-Oct-17 08:57:00 352.00 352.95 347.55
02-Oct-17 04:58:30 331.00 335.01 332.66
09-Oct-17 08:57:30 352.00 352.95 347.52 02-Oct-17 04:59:00 331.00 335.02 332.68
09-Oct-17 08:58:00 352.00 352.94 347.53
02-Oct-17 04:59:30 331.00 335.03 332.69
09-Oct-17 08:58:30 352.00 352.95 347.54 02-Oct-17 05:00:00 331.00 335.03 332.67
09-Oct-17 08:59:00 352.00 352.95 347.51
02-Oct-17 05:00:30 331.00 335.04 332.68
09-Oct-17 08:59:30 352.00 352.95 347.51 02-Oct-17 05:01:00 331.00 335.05 332.71
09-Oct-17 09:00:00 352.00 352.94 347.48
02-Oct-17 05:01:30 331.00 335.06 332.73
09-Oct-17 09:00:30 352.00 352.94 347.46 02-Oct-17 05:02:00 331.00 335.06 332.70
09-Oct-17 09:01:00 352.00 352.94 347.46
02-Oct-17 05:02:30 331.00 335.07 332.74
09-Oct-17 09:01:30 352.00 352.95 347.46 02-Oct-17 05:03:00 331.00 335.09 332.75
09-Oct-17 09:02:00 352.00 352.94 347.46
02-Oct-17 05:03:30 331.00 335.09 332.73
09-Oct-17 09:02:30 352.00 352.94 347.43 02-Oct-17 05:04:00 331.00 335.10 332.72
09-Oct-17 09:03:00 352.00 352.93 347.42
02-Oct-17 05:04:30 331.00 335.10 332.74
09-Oct-17 09:03:30 352.00 352.94 347.42 02-Oct-17 05:05:00 331.00 335.11 332.72
09-Oct-17 09:04:00 352.00 352.95 347.40
02-Oct-17 05:05:30 331.00 335.12 332.74
09-Oct-17 09:04:30 352.00 352.95 347.40 02-Oct-17 05:06:00 331.00 335.13 332.77
09-Oct-17 09:05:00 352.00 352.95 347.41
02-Oct-17 05:06:30 331.00 335.13 332.77
09-Oct-17 09:05:30 352.00 352.95 347.42 02-Oct-17 05:07:00 331.00 335.14 332.77
09-Oct-17 09:06:00 352.00 352.97 347.44
02-Oct-17 05:07:30 331.00 335.15 332.77
09-Oct-17 09:06:30 352.00 352.97 347.46 02-Oct-17 05:08:00 331.00 335.15 332.75
09-Oct-17 09:07:00 352.00 352.97 347.46
02-Oct-17 05:08:30 331.00 335.16 332.77
09-Oct-17 09:07:30 352.00 352.97 347.46 02-Oct-17 05:09:00 331.00 335.16 332.76
09-Oct-17 09:08:00 352.00 352.97 347.46
02-Oct-17 05:09:30 331.00 335.16 332.74
09-Oct-17 09:08:30 352.00 352.98 347.46 02-Oct-17 05:10:00 331.00 335.16 332.69
09-Oct-17 09:09:00 352.00 352.98 347.48
02-Oct-17 05:10:30 331.00 335.16 332.70
09-Oct-17 09:09:30 352.00 352.98 347.49 02-Oct-17 05:11:00 331.00 335.16 332.67
09-Oct-17 09:10:00 352.00 352.98 347.51
02-Oct-17 05:11:30 331.00 335.17 332.70
09-Oct-17 09:10:30 352.00 352.98 347.48 02-Oct-17 05:12:00 331.00 335.17 332.70
09-Oct-17 09:11:00 352.00 352.98 347.48
02-Oct-17 05:12:30 331.00 335.17 332.66
09-Oct-17 09:11:30 352.00 352.97 347.48 02-Oct-17 05:13:00 331.00 335.17 332.68
09-Oct-17 09:12:00 352.00 352.86 347.46
02-Oct-17 05:13:30 331.00 335.18 332.70
09-Oct-17 09:12:30 352.00 352.96 347.46 02-Oct-17 05:14:00 331.00 335.18 332.66
09-Oct-17 09:13:00 352.00 352.97 347.46
02-Oct-17 05:14:30 331.00 335.17 332.64
09-Oct-17 09:13:30 352.00 352.95 347.42
76
Master of Engineering (Industrial Automation)
02-Oct-17 05:15:00 331.00 335.18 332.68
09-Oct-17 09:14:00 352.00 352.93 347.39 02-Oct-17 05:15:30 331.00 335.20 332.70
09-Oct-17 09:14:30 352.00 352.95 347.37
02-Oct-17 05:16:00 331.00 335.20 332.71
09-Oct-17 09:15:00 352.00 352.94 347.39 02-Oct-17 05:16:30 331.00 335.21 332.72
09-Oct-17 09:15:30 352.00 352.94 347.36
02-Oct-17 05:17:00 331.00 335.21 332.74
09-Oct-17 09:16:00 352.00 352.94 347.37 02-Oct-17 05:17:30 331.00 335.22 332.75
09-Oct-17 09:16:30 352.00 352.95 347.40
02-Oct-17 05:18:00 331.00 335.23 332.78
09-Oct-17 09:17:00 352.00 352.95 347.40 02-Oct-17 05:18:30 331.00 335.23 332.76
09-Oct-17 09:17:30 352.00 352.95 347.42
02-Oct-17 05:19:00 331.00 335.22 332.73
09-Oct-17 09:18:00 352.00 352.96 347.44 02-Oct-17 05:19:30 331.00 335.23 332.75
09-Oct-17 09:18:30 352.00 352.96 347.45
02-Oct-17 05:20:00 331.00 335.24 332.74
09-Oct-17 09:19:00 352.00 352.96 347.47 02-Oct-17 05:20:30 331.00 335.24 332.75
09-Oct-17 09:19:30 352.00 352.97 347.48
02-Oct-17 05:21:00 331.00 335.24 332.76
09-Oct-17 09:20:00 352.00 352.97 347.49 02-Oct-17 05:21:30 331.00 335.24 332.75
09-Oct-17 09:20:30 352.00 352.97 347.48
02-Oct-17 05:22:00 331.00 335.24 332.73
09-Oct-17 09:21:00 352.00 352.97 347.49 02-Oct-17 05:22:30 331.00 335.24 332.72
09-Oct-17 09:21:30 352.00 352.96 347.49
02-Oct-17 05:23:00 331.00 335.25 332.75
09-Oct-17 09:22:00 352.00 352.97 347.52 02-Oct-17 05:23:30 331.00 335.25 332.73
09-Oct-17 09:22:30 352.00 352.97 347.51
02-Oct-17 05:24:00 331.00 335.25 332.71
09-Oct-17 09:23:00 352.00 352.98 347.53 02-Oct-17 05:24:30 331.00 335.25 332.71
09-Oct-17 09:23:30 352.00 352.97 347.52
02-Oct-17 05:25:00 331.00 335.25 332.72
09-Oct-17 09:24:00 352.00 352.96 347.49 02-Oct-17 05:25:30 331.00 335.25 332.71
09-Oct-17 09:24:30 352.00 352.95 347.43
02-Oct-17 05:26:00 331.00 335.25 332.72
09-Oct-17 09:25:00 352.00 352.95 347.45 02-Oct-17 05:26:30 331.00 335.25 332.70
09-Oct-17 09:25:30 352.00 352.94 347.45
02-Oct-17 05:27:00 331.00 335.24 332.67
09-Oct-17 09:26:00 352.00 352.93 347.42 02-Oct-17 05:27:30 331.00 335.24 332.66
09-Oct-17 09:26:30 352.00 352.94 347.42
02-Oct-17 05:28:00 331.00 335.24 332.64
09-Oct-17 09:27:00 352.00 352.92 347.42 02-Oct-17 05:28:30 331.00 335.24 332.65
09-Oct-17 09:27:30 352.00 352.92 347.42
02-Oct-17 05:29:00 331.00 335.25 332.67
09-Oct-17 09:28:00 352.00 352.92 347.43 02-Oct-17 05:29:30 331.00 335.25 332.67
09-Oct-17 09:28:30 352.00 352.91 347.43
02-Oct-17 05:30:00 331.00 335.25 332.68
09-Oct-17 09:29:00 352.00 352.91 347.43 02-Oct-17 05:30:30 331.00 335.26 332.69
09-Oct-17 09:29:30 352.00 352.91 347.46
02-Oct-17 05:31:00 331.00 335.26 332.70
09-Oct-17 09:30:00 352.00 352.92 347.50 02-Oct-17 05:31:30 331.00 335.26 332.68
09-Oct-17 09:30:30 352.00 352.93 347.52
02-Oct-17 05:32:00 331.00 335.26 332.67
09-Oct-17 09:31:00 352.00 352.93 347.54 02-Oct-17 05:32:30 331.00 335.26 332.68
09-Oct-17 09:31:30 352.00 352.94 347.56
02-Oct-17 05:33:00 331.00 335.26 332.67
09-Oct-17 09:32:00 352.00 352.94 347.57 02-Oct-17 05:33:30 331.00 335.27 332.67
09-Oct-17 09:32:30 352.00 352.94 347.58
02-Oct-17 05:34:00 331.00 335.28 332.69
09-Oct-17 09:33:00 352.00 352.94 347.55 02-Oct-17 05:34:30 331.00 335.28 332.70
09-Oct-17 09:33:30 352.00 352.94 347.56
02-Oct-17 05:35:00 331.00 335.28 332.69
09-Oct-17 09:34:00 352.00 352.94 347.55 02-Oct-17 05:35:30 331.00 335.27 332.67
09-Oct-17 09:34:30 352.00 352.93 347.54
02-Oct-17 05:36:00 331.00 335.28 332.67
09-Oct-17 09:35:00 352.00 352.94 347.53 02-Oct-17 05:36:30 331.00 335.28 332.67
09-Oct-17 09:35:30 352.00 352.93 347.51
02-Oct-17 05:37:00 331.00 335.28 332.66
09-Oct-17 09:36:00 352.00 352.94 347.50 02-Oct-17 05:37:30 331.00 335.29 332.67
09-Oct-17 09:36:30 352.00 352.94 347.50
02-Oct-17 05:38:00 331.00 335.28 332.68
09-Oct-17 09:37:00 352.00 352.93 347.50 02-Oct-17 05:38:30 331.00 335.28 332.68
09-Oct-17 09:37:30 352.00 352.93 347.51
02-Oct-17 05:39:00 331.00 335.28 332.65
09-Oct-17 09:38:00 352.00 352.93 347.49 02-Oct-17 05:39:30 331.00 335.29 332.66
09-Oct-17 09:38:30 352.00 352.93 347.50
02-Oct-17 05:40:00 331.00 335.28 332.64
09-Oct-17 09:39:00 352.00 352.93 347.50 02-Oct-17 05:40:30 331.00 335.28 332.67
09-Oct-17 09:39:30 352.00 352.93 347.50
02-Oct-17 05:41:00 331.00 335.28 332.66
09-Oct-17 09:40:00 352.00 352.93 347.51 02-Oct-17 05:41:30 331.00 335.28 332.68
09-Oct-17 09:40:30 352.00 352.93 347.49
02-Oct-17 05:42:00 331.00 335.27 332.66
09-Oct-17 09:41:00 352.00 352.94 347.52 02-Oct-17 05:42:30 331.00 335.27 332.66
09-Oct-17 09:41:30 352.00 352.95 347.54
02-Oct-17 05:43:00 331.00 335.27 332.66
09-Oct-17 09:42:00 352.00 352.95 347.54
77
Master of Engineering (Industrial Automation)
02-Oct-17 05:43:30 331.00 335.27 332.67
09-Oct-17 09:42:30 352.00 352.96 347.58 02-Oct-17 05:44:00 331.00 335.27 332.64
09-Oct-17 09:43:00 352.00 352.96 347.59
02-Oct-17 05:44:30 331.00 335.26 332.65
09-Oct-17 09:43:30 352.00 352.97 347.59 02-Oct-17 05:45:00 331.00 335.25 332.61
09-Oct-17 09:44:00 352.00 352.98 347.63
02-Oct-17 05:45:30 331.00 335.26 332.61
09-Oct-17 09:44:30 352.00 352.98 347.63 02-Oct-17 05:46:00 331.00 335.25 332.61
09-Oct-17 09:45:00 352.00 352.98 347.60
02-Oct-17 05:46:30 331.00 335.25 332.61
09-Oct-17 09:45:30 352.00 352.97 347.55 02-Oct-17 05:47:00 331.00 335.25 332.62
09-Oct-17 09:46:00 352.00 352.98 347.54
02-Oct-17 05:47:30 331.00 335.25 332.60
09-Oct-17 09:46:30 352.00 352.99 347.55 02-Oct-17 05:48:00 331.00 335.25 332.61
09-Oct-17 09:47:00 352.00 352.99 347.56
02-Oct-17 05:48:30 331.00 335.26 332.64
09-Oct-17 09:47:30 352.00 352.98 347.55 02-Oct-17 05:49:00 331.00 335.26 332.65
09-Oct-17 09:48:00 352.00 352.98 347.52
02-Oct-17 05:49:30 331.00 335.26 332.61
09-Oct-17 09:48:30 352.00 352.97 347.50 02-Oct-17 05:50:00 331.00 335.26 332.64
09-Oct-17 09:49:00 352.00 352.97 347.50
02-Oct-17 05:50:30 331.00 335.26 332.66
09-Oct-17 09:49:30 352.00 352.98 347.55 02-Oct-17 05:51:00 331.00 335.26 332.66
09-Oct-17 09:50:00 352.00 352.98 347.53
02-Oct-17 05:51:30 331.00 335.27 332.67
09-Oct-17 09:50:30 352.00 352.99 347.57 02-Oct-17 05:52:00 331.00 335.27 332.70
09-Oct-17 09:51:00 352.00 352.99 347.58
02-Oct-17 05:52:30 331.00 335.27 332.69
09-Oct-17 09:51:30 352.00 352.98 347.55 02-Oct-17 05:53:00 331.00 335.27 332.69
09-Oct-17 09:52:00 352.00 352.99 347.57
02-Oct-17 05:53:30 331.00 335.27 332.69
09-Oct-17 09:52:30 352.00 352.98 347.55 02-Oct-17 05:54:00 331.00 335.27 332.69
09-Oct-17 09:53:00 352.00 352.98 347.53
02-Oct-17 05:54:30 331.00 335.27 332.69
09-Oct-17 09:53:30 352.00 352.98 347.56 02-Oct-17 05:55:00 331.00 335.26 332.69
09-Oct-17 09:54:00 352.00 352.98 347.55
02-Oct-17 05:55:30 331.00 335.27 332.70
09-Oct-17 09:54:30 352.00 352.99 347.59 02-Oct-17 05:56:00 331.00 335.26 332.70
09-Oct-17 09:55:00 352.00 353.01 347.61
02-Oct-17 05:56:30 331.00 335.26 332.70
09-Oct-17 09:55:30 352.00 353.01 347.60 02-Oct-17 05:57:00 331.00 335.26 332.67
09-Oct-17 09:56:00 352.00 353.00 347.59
02-Oct-17 05:57:30 331.00 335.26 332.65
09-Oct-17 09:56:30 352.00 353.01 347.57 02-Oct-17 05:58:00 331.00 335.25 332.64
09-Oct-17 09:57:00 352.00 353.03 347.60
02-Oct-17 05:58:30 331.00 335.26 332.68
09-Oct-17 09:57:30 352.00 353.03 347.62 02-Oct-17 05:59:00 331.00 335.26 332.69
09-Oct-17 09:58:00 352.00 353.03 347.61
02-Oct-17 05:59:30 331.00 335.26 332.70
09-Oct-17 09:58:30 352.00 353.03 347.62 02-Oct-17 06:00:00 331.00 335.25 332.66
09-Oct-17 09:59:00 352.00 353.03 347.55
02-Oct-17 06:00:30 331.00 335.25 332.63
09-Oct-17 09:59:30 352.00 353.03 347.57 02-Oct-17 06:01:00 331.00 335.26 332.69
09-Oct-17 10:00:00 352.00 353.03 347.57
02-Oct-17 06:01:30 331.00 335.25 332.70
09-Oct-17 10:00:30 352.00 353.03 347.58 02-Oct-17 06:02:00 331.00 335.26 332.70
09-Oct-17 10:01:00 352.00 353.04 347.59
02-Oct-17 06:02:30 331.00 335.25 332.68
09-Oct-17 10:01:30 352.00 353.03 347.59 02-Oct-17 06:03:00 331.00 335.25 332.69
09-Oct-17 10:02:00 352.00 353.04 347.55
02-Oct-17 06:03:30 331.00 335.25 332.69
09-Oct-17 10:02:30 352.00 353.03 347.52 02-Oct-17 06:04:00 331.00 335.25 332.66
09-Oct-17 10:03:00 352.00 353.05 347.52
02-Oct-17 06:04:30 331.00 335.24 332.65
09-Oct-17 10:03:30 352.00 353.06 347.63 02-Oct-17 06:05:00 331.00 335.24 332.64
09-Oct-17 10:04:00 352.00 353.07 347.64
02-Oct-17 06:05:30 331.00 335.25 332.66
09-Oct-17 10:04:30 352.00 353.07 347.64 02-Oct-17 06:06:00 331.00 335.25 332.67
09-Oct-17 10:05:00 352.00 353.07 347.64
02-Oct-17 06:06:30 331.00 335.25 332.67
09-Oct-17 10:05:30 352.00 353.08 347.68 02-Oct-17 06:07:00 331.00 335.25 332.67
09-Oct-17 10:06:00 352.00 353.10 347.72
02-Oct-17 06:07:30 331.00 335.25 332.67
09-Oct-17 10:06:30 352.00 353.10 347.71 02-Oct-17 06:08:00 331.00 335.25 332.67
09-Oct-17 10:07:00 352.00 353.10 347.70
02-Oct-17 06:08:30 331.00 335.26 332.67
09-Oct-17 10:07:30 352.00 353.10 347.70 02-Oct-17 06:09:00 331.00 335.25 332.66
09-Oct-17 10:08:00 352.00 353.10 347.72
02-Oct-17 06:09:30 331.00 335.25 332.65
09-Oct-17 10:08:30 352.00 353.11 347.73 02-Oct-17 06:10:00 331.00 335.25 332.65
09-Oct-17 10:09:00 352.00 353.12 347.76
02-Oct-17 06:10:30 331.00 335.25 332.67
09-Oct-17 10:09:30 352.00 353.12 347.77 02-Oct-17 06:11:00 331.00 335.25 332.67
09-Oct-17 10:10:00 352.00 353.13 347.78
02-Oct-17 06:11:30 331.00 335.25 332.66
09-Oct-17 10:10:30 352.00 353.13 347.83
78
Master of Engineering (Industrial Automation)
02-Oct-17 06:12:00 331.00 335.25 332.65
09-Oct-17 10:11:00 352.00 353.14 347.85 02-Oct-17 06:12:30 331.00 335.25 332.66
09-Oct-17 10:11:30 352.00 353.14 347.83
02-Oct-17 06:13:00 331.00 335.25 332.69
09-Oct-17 10:12:00 352.00 353.14 347.83 02-Oct-17 06:13:30 331.00 335.25 332.68
09-Oct-17 10:12:30 352.00 353.15 347.85
02-Oct-17 06:14:00 331.00 335.25 332.68
09-Oct-17 10:13:00 352.00 353.15 347.85 02-Oct-17 06:14:30 331.00 335.25 332.67
09-Oct-17 10:13:30 352.00 353.16 347.88
02-Oct-17 06:15:00 331.00 335.25 332.67
09-Oct-17 10:14:00 352.00 353.16 347.89 02-Oct-17 06:15:30 331.00 335.24 332.67
09-Oct-17 10:14:30 352.00 353.16 347.88
02-Oct-17 06:16:00 331.00 335.24 332.63
09-Oct-17 10:15:00 352.00 353.16 347.86 02-Oct-17 06:16:30 331.00 335.24 332.63
09-Oct-17 10:15:30 352.00 353.17 347.88
02-Oct-17 06:17:00 331.00 335.23 332.61
09-Oct-17 10:16:00 352.00 353.18 347.89 02-Oct-17 06:17:30 331.00 335.23 332.59
09-Oct-17 10:16:30 352.00 353.19 347.88
02-Oct-17 06:18:00 331.00 335.23 332.60
09-Oct-17 10:17:00 352.00 353.18 347.87 02-Oct-17 06:18:30 331.00 335.22 332.59
09-Oct-17 10:17:30 352.00 353.19 347.88
02-Oct-17 06:19:00 331.00 335.22 332.57
09-Oct-17 10:18:00 352.00 353.20 347.87 02-Oct-17 06:19:30 331.00 335.21 332.57
09-Oct-17 10:18:30 352.00 353.21 347.91
02-Oct-17 06:20:00 331.00 335.22 332.57
09-Oct-17 10:19:00 352.00 353.22 347.91 02-Oct-17 06:20:30 331.00 335.22 332.59
09-Oct-17 10:19:30 352.00 353.22 347.90
02-Oct-17 06:21:00 331.00 335.22 332.58
09-Oct-17 10:20:00 352.00 353.23 347.90 02-Oct-17 06:21:30 331.00 335.21 332.58
09-Oct-17 10:20:30 352.00 353.24 347.94
02-Oct-17 06:22:00 331.00 335.21 332.57
09-Oct-17 10:21:00 352.00 353.24 347.94 02-Oct-17 06:22:30 331.00 335.22 332.60
09-Oct-17 10:21:30 352.00 353.25 347.89
02-Oct-17 06:23:00 331.00 335.22 332.59
09-Oct-17 10:22:00 352.00 353.28 347.87 02-Oct-17 06:23:30 331.00 335.22 332.61
09-Oct-17 10:22:30 352.00 353.31 347.86
02-Oct-17 06:24:00 331.00 335.22 332.60
09-Oct-17 10:23:00 352.00 353.33 347.85 02-Oct-17 06:24:30 331.00 335.21 332.56
09-Oct-17 10:23:30 352.00 353.35 347.89
02-Oct-17 06:25:00 331.00 335.20 332.57
09-Oct-17 10:24:00 352.00 353.37 347.90 02-Oct-17 06:25:30 331.00 335.22 332.60
09-Oct-17 10:24:30 352.00 353.38 347.91
02-Oct-17 06:26:00 331.00 335.22 332.59
09-Oct-17 10:25:00 352.00 353.39 347.91 02-Oct-17 06:26:30 331.00 335.22 332.62
09-Oct-17 10:25:30 352.00 353.40 347.91
02-Oct-17 06:27:00 331.00 335.22 332.62
09-Oct-17 10:26:00 352.00 353.41 347.94 02-Oct-17 06:27:30 331.00 335.22 332.65
09-Oct-17 10:26:30 352.00 353.42 347.94
02-Oct-17 06:28:00 331.00 335.22 332.67
09-Oct-17 10:27:00 352.00 353.43 347.95 02-Oct-17 06:28:30 331.00 335.23 332.69
09-Oct-17 10:27:30 352.00 353.43 347.93
02-Oct-17 06:29:00 331.00 335.23 332.72
09-Oct-17 10:28:00 352.00 353.45 347.92 02-Oct-17 06:29:30 331.00 335.24 332.73
09-Oct-17 10:28:30 352.00 353.46 347.93
02-Oct-17 06:30:00 331.00 335.24 332.74
09-Oct-17 10:29:00 352.00 353.47 347.89 02-Oct-17 06:30:30 331.00 335.24 332.74
09-Oct-17 10:29:30 352.00 353.49 347.91
02-Oct-17 06:31:00 331.00 335.23 332.76
09-Oct-17 10:30:00 352.00 353.50 347.90 02-Oct-17 06:31:30 331.00 335.23 332.76
09-Oct-17 10:30:30 352.00 353.53 347.91
02-Oct-17 06:32:00 331.00 335.23 332.77
09-Oct-17 10:31:00 352.00 353.55 347.91 02-Oct-17 06:32:30 331.00 335.24 332.79
09-Oct-17 10:31:30 352.00 353.58 347.96
02-Oct-17 06:33:00 331.00 335.23 332.79
09-Oct-17 10:32:00 352.00 353.59 347.93 02-Oct-17 06:33:30 331.00 335.23 332.78
09-Oct-17 10:32:30 352.00 353.61 347.97
02-Oct-17 06:34:00 331.00 335.23 332.81
09-Oct-17 10:33:00 352.00 353.64 347.97 02-Oct-17 06:34:30 331.00 335.23 332.81
09-Oct-17 10:33:30 352.00 353.65 347.96
02-Oct-17 06:35:00 331.00 335.23 332.82
09-Oct-17 10:34:00 352.00 353.67 347.95 02-Oct-17 06:35:30 331.00 335.24 332.84
09-Oct-17 10:34:30 352.00 353.70 347.95
02-Oct-17 06:36:00 331.00 335.24 332.84
09-Oct-17 10:35:00 352.00 353.71 347.93 02-Oct-17 06:36:30 331.00 335.24 332.84
09-Oct-17 10:35:30 352.00 353.73 347.95
02-Oct-17 06:37:00 331.00 335.24 332.84
09-Oct-17 10:36:00 352.00 353.76 348.03 02-Oct-17 06:37:30 331.00 335.24 332.84
09-Oct-17 10:36:30 352.00 353.78 348.04
02-Oct-17 06:38:00 331.00 335.24 332.82
09-Oct-17 10:37:00 352.00 353.79 348.01 02-Oct-17 06:38:30 331.00 335.24 332.81
09-Oct-17 10:37:30 352.00 353.79 348.00
02-Oct-17 06:39:00 331.00 335.24 332.78
09-Oct-17 10:38:00 352.00 353.83 348.05 02-Oct-17 06:39:30 331.00 335.24 332.78
09-Oct-17 10:38:30 352.00 353.85 348.08
02-Oct-17 06:40:00 331.00 335.24 332.79
09-Oct-17 10:39:00 352.00 353.86 348.05
79
Master of Engineering (Industrial Automation)
02-Oct-17 06:40:30 331.00 335.25 332.80
09-Oct-17 10:39:30 352.00 353.86 348.02 02-Oct-17 06:41:00 331.00 335.26 332.81
09-Oct-17 10:40:00 352.00 353.88 348.03
02-Oct-17 06:41:30 331.00 335.26 332.81
09-Oct-17 10:40:30 352.00 353.89 348.03 02-Oct-17 06:42:00 331.00 335.28 332.86
09-Oct-17 10:41:00 352.00 353.91 348.08
02-Oct-17 06:42:30 331.00 335.30 332.87
09-Oct-17 10:41:30 352.00 353.92 348.09 02-Oct-17 06:43:00 331.00 335.32 332.89
09-Oct-17 10:42:00 352.00 353.91 348.05
02-Oct-17 06:43:30 331.00 335.33 332.88
09-Oct-17 10:42:30 352.00 353.93 348.12 02-Oct-17 06:44:00 331.00 335.34 332.91
09-Oct-17 10:43:00 352.00 353.93 348.08
02-Oct-17 06:44:30 331.00 335.36 332.90
09-Oct-17 10:43:30 352.00 353.93 348.10 02-Oct-17 06:45:00 331.00 335.36 332.86
09-Oct-17 10:44:00 352.00 353.94 348.13
02-Oct-17 06:45:30 331.00 335.37 332.88
09-Oct-17 10:44:30 352.00 353.93 348.13 02-Oct-17 06:46:00 331.00 335.38 332.84
09-Oct-17 10:45:00 353.00 353.93 348.15
02-Oct-17 06:46:30 331.00 335.39 332.86
09-Oct-17 10:45:30 353.00 353.93 348.16 02-Oct-17 06:47:00 331.00 335.41 332.86
09-Oct-17 10:46:00 353.00 353.93 348.13
02-Oct-17 06:47:30 331.00 335.42 332.89
09-Oct-17 10:46:30 353.00 353.94 348.13 02-Oct-17 06:48:00 331.00 335.44 332.90
09-Oct-17 10:47:00 353.00 353.95 348.16
02-Oct-17 06:48:30 331.00 335.44 332.89
09-Oct-17 10:47:30 353.00 353.95 348.15 02-Oct-17 06:49:00 331.00 335.45 332.88
09-Oct-17 10:48:00 353.00 353.95 348.15
02-Oct-17 06:49:30 331.00 335.46 332.89
09-Oct-17 10:48:30 353.00 353.95 348.12 02-Oct-17 06:50:00 331.00 335.47 332.87
09-Oct-17 10:49:00 353.00 353.92 348.10
02-Oct-17 06:50:30 331.00 335.48 332.88
09-Oct-17 10:49:30 353.00 353.94 348.10 02-Oct-17 06:51:00 331.00 335.48 332.84
09-Oct-17 10:50:00 353.00 353.96 348.09
02-Oct-17 06:51:30 331.00 335.48 332.84
09-Oct-17 10:50:30 353.00 353.95 348.07 02-Oct-17 06:52:00 331.00 335.47 332.78
09-Oct-17 10:51:00 353.00 353.96 348.10
02-Oct-17 06:52:30 331.00 335.48 332.76
09-Oct-17 10:51:30 353.00 353.97 348.10 02-Oct-17 06:53:00 331.00 335.48 332.76
09-Oct-17 10:52:00 353.00 353.97 348.11
02-Oct-17 06:53:30 331.00 335.48 332.73
09-Oct-17 10:52:30 353.00 353.98 348.12 02-Oct-17 06:54:00 331.00 335.47 332.71
09-Oct-17 10:53:00 353.00 353.98 348.10
02-Oct-17 06:54:30 331.00 335.47 332.69
09-Oct-17 10:53:30 353.00 353.97 348.09 02-Oct-17 06:55:00 331.00 335.47 332.71
09-Oct-17 10:54:00 353.00 354.00 348.18
02-Oct-17 06:55:30 331.00 335.48 332.71
09-Oct-17 10:54:30 353.00 353.99 348.17 02-Oct-17 06:56:00 331.00 335.48 332.73
09-Oct-17 10:55:00 353.00 353.99 348.17
02-Oct-17 06:56:30 331.00 335.48 332.69
09-Oct-17 10:55:30 353.00 354.00 348.18 02-Oct-17 06:57:00 331.00 335.47 332.68
09-Oct-17 10:56:00 353.00 354.00 348.17
02-Oct-17 06:57:30 331.00 335.47 332.65
09-Oct-17 10:56:30 353.00 354.00 348.16 02-Oct-17 06:58:00 331.00 335.46 332.62
09-Oct-17 10:57:00 353.00 354.01 348.16
02-Oct-17 06:58:30 331.00 335.47 332.66
09-Oct-17 10:57:30 353.00 354.00 348.17 02-Oct-17 06:59:00 331.00 335.46 332.59
09-Oct-17 10:58:00 353.00 354.00 348.15
02-Oct-17 06:59:30 331.00 335.46 332.58
09-Oct-17 10:58:30 353.00 354.01 348.10 02-Oct-17 07:00:00 331.00 335.45 332.57
09-Oct-17 10:59:00 353.00 354.00 348.05
02-Oct-17 07:00:30 331.00 335.45 332.53
09-Oct-17 10:59:30 353.00 354.00 348.09 02-Oct-17 07:01:00 331.00 335.45 332.53
09-Oct-17 11:00:00 353.00 354.01 348.11
02-Oct-17 07:01:30 331.00 335.45 332.53
09-Oct-17 11:00:30 353.00 354.00 348.05 02-Oct-17 07:02:00 331.00 335.44 332.53
09-Oct-17 11:01:00 353.00 354.01 348.06
02-Oct-17 07:02:30 331.00 335.44 332.53
09-Oct-17 11:01:30 353.00 354.01 348.07 02-Oct-17 07:03:00 331.00 335.43 332.51
09-Oct-17 11:02:00 353.00 354.01 348.02
02-Oct-17 07:03:30 331.00 335.43 332.48
09-Oct-17 11:02:30 353.00 354.01 347.99 02-Oct-17 07:04:00 331.00 335.42 332.48
09-Oct-17 11:03:00 353.00 354.01 347.97
02-Oct-17 07:04:30 331.00 335.43 332.49
09-Oct-17 11:03:30 353.00 354.02 347.97 02-Oct-17 07:05:00 331.00 335.43 332.53
09-Oct-17 11:04:00 353.00 354.04 348.04
02-Oct-17 07:05:30 331.00 335.42 332.51
09-Oct-17 11:04:30 353.00 354.04 348.00 02-Oct-17 07:06:00 331.00 335.42 332.51
09-Oct-17 11:05:00 353.00 354.04 348.00
02-Oct-17 07:06:30 331.00 335.42 332.51
09-Oct-17 11:05:30 353.00 354.05 348.01 02-Oct-17 07:07:00 331.00 335.41 332.50
09-Oct-17 11:06:00 353.00 354.05 348.01
02-Oct-17 07:07:30 331.00 335.40 332.48
09-Oct-17 11:06:30 353.00 354.05 348.00 02-Oct-17 07:08:00 331.00 335.39 332.48
09-Oct-17 11:07:00 353.00 354.06 347.99
02-Oct-17 07:08:30 331.00 335.39 332.48
09-Oct-17 11:07:30 353.00 354.05 347.94
80
Master of Engineering (Industrial Automation)
02-Oct-17 07:09:00 331.00 335.39 332.50
09-Oct-17 11:08:00 353.00 354.06 347.93 02-Oct-17 07:09:30 331.00 335.38 332.47
09-Oct-17 11:08:30 353.00 354.07 347.93
02-Oct-17 07:10:00 331.00 335.38 332.50
09-Oct-17 11:09:00 353.00 354.06 347.93 02-Oct-17 07:10:30 331.00 335.37 332.51
09-Oct-17 11:09:30 353.00 354.07 348.00
02-Oct-17 07:11:00 331.00 335.37 332.50
09-Oct-17 11:10:00 353.00 354.07 348.00 02-Oct-17 07:11:30 331.00 335.36 332.51
09-Oct-17 11:10:30 353.00 354.07 347.95
02-Oct-17 07:12:00 331.00 335.36 332.54
09-Oct-17 11:11:00 353.00 354.07 347.98 02-Oct-17 07:12:30 331.00 335.35 332.54
09-Oct-17 11:11:30 353.00 354.07 347.96
02-Oct-17 07:13:00 331.00 335.35 332.57
09-Oct-17 11:12:00 353.00 354.07 347.95 02-Oct-17 07:13:30 331.00 335.35 332.58
09-Oct-17 11:12:30 353.00 354.08 347.94
02-Oct-17 07:14:00 331.00 335.34 332.58
09-Oct-17 11:13:00 353.00 354.07 347.93 02-Oct-17 07:14:30 331.00 335.33 332.57
09-Oct-17 11:13:30 353.00 354.07 347.92
02-Oct-17 07:15:00 331.00 335.33 332.57
09-Oct-17 11:14:00 353.00 354.08 347.94 02-Oct-17 07:15:30 331.00 335.33 332.59
09-Oct-17 11:14:30 353.00 354.07 347.91
02-Oct-17 07:16:00 331.00 335.32 332.58
09-Oct-17 11:15:00 353.00 354.07 347.93 02-Oct-17 07:16:30 331.00 335.32 332.58
09-Oct-17 11:15:30 353.00 354.07 347.92
02-Oct-17 07:17:00 331.00 335.32 332.59
09-Oct-17 11:16:00 353.00 354.06 347.89 02-Oct-17 07:17:30 331.00 335.31 332.59
09-Oct-17 11:16:30 353.00 354.07 347.92
02-Oct-17 07:18:00 331.00 335.31 332.59
09-Oct-17 11:17:00 353.00 354.07 347.89 02-Oct-17 07:18:30 331.00 335.29 332.57
09-Oct-17 11:17:30 353.00 354.06 347.86
02-Oct-17 07:19:00 331.00 335.29 332.59
09-Oct-17 11:18:00 353.00 354.07 347.89 02-Oct-17 07:19:30 331.00 335.29 332.60
09-Oct-17 11:18:30 353.00 354.06 347.86
02-Oct-17 07:20:00 331.00 335.28 332.61
09-Oct-17 11:19:00 353.00 354.05 347.83 02-Oct-17 07:20:30 331.00 335.27 332.61
09-Oct-17 11:19:30 353.00 354.06 347.85
02-Oct-17 07:21:00 331.00 335.26 332.61
09-Oct-17 11:20:00 353.00 354.06 347.89 02-Oct-17 07:21:30 331.00 335.26 332.62
09-Oct-17 11:20:30 353.00 354.05 347.79
02-Oct-17 07:22:00 331.00 335.26 332.62
09-Oct-17 11:21:00 353.00 354.06 347.85 02-Oct-17 07:22:30 331.00 335.26 332.63
09-Oct-17 11:21:30 353.00 354.05 347.86
02-Oct-17 07:23:00 331.00 335.25 332.63
09-Oct-17 11:22:00 353.00 354.03 347.86 02-Oct-17 07:23:30 331.00 335.24 332.64
09-Oct-17 11:22:30 353.00 354.04 347.74
02-Oct-17 07:24:00 331.00 335.23 332.62
09-Oct-17 11:23:00 353.00 354.03 347.72 02-Oct-17 07:24:30 331.00 335.22 332.61
09-Oct-17 11:23:30 353.00 354.01 347.65
02-Oct-17 07:25:00 331.00 335.22 332.62
09-Oct-17 11:24:00 353.00 354.01 347.64 02-Oct-17 07:25:30 331.00 335.21 332.62
09-Oct-17 11:24:30 353.00 353.99 347.58
02-Oct-17 07:26:00 331.00 335.20 332.62
09-Oct-17 11:25:00 353.00 353.98 347.51 02-Oct-17 07:26:30 331.00 335.20 332.61
09-Oct-17 11:25:30 353.00 353.98 347.56
02-Oct-17 07:27:00 331.00 335.20 332.61
09-Oct-17 11:26:00 353.00 353.97 347.54 02-Oct-17 07:27:30 331.00 335.18 332.63
09-Oct-17 11:26:30 353.00 353.97 347.53
02-Oct-17 07:28:00 331.00 335.18 332.67
09-Oct-17 11:27:00 353.00 353.97 347.56 02-Oct-17 07:28:30 331.00 335.18 332.67
09-Oct-17 11:27:30 353.00 353.96 347.52
02-Oct-17 07:29:00 331.00 335.17 332.67
09-Oct-17 11:28:00 353.00 353.94 347.46 02-Oct-17 07:29:30 331.00 335.17 332.69
09-Oct-17 11:28:30 353.00 353.95 347.48
02-Oct-17 07:30:00 331.00 335.17 332.70
09-Oct-17 11:29:00 353.00 353.94 347.45 02-Oct-17 07:30:30 331.00 335.16 332.68
09-Oct-17 11:29:30 353.00 353.93 347.46
02-Oct-17 07:31:00 331.00 335.15 332.67
09-Oct-17 11:30:00 353.00 353.93 347.47 02-Oct-17 07:31:30 331.00 335.15 332.66
09-Oct-17 11:30:30 353.00 353.92 347.45
02-Oct-17 07:32:00 331.00 335.15 332.67
09-Oct-17 11:31:00 353.00 353.90 347.42 02-Oct-17 07:32:30 331.00 335.15 332.68
09-Oct-17 11:31:30 353.00 353.89 347.41
02-Oct-17 07:33:00 331.00 335.14 332.68
09-Oct-17 11:32:00 353.00 353.88 347.38 02-Oct-17 07:33:30 331.00 335.14 332.69
09-Oct-17 11:32:30 353.00 353.87 347.37
02-Oct-17 07:34:00 331.00 335.14 332.67
09-Oct-17 11:33:00 353.00 353.88 347.41 02-Oct-17 07:34:30 331.00 335.13 332.68
09-Oct-17 11:33:30 353.00 353.86 347.38
02-Oct-17 07:35:00 331.00 335.13 332.69
09-Oct-17 11:34:00 353.00 353.85 347.38 02-Oct-17 07:35:30 331.00 335.13 332.67
09-Oct-17 11:34:30 353.00 353.85 347.40
02-Oct-17 07:36:00 331.00 335.12 332.65
09-Oct-17 11:35:00 353.00 353.84 347.39 02-Oct-17 07:36:30 331.00 335.12 332.65
09-Oct-17 11:35:30 353.00 353.82 347.34
02-Oct-17 07:37:00 331.00 335.11 332.64
09-Oct-17 11:36:00 353.00 353.82 347.34
81
Master of Engineering (Industrial Automation)
02-Oct-17 07:37:30 331.00 335.10 332.61
09-Oct-17 11:36:30 353.00 353.81 347.34 02-Oct-17 07:38:00 331.00 335.10 332.58
09-Oct-17 11:37:00 353.00 353.79 347.34
02-Oct-17 07:38:30 331.00 335.10 332.60
09-Oct-17 11:37:30 353.00 353.79 347.34 02-Oct-17 07:39:00 331.00 335.10 332.59
09-Oct-17 11:38:00 353.00 353.77 347.33
02-Oct-17 07:39:30 331.00 335.10 332.58
09-Oct-17 11:38:30 353.00 353.76 347.31 02-Oct-17 07:40:00 331.00 335.10 332.58
09-Oct-17 11:39:00 353.00 353.75 347.31
02-Oct-17 07:40:30 331.00 335.10 332.56
09-Oct-17 11:39:30 353.00 353.73 347.25 02-Oct-17 07:41:00 331.00 335.09 332.56
09-Oct-17 11:40:00 353.00 353.71 347.21
02-Oct-17 07:41:30 331.00 335.10 332.56
09-Oct-17 11:40:30 353.00 353.71 347.26 02-Oct-17 07:42:00 331.00 335.10 332.57
09-Oct-17 11:41:00 353.00 353.70 347.21
02-Oct-17 07:42:30 331.00 335.11 332.62
09-Oct-17 11:41:30 353.00 353.70 347.25 02-Oct-17 07:43:00 331.00 335.12 332.62
09-Oct-17 11:42:00 353.00 353.69 347.25
02-Oct-17 07:43:30 331.00 335.12 332.63
09-Oct-17 11:42:30 353.00 353.67 347.22 02-Oct-17 07:44:00 331.00 335.13 332.66
09-Oct-17 11:43:00 353.00 353.65 347.27
02-Oct-17 07:44:30 331.00 335.13 332.65
09-Oct-17 11:43:30 353.00 353.65 347.21 02-Oct-17 07:45:00 331.00 335.14 332.66
09-Oct-17 11:44:00 353.00 353.64 347.20
02-Oct-17 07:45:30 331.00 335.16 332.71
09-Oct-17 11:44:30 353.00 353.64 347.27 02-Oct-17 07:46:00 331.00 335.17 332.76
09-Oct-17 11:45:00 353.00 353.63 347.24
02-Oct-17 07:46:30 331.00 335.17 332.76
09-Oct-17 11:45:30 353.00 353.62 347.24 02-Oct-17 07:47:00 331.00 335.18 332.76
09-Oct-17 11:46:00 353.00 353.61 347.25
02-Oct-17 07:47:30 331.00 335.20 332.81
09-Oct-17 11:46:30 353.00 353.59 347.19 02-Oct-17 07:48:00 331.00 335.21 332.83
09-Oct-17 11:47:00 353.00 353.60 347.20
02-Oct-17 07:48:30 331.00 335.24 332.89
09-Oct-17 11:47:30 353.00 353.57 347.22 02-Oct-17 07:49:00 331.00 335.25 332.89
09-Oct-17 11:48:00 353.00 353.56 347.22
02-Oct-17 07:49:30 331.00 335.25 332.86
09-Oct-17 11:48:30 353.00 353.56 347.23 02-Oct-17 07:50:00 331.00 335.25 332.83
09-Oct-17 11:49:00 353.00 353.53 347.19
02-Oct-17 07:50:30 331.00 335.26 332.83
09-Oct-17 11:49:30 353.00 353.52 347.22 02-Oct-17 07:51:00 331.00 335.27 332.80
09-Oct-17 11:50:00 353.00 353.52 347.26
02-Oct-17 07:51:30 331.00 335.27 332.79
09-Oct-17 11:50:30 353.00 353.51 347.21 02-Oct-17 07:52:00 331.00 335.28 332.80
09-Oct-17 11:51:00 353.00 353.48 347.20
02-Oct-17 07:52:30 331.00 335.28 332.78
09-Oct-17 11:51:30 353.00 353.47 347.24 02-Oct-17 07:53:00 331.00 335.30 332.79
09-Oct-17 11:52:00 353.00 353.44 347.15
02-Oct-17 07:53:30 331.00 335.30 332.79
09-Oct-17 11:52:30 353.00 353.43 347.19 02-Oct-17 07:54:00 331.00 335.31 332.82
09-Oct-17 11:53:00 353.00 353.42 347.16
02-Oct-17 07:54:30 331.00 335.32 332.82
09-Oct-17 11:53:30 353.00 353.40 347.16 02-Oct-17 07:55:00 331.00 335.33 332.86
09-Oct-17 11:54:00 353.00 353.40 347.20
02-Oct-17 07:55:30 331.00 335.35 332.85
09-Oct-17 11:54:30 353.00 353.39 347.17 02-Oct-17 07:56:00 331.00 335.36 332.85
09-Oct-17 11:55:00 353.00 353.39 347.23
02-Oct-17 07:56:30 331.00 335.37 332.84
09-Oct-17 11:55:30 353.00 353.37 347.20 02-Oct-17 07:57:00 331.00 335.38 332.85
09-Oct-17 11:56:00 353.00 353.36 347.17
02-Oct-17 07:57:30 331.00 335.40 332.85
09-Oct-17 11:56:30 353.00 353.35 347.17 02-Oct-17 07:58:00 331.00 335.42 332.86
09-Oct-17 11:57:00 353.00 353.32 347.15
02-Oct-17 07:58:30 331.00 335.43 332.86
09-Oct-17 11:57:30 353.00 353.32 347.16 02-Oct-17 07:59:00 331.00 335.44 332.82
09-Oct-17 11:58:00 353.00 353.32 347.19
02-Oct-17 07:59:30 331.00 335.46 332.86
09-Oct-17 11:58:30 353.00 353.29 347.13 02-Oct-17 08:00:00 332.00 335.48 332.86
09-Oct-17 11:59:00 353.00 353.28 347.14
02-Oct-17 08:00:30 332.00 335.50 332.88
09-Oct-17 11:59:30 353.00 353.28 347.17 02-Oct-17 08:01:00 332.00 335.51 332.87
09-Oct-17 12:00:00 353.00 353.26 347.13
02-Oct-17 08:01:30 332.00 335.52 332.86
09-Oct-17 12:00:30 353.00 353.26 347.13 02-Oct-17 08:02:00 332.00 335.54 332.89
09-Oct-17 12:01:00 353.00 353.24 347.13
02-Oct-17 08:02:30 332.00 335.57 332.93
09-Oct-17 12:01:30 353.00 353.22 347.09 02-Oct-17 08:03:00 332.00 335.59 332.98
09-Oct-17 12:02:00 353.00 353.22 347.11
02-Oct-17 08:03:30 332.00 335.61 333.03
09-Oct-17 12:02:30 353.00 353.21 347.09 02-Oct-17 08:04:00 332.00 335.62 333.00
09-Oct-17 12:03:00 353.00 353.20 347.07
02-Oct-17 08:04:30 332.00 335.63 332.97
09-Oct-17 12:03:30 353.00 353.19 347.09 02-Oct-17 08:05:00 332.00 335.64 332.99
09-Oct-17 12:04:00 353.00 353.16 347.04
02-Oct-17 08:05:30 332.00 335.67 333.02
09-Oct-17 12:04:30 353.00 353.16 347.04
82
Master of Engineering (Industrial Automation)
02-Oct-17 08:06:00 332.00 335.69 333.07
09-Oct-17 12:05:00 353.00 353.17 347.07 02-Oct-17 08:06:30 332.00 335.70 333.11
09-Oct-17 12:05:30 353.00 353.15 347.03
02-Oct-17 08:07:00 332.00 335.72 333.13
09-Oct-17 12:06:00 353.00 353.15 347.02 02-Oct-17 08:07:30 332.00 335.75 333.24
09-Oct-17 12:06:30 353.00 353.15 347.08
02-Oct-17 08:08:00 332.00 335.77 333.28
09-Oct-17 12:07:00 353.00 353.15 347.07 02-Oct-17 08:08:30 332.00 335.78 333.29
09-Oct-17 12:07:30 353.00 353.14 347.07
02-Oct-17 08:09:00 332.00 335.79 333.31
09-Oct-17 12:08:00 353.00 353.15 347.10 02-Oct-17 08:09:30 332.00 335.80 333.32
09-Oct-17 12:08:30 353.00 353.14 347.08
02-Oct-17 08:10:00 332.00 335.82 333.32
09-Oct-17 12:09:00 353.00 353.13 347.08 02-Oct-17 08:10:30 332.00 335.82 333.30
09-Oct-17 12:09:30 353.00 353.13 347.08
02-Oct-17 08:11:00 332.00 335.84 333.30
09-Oct-17 12:10:00 353.00 353.12 347.07 02-Oct-17 08:11:30 332.00 335.85 333.30
09-Oct-17 12:10:30 353.00 353.12 347.07
02-Oct-17 08:12:00 332.00 335.86 333.30
09-Oct-17 12:11:00 353.00 353.11 347.06 02-Oct-17 08:12:30 332.00 335.87 333.28
09-Oct-17 12:11:30 353.00 353.11 347.07
02-Oct-17 08:13:00 332.00 335.88 333.26
09-Oct-17 12:12:00 353.00 353.12 347.08 02-Oct-17 08:13:30 332.00 335.89 333.25
09-Oct-17 12:12:30 353.00 353.11 347.05
02-Oct-17 08:14:00 332.00 335.90 333.23
09-Oct-17 12:13:00 353.00 353.10 347.06 02-Oct-17 08:14:30 332.00 335.90 333.20
09-Oct-17 12:13:30 353.00 353.10 347.06
02-Oct-17 08:15:00 332.00 335.90 333.14
09-Oct-17 12:14:00 353.00 353.08 347.06 02-Oct-17 08:15:30 332.00 335.91 333.14
09-Oct-17 12:14:30 353.00 353.08 347.00
02-Oct-17 08:16:00 332.00 335.92 333.16
09-Oct-17 12:15:00 353.00 353.08 346.97 02-Oct-17 08:16:30 332.00 335.94 333.14
09-Oct-17 12:15:30 353.00 353.06 346.96
02-Oct-17 08:17:00 332.00 335.95 333.13
09-Oct-17 12:16:00 353.00 353.06 346.97 02-Oct-17 08:17:30 332.00 335.97 333.15
09-Oct-17 12:16:30 353.00 353.06 346.99
02-Oct-17 08:18:00 332.00 335.98 333.14
09-Oct-17 12:17:00 353.00 353.04 346.95 02-Oct-17 08:18:30 332.00 336.00 333.14
09-Oct-17 12:17:30 353.00 353.04 346.96
02-Oct-17 08:19:00 332.00 336.00 333.13
09-Oct-17 12:18:00 353.00 353.04 346.97 02-Oct-17 08:19:30 332.00 336.02 333.14
09-Oct-17 12:18:30 353.00 353.03 346.96
02-Oct-17 08:20:00 332.00 336.04 333.17
09-Oct-17 12:19:00 353.00 353.03 346.99 02-Oct-17 08:20:30 332.00 336.06 333.19
09-Oct-17 12:19:30 353.00 353.03 347.01
02-Oct-17 08:21:00 332.00 336.07 333.21
09-Oct-17 12:20:00 353.00 353.04 347.03 02-Oct-17 08:21:30 332.00 336.08 333.23
09-Oct-17 12:20:30 353.00 353.04 347.05
02-Oct-17 08:22:00 332.00 336.09 333.25
09-Oct-17 12:21:00 353.00 353.04 347.05 02-Oct-17 08:22:30 332.00 336.09 333.27
09-Oct-17 12:21:30 353.00 353.03 346.99
02-Oct-17 08:23:00 332.00 336.09 333.24
09-Oct-17 12:22:00 353.00 353.04 347.02 02-Oct-17 08:23:30 332.00 336.10 333.26
09-Oct-17 12:22:30 353.00 353.04 347.03
02-Oct-17 08:24:00 332.00 336.09 333.24
09-Oct-17 12:23:00 353.00 353.03 347.01 02-Oct-17 08:24:30 332.00 336.09 333.21
09-Oct-17 12:23:30 353.00 353.04 347.01
02-Oct-17 08:25:00 332.00 336.09 333.20
09-Oct-17 12:24:00 353.00 353.03 346.99 02-Oct-17 08:25:30 332.00 336.10 333.24
09-Oct-17 12:24:30 353.00 353.02 347.01
02-Oct-17 08:26:00 332.00 336.10 333.31
09-Oct-17 12:25:00 353.00 353.02 346.98 02-Oct-17 08:26:30 332.00 336.10 333.35
09-Oct-17 12:25:30 353.00 353.02 346.98
02-Oct-17 08:27:00 332.00 336.10 333.38
09-Oct-17 12:26:00 353.00 353.03 347.03 02-Oct-17 08:27:30 332.00 336.08 333.37
09-Oct-17 12:26:30 353.00 353.01 346.96
02-Oct-17 08:28:00 332.00 336.07 333.35
09-Oct-17 12:27:00 353.00 353.00 346.94 02-Oct-17 08:28:30 332.00 336.07 333.34
09-Oct-17 12:27:30 353.00 353.02 347.00
02-Oct-17 08:29:00 332.00 336.07 333.35
09-Oct-17 12:28:00 353.00 353.00 346.97 02-Oct-17 08:29:30 332.00 336.05 333.35
09-Oct-17 12:28:30 353.00 352.99 346.90
02-Oct-17 08:30:00 332.00 336.05 333.35
09-Oct-17 12:29:00 353.00 352.99 346.91 02-Oct-17 08:30:30 332.00 336.04 333.33
09-Oct-17 12:29:30 353.00 352.97 346.85
02-Oct-17 08:31:00 332.00 336.02 333.31
09-Oct-17 12:30:00 353.00 352.97 346.86 02-Oct-17 08:31:30 332.00 336.01 333.27
09-Oct-17 12:30:30 353.00 352.97 346.89
02-Oct-17 08:32:00 332.00 336.00 333.25
09-Oct-17 12:31:00 353.00 352.96 346.85 02-Oct-17 08:32:30 332.00 335.99 333.24
09-Oct-17 12:31:30 353.00 352.97 346.88
02-Oct-17 08:33:00 332.00 335.99 333.23
09-Oct-17 12:32:00 353.00 352.97 346.89 02-Oct-17 08:33:30 332.00 335.97 333.19
09-Oct-17 12:32:30 353.00 352.96 346.84
02-Oct-17 08:34:00 332.00 335.96 333.16
09-Oct-17 12:33:00 353.00 352.96 346.87
83
Master of Engineering (Industrial Automation)
02-Oct-17 08:34:30 332.00 335.96 333.15
09-Oct-17 12:33:30 353.00 352.97 346.88 02-Oct-17 08:35:00 332.00 335.96 333.15
09-Oct-17 12:34:00 353.00 352.96 346.80
02-Oct-17 08:35:30 332.00 335.96 333.13
09-Oct-17 12:34:30 353.00 352.96 346.83 02-Oct-17 08:36:00 332.00 335.95 333.13
09-Oct-17 12:35:00 353.00 352.96 346.81
02-Oct-17 08:36:30 332.00 335.95 333.14
09-Oct-17 12:35:30 353.00 352.95 346.77 02-Oct-17 08:37:00 332.00 335.95 333.14
09-Oct-17 12:36:00 353.00 352.95 346.82
02-Oct-17 08:37:30 332.00 335.96 333.15
09-Oct-17 12:36:30 353.00 352.94 346.76 02-Oct-17 08:38:00 332.00 335.95 333.15
09-Oct-17 12:37:00 353.00 352.94 346.77
02-Oct-17 08:38:30 332.00 335.95 333.13
09-Oct-17 12:37:30 353.00 352.93 346.71 02-Oct-17 08:39:00 332.00 335.95 333.14
09-Oct-17 12:38:00 353.00 352.92 346.73
02-Oct-17 08:39:30 332.00 335.94 333.16
09-Oct-17 12:38:30 353.00 352.92 346.74 02-Oct-17 08:40:00 332.00 335.94 333.20
09-Oct-17 12:39:00 353.00 352.90 346.67
02-Oct-17 08:40:30 332.00 335.95 333.24
09-Oct-17 12:39:30 353.00 352.91 346.72 02-Oct-17 08:41:00 332.00 335.95 333.28
09-Oct-17 12:40:00 353.00 352.89 346.67
02-Oct-17 08:41:30 332.00 335.94 333.28
09-Oct-17 12:40:30 353.00 352.88 346.63 02-Oct-17 08:42:00 332.00 335.94 333.35
09-Oct-17 12:41:00 353.00 352.87 346.64
02-Oct-17 08:42:30 332.00 335.93 333.37
09-Oct-17 12:41:30 353.00 352.86 346.64 02-Oct-17 08:43:00 332.00 335.92 333.37
09-Oct-17 12:42:00 353.00 352.86 346.63
02-Oct-17 08:43:30 332.00 335.91 333.36
09-Oct-17 12:42:30 353.00 352.86 346.64 02-Oct-17 08:44:00 332.00 335.89 333.38
09-Oct-17 12:43:00 353.00 352.83 346.57
02-Oct-17 08:44:30 332.00 335.88 333.37
09-Oct-17 12:43:30 353.00 352.83 346.59 02-Oct-17 08:45:00 332.00 335.87 333.36
09-Oct-17 12:44:00 353.00 352.83 346.60
02-Oct-17 08:45:30 332.00 335.85 333.36
09-Oct-17 12:44:30 353.00 352.81 346.54 02-Oct-17 08:46:00 332.00 335.83 333.34
09-Oct-17 12:45:00 353.00 352.82 346.56
02-Oct-17 08:46:30 332.00 335.81 333.33
09-Oct-17 12:45:30 353.00 352.80 346.51 02-Oct-17 08:47:00 332.00 335.80 333.32
09-Oct-17 12:46:00 353.00 352.80 346.50
02-Oct-17 08:47:30 332.00 335.78 333.32
09-Oct-17 12:46:30 353.00 352.80 346.54 02-Oct-17 08:48:00 332.00 335.76 333.29
09-Oct-17 12:47:00 353.00 352.79 346.51
02-Oct-17 08:48:30 332.00 335.74 333.29
09-Oct-17 12:47:30 353.00 352.78 346.50 02-Oct-17 08:49:00 332.00 335.73 333.31
09-Oct-17 12:48:00 353.00 352.78 346.52
02-Oct-17 08:49:30 332.00 335.72 333.31
09-Oct-17 12:48:30 353.00 352.77 346.48 02-Oct-17 08:50:00 332.00 335.71 333.33
09-Oct-17 12:49:00 353.00 352.77 346.52
02-Oct-17 08:50:30 332.00 335.70 333.33
09-Oct-17 12:49:30 353.00 352.76 346.49 02-Oct-17 08:51:00 332.00 335.68 333.33
09-Oct-17 12:50:00 353.00 352.75 346.47
02-Oct-17 08:51:30 332.00 335.67 333.35
09-Oct-17 12:50:30 353.00 352.76 346.53 02-Oct-17 08:52:00 332.00 335.67 333.39
09-Oct-17 12:51:00 353.00 352.74 346.48
02-Oct-17 08:52:30 332.00 335.66 333.40
09-Oct-17 12:51:30 353.00 352.73 346.44 02-Oct-17 08:53:00 332.00 335.66 333.44
09-Oct-17 12:52:00 353.00 352.73 346.48
02-Oct-17 08:53:30 332.00 335.66 333.48
09-Oct-17 12:52:30 353.00 352.70 346.40 02-Oct-17 08:54:00 332.00 335.65 333.46
09-Oct-17 12:53:00 353.00 352.70 346.41
02-Oct-17 08:54:30 332.00 335.65 333.49
09-Oct-17 12:53:30 353.00 352.68 346.40 02-Oct-17 08:55:00 332.00 335.64 333.49
09-Oct-17 12:54:00 353.00 352.66 346.37
02-Oct-17 08:55:30 332.00 335.64 333.49
09-Oct-17 12:54:30 353.00 352.65 346.34 02-Oct-17 08:56:00 332.00 335.64 333.53
09-Oct-17 12:55:00 353.00 352.63 346.29
02-Oct-17 08:56:30 332.00 335.64 333.52
09-Oct-17 12:55:30 353.00 352.60 346.21 02-Oct-17 08:57:00 332.00 335.64 333.52
09-Oct-17 12:56:00 353.00 352.59 346.22
02-Oct-17 08:57:30 332.00 335.64 333.52
09-Oct-17 12:56:30 353.00 352.57 346.15 02-Oct-17 08:58:00 332.00 335.63 333.58
09-Oct-17 12:57:00 353.00 352.56 346.16
02-Oct-17 08:58:30 332.00 335.64 333.60
09-Oct-17 12:57:30 353.00 352.55 346.20 02-Oct-17 08:59:00 332.00 335.62 333.58
09-Oct-17 12:58:00 353.00 352.53 346.15
02-Oct-17 08:59:30 332.00 335.63 333.61
09-Oct-17 12:58:30 353.00 352.52 346.18 02-Oct-17 09:00:00 332.00 335.63 333.63
09-Oct-17 12:59:00 353.00 352.52 346.21
02-Oct-17 09:00:30 332.00 335.63 333.63
09-Oct-17 12:59:30 353.00 352.50 346.17 02-Oct-17 09:01:00 332.00 335.62 333.62
09-Oct-17 13:00:00 353.00 352.51 346.27
02-Oct-17 09:01:30 332.00 335.62 333.66
09-Oct-17 13:00:30 353.00 352.51 346.26 02-Oct-17 09:02:00 332.00 335.62 333.67
09-Oct-17 13:01:00 353.00 352.48 346.19
02-Oct-17 09:02:30 332.00 335.62 333.68
09-Oct-17 13:01:30 353.00 352.48 346.18
84
Master of Engineering (Industrial Automation)
02-Oct-17 09:03:00 332.00 335.61 333.64
09-Oct-17 13:02:00 353.00 352.45 346.19 02-Oct-17 09:03:30 332.00 335.61 333.67
09-Oct-17 13:02:30 353.00 352.43 346.15
02-Oct-17 09:04:00 332.00 335.61 333.67
09-Oct-17 13:03:00 353.00 352.42 346.15 02-Oct-17 09:04:30 332.00 335.60 333.66
09-Oct-17 13:03:30 353.00 352.39 346.07
02-Oct-17 09:05:00 332.00 335.61 333.69
09-Oct-17 13:04:00 353.00 352.37 346.04 02-Oct-17 09:05:30 332.00 335.60 333.69
09-Oct-17 13:04:30 353.00 352.35 346.02
02-Oct-17 09:06:00 332.00 335.61 333.72
09-Oct-17 13:05:00 353.00 352.32 345.95 02-Oct-17 09:06:30 332.00 335.61 333.73
09-Oct-17 13:05:30 353.00 352.31 345.96
02-Oct-17 09:07:00 332.00 335.61 333.72
09-Oct-17 13:06:00 353.00 352.29 345.96 02-Oct-17 09:07:30 332.00 335.62 333.72
09-Oct-17 13:06:30 353.00 352.25 345.88
02-Oct-17 09:08:00 332.00 335.62 333.72
09-Oct-17 13:07:00 353.00 352.23 345.84 02-Oct-17 09:08:30 332.00 335.62 333.73
09-Oct-17 13:07:30 353.00 352.21 345.86
02-Oct-17 09:09:00 332.00 335.63 333.73
09-Oct-17 13:08:00 353.00 352.20 345.84 02-Oct-17 09:09:30 332.00 335.64 333.75
09-Oct-17 13:08:30 353.00 352.19 345.86
02-Oct-17 09:10:00 332.00 335.64 333.75
09-Oct-17 13:09:00 353.00 352.17 345.87 02-Oct-17 09:10:30 332.00 335.65 333.74
09-Oct-17 13:09:30 353.00 352.15 345.84
02-Oct-17 09:11:00 332.00 335.66 333.76
09-Oct-17 13:10:00 353.00 352.14 345.88 02-Oct-17 09:11:30 332.00 335.66 333.72
09-Oct-17 13:10:30 353.00 352.12 345.88
02-Oct-17 09:12:00 332.00 335.67 333.73
09-Oct-17 13:11:00 353.00 352.10 345.84 02-Oct-17 09:12:30 332.00 335.67 333.71
09-Oct-17 13:11:30 353.00 352.10 345.88
02-Oct-17 09:13:00 332.00 335.67 333.68
09-Oct-17 13:12:00 353.00 352.08 345.86 02-Oct-17 09:13:30 332.00 335.67 333.67
09-Oct-17 13:12:30 353.00 352.06 345.79
02-Oct-17 09:14:00 332.00 335.68 333.71
09-Oct-17 13:13:00 353.00 352.06 345.82 02-Oct-17 09:14:30 332.00 335.68 333.68
09-Oct-17 13:13:30 353.00 352.05 345.81
02-Oct-17 09:15:00 332.00 335.70 333.72
09-Oct-17 13:14:00 353.00 352.02 345.79 02-Oct-17 09:15:30 332.00 335.69 333.70
09-Oct-17 13:14:30 353.00 352.02 345.83
02-Oct-17 09:16:00 332.00 335.70 333.70
09-Oct-17 13:15:00 353.00 352.00 345.81 02-Oct-17 09:16:30 332.00 335.71 333.71
09-Oct-17 13:15:30 353.00 351.97 345.77
02-Oct-17 09:17:00 332.00 335.72 333.71
09-Oct-17 13:16:00 353.00 351.96 345.80 02-Oct-17 09:17:30 332.00 335.72 333.71
09-Oct-17 13:16:30 353.00 351.96 345.81
02-Oct-17 09:18:00 332.00 335.73 333.76
09-Oct-17 13:17:00 353.00 351.93 345.78 02-Oct-17 09:18:30 332.00 335.74 333.77
09-Oct-17 13:17:30 353.00 351.91 345.78
02-Oct-17 09:19:00 332.00 335.74 333.77
09-Oct-17 13:18:00 353.00 351.89 345.78 02-Oct-17 09:19:30 332.00 335.74 333.76
09-Oct-17 13:18:30 353.00 351.87 345.78
02-Oct-17 09:20:00 332.00 335.75 333.76
09-Oct-17 13:19:00 353.00 351.85 345.77 02-Oct-17 09:20:30 332.00 335.76 333.80
09-Oct-17 13:19:30 353.00 351.84 345.76
02-Oct-17 09:21:00 332.00 335.76 333.75
09-Oct-17 13:20:00 353.00 351.82 345.81 02-Oct-17 09:21:30 332.00 335.77 333.75
09-Oct-17 13:20:30 353.00 351.80 345.78
02-Oct-17 09:22:00 332.00 335.77 333.76
09-Oct-17 13:21:00 353.00 351.78 345.76 02-Oct-17 09:22:30 332.00 335.78 333.75
09-Oct-17 13:21:30 353.00 351.76 345.74
02-Oct-17 09:23:00 332.00 335.79 333.75
09-Oct-17 13:22:00 353.00 351.74 345.72 02-Oct-17 09:23:30 332.00 335.81 333.80
09-Oct-17 13:22:30 353.00 351.73 345.77
02-Oct-17 09:24:00 332.00 335.82 333.81
09-Oct-17 13:23:00 353.00 351.71 345.76 02-Oct-17 09:24:30 332.00 335.83 333.79
09-Oct-17 13:23:30 353.00 351.68 345.71
02-Oct-17 09:25:00 332.00 335.83 333.75
09-Oct-17 13:24:00 353.00 351.67 345.73 02-Oct-17 09:25:30 332.00 335.84 333.77
09-Oct-17 13:24:30 353.00 351.65 345.73
02-Oct-17 09:26:00 332.00 335.86 333.82
09-Oct-17 13:25:00 353.00 351.66 345.81 02-Oct-17 09:26:30 332.00 335.87 333.82
09-Oct-17 13:25:30 353.00 351.64 345.81
02-Oct-17 09:27:00 332.00 335.88 333.81
09-Oct-17 13:26:00 353.00 351.62 345.80 02-Oct-17 09:27:30 332.00 335.90 333.82
09-Oct-17 13:26:30 353.00 351.62 345.85
02-Oct-17 09:28:00 332.00 335.91 333.83
09-Oct-17 13:27:00 353.00 351.60 345.89 02-Oct-17 09:28:30 332.00 335.91 333.83
09-Oct-17 13:27:30 353.00 351.58 345.89
02-Oct-17 09:29:00 332.00 335.93 333.85
09-Oct-17 13:28:00 353.00 351.56 345.78 02-Oct-17 09:29:30 332.00 335.93 333.83
09-Oct-17 13:28:30 353.00 351.54 345.80
02-Oct-17 09:30:00 332.00 335.95 333.82
09-Oct-17 13:29:00 353.00 351.52 345.78 02-Oct-17 09:30:30 332.00 335.95 333.79
09-Oct-17 13:29:30 353.00 351.51 345.80
02-Oct-17 09:31:00 332.00 335.96 333.79
09-Oct-17 13:30:00 353.00 351.50 345.84
85
Master of Engineering (Industrial Automation)
02-Oct-17 09:31:30 332.00 335.97 333.79
09-Oct-17 13:30:30 353.00 351.48 345.84 02-Oct-17 09:32:00 332.00 335.99 333.83
09-Oct-17 13:31:00 353.00 351.47 345.87
02-Oct-17 09:32:30 332.00 335.99 333.83
09-Oct-17 13:31:30 353.00 351.45 345.89 02-Oct-17 09:33:00 332.00 336.01 333.85
09-Oct-17 13:32:00 353.00 351.43 345.88
02-Oct-17 09:33:30 332.00 336.01 333.85
09-Oct-17 13:32:30 353.00 351.41 345.86 02-Oct-17 09:34:00 332.00 336.01 333.85
09-Oct-17 13:33:00 353.00 351.40 345.87
02-Oct-17 09:34:30 332.00 336.02 333.81
09-Oct-17 13:33:30 353.00 351.39 345.88 02-Oct-17 09:35:00 332.00 336.02 333.80
09-Oct-17 13:34:00 353.00 351.37 345.90
02-Oct-17 09:35:30 332.00 336.03 333.78
09-Oct-17 13:34:30 353.00 351.36 345.90 02-Oct-17 09:36:00 332.00 336.03 333.77
09-Oct-17 13:35:00 353.00 351.33 345.86
02-Oct-17 09:36:30 332.00 336.04 333.76
09-Oct-17 13:35:30 353.00 351.31 345.80 02-Oct-17 09:37:00 332.00 336.03 333.71
09-Oct-17 13:36:00 353.00 351.31 345.84
02-Oct-17 09:37:30 332.00 336.04 333.72
09-Oct-17 13:36:30 353.00 351.30 345.87 02-Oct-17 09:38:00 332.00 336.04 333.72
09-Oct-17 13:37:00 353.00 351.30 345.90
02-Oct-17 09:38:30 332.00 336.04 333.69
09-Oct-17 13:37:30 353.00 351.29 345.89 02-Oct-17 09:39:00 332.00 336.05 333.69
09-Oct-17 13:38:00 353.00 351.28 345.88
02-Oct-17 09:39:30 332.00 336.06 333.69
09-Oct-17 13:38:30 353.00 351.29 345.96 02-Oct-17 09:40:00 332.00 336.07 333.72
09-Oct-17 13:39:00 353.00 351.30 346.00
02-Oct-17 09:40:30 332.00 336.07 333.72
09-Oct-17 13:39:30 353.00 351.29 346.02 02-Oct-17 09:41:00 332.00 336.08 333.73
09-Oct-17 13:40:00 353.00 351.28 345.99
02-Oct-17 09:41:30 332.00 336.09 333.76
09-Oct-17 13:40:30 353.00 351.27 345.98 02-Oct-17 09:42:00 332.00 336.09 333.76
09-Oct-17 13:41:00 353.00 351.27 345.99
02-Oct-17 09:42:30 332.00 336.09 333.76
09-Oct-17 13:41:30 353.00 351.26 345.98 02-Oct-17 09:43:00 332.00 336.09 333.76
09-Oct-17 13:42:00 353.00 351.25 345.97
02-Oct-17 09:43:30 332.00 336.09 333.75
09-Oct-17 13:42:30 353.00 351.23 345.95 02-Oct-17 09:44:00 332.00 336.09 333.73
09-Oct-17 13:43:00 353.00 351.24 345.93
02-Oct-17 09:44:30 332.00 336.10 333.77
09-Oct-17 13:43:30 353.00 351.24 345.93 02-Oct-17 09:45:00 332.00 336.10 333.77
09-Oct-17 13:44:00 353.00 351.23 346.01
02-Oct-17 09:45:30 332.00 336.10 333.76
09-Oct-17 13:44:30 353.00 351.23 346.01 02-Oct-17 09:46:00 332.00 336.10 333.77
09-Oct-17 13:45:00 353.00 351.22 346.01
02-Oct-17 09:46:30 332.00 336.10 333.76
09-Oct-17 13:45:30 353.00 351.20 346.00 02-Oct-17 09:47:00 332.00 336.10 333.75
09-Oct-17 13:46:00 353.00 351.22 346.00
02-Oct-17 09:47:30 332.00 336.10 333.74
09-Oct-17 13:46:30 353.00 351.22 346.10 02-Oct-17 09:48:00 332.00 336.11 333.75
09-Oct-17 13:47:00 353.00 351.22 346.12
02-Oct-17 09:48:30 332.00 336.11 333.74
09-Oct-17 13:47:30 353.00 351.21 346.11 02-Oct-17 09:49:00 332.00 336.11 333.74
09-Oct-17 13:48:00 353.00 351.21 346.12
02-Oct-17 09:49:30 332.00 336.12 333.74
09-Oct-17 13:48:30 353.00 351.23 346.19 02-Oct-17 09:50:00 332.00 336.12 333.75
09-Oct-17 13:49:00 353.00 351.23 346.22
02-Oct-17 09:50:30 332.00 336.13 333.75
09-Oct-17 13:49:30 353.00 351.24 346.23 02-Oct-17 09:51:00 332.00 336.13 333.77
09-Oct-17 13:50:00 353.00 351.25 346.27
02-Oct-17 09:51:30 332.00 336.14 333.77
09-Oct-17 13:50:30 353.00 351.26 346.29 02-Oct-17 09:52:00 332.00 336.13 333.73
09-Oct-17 13:51:00 353.00 351.27 346.35
02-Oct-17 09:52:30 332.00 336.13 333.72
09-Oct-17 13:51:30 353.00 351.27 346.33 02-Oct-17 09:53:00 332.00 336.13 333.73
09-Oct-17 13:52:00 353.00 351.27 346.31
02-Oct-17 09:53:30 332.00 336.14 333.74
09-Oct-17 13:52:30 353.00 351.27 346.29 02-Oct-17 09:54:00 332.00 336.14 333.74
09-Oct-17 13:53:00 353.00 351.29 346.33
02-Oct-17 09:54:30 332.00 336.14 333.75
09-Oct-17 13:53:30 353.00 351.30 346.35 02-Oct-17 09:55:00 332.00 336.14 333.75
09-Oct-17 13:54:00 353.00 351.30 346.35
02-Oct-17 09:55:30 332.00 336.15 333.78
09-Oct-17 13:54:30 353.00 351.31 346.36 02-Oct-17 09:56:00 332.00 336.16 333.79
09-Oct-17 13:55:00 353.00 351.32 346.37
02-Oct-17 09:56:30 332.00 336.16 333.78
09-Oct-17 13:55:30 353.00 351.34 346.46 02-Oct-17 09:57:00 332.00 336.16 333.76
09-Oct-17 13:56:00 353.00 351.35 346.46
02-Oct-17 09:57:30 332.00 336.15 333.73
09-Oct-17 13:56:30 353.00 351.36 346.48 02-Oct-17 09:58:00 332.00 336.15 333.74
09-Oct-17 13:57:00 353.00 351.38 346.52
02-Oct-17 09:58:30 332.00 336.16 333.75
09-Oct-17 13:57:30 353.00 351.39 346.54 02-Oct-17 09:59:00 332.00 336.16 333.76
09-Oct-17 13:58:00 353.00 351.40 346.54
02-Oct-17 09:59:30 332.00 336.16 333.78
09-Oct-17 13:58:30 353.00 351.42 346.61
86
Master of Engineering (Industrial Automation)
02-Oct-17 10:00:00 332.00 336.16 333.78
09-Oct-17 13:59:00 353.00 351.43 346.59 02-Oct-17 10:00:30 332.00 336.16 333.78
09-Oct-17 13:59:30 353.00 351.44 346.60
02-Oct-17 10:01:00 332.00 336.17 333.80
09-Oct-17 14:00:00 353.00 351.46 346.64 02-Oct-17 10:01:30 332.00 336.16 333.81
09-Oct-17 14:00:30 353.00 351.48 346.63
02-Oct-17 10:02:00 332.00 336.17 333.82
09-Oct-17 14:01:00 353.00 351.50 346.63 02-Oct-17 10:02:30 332.00 336.17 333.81
09-Oct-17 14:01:30 353.00 351.52 346.68
02-Oct-17 10:03:00 332.00 336.16 333.80
09-Oct-17 14:02:00 353.00 351.52 346.55 02-Oct-17 10:03:30 332.00 336.16 333.76
09-Oct-17 14:02:30 353.00 351.53 346.54
02-Oct-17 10:04:00 332.00 336.15 333.78
09-Oct-17 14:03:00 353.00 351.56 346.42 02-Oct-17 10:04:30 332.00 336.16 333.77
09-Oct-17 14:03:30 353.00 351.58 346.40
02-Oct-17 10:05:00 332.00 336.15 333.78
09-Oct-17 14:04:00 353.00 351.59 346.42 02-Oct-17 10:05:30 332.00 336.15 333.76
09-Oct-17 14:04:30 353.00 351.59 346.39
02-Oct-17 10:06:00 332.00 336.16 333.76
09-Oct-17 14:05:00 353.00 351.58 346.36 02-Oct-17 10:06:30 332.00 336.16 333.81
09-Oct-17 14:05:30 353.00 351.58 346.36
02-Oct-17 10:07:00 332.00 336.16 333.82
09-Oct-17 14:06:00 353.00 351.57 346.36 02-Oct-17 10:07:30 332.00 336.16 333.84
09-Oct-17 14:06:30 353.00 351.56 346.29
02-Oct-17 10:08:00 332.00 336.16 333.85
09-Oct-17 14:07:00 353.00 351.55 346.26 02-Oct-17 10:08:30 332.00 336.16 333.85
09-Oct-17 14:07:30 353.00 351.56 346.26
02-Oct-17 10:09:00 332.00 336.16 333.85
09-Oct-17 14:08:00 353.00 351.56 346.25 02-Oct-17 10:09:30 332.00 336.16 333.86
09-Oct-17 14:08:30 353.00 351.56 346.25
02-Oct-17 10:10:00 332.00 336.16 333.85
09-Oct-17 14:09:00 353.00 351.59 346.45 02-Oct-17 10:10:30 332.00 336.16 333.85
09-Oct-17 14:09:30 353.00 351.59 346.53
02-Oct-17 10:11:00 332.00 336.16 333.84
09-Oct-17 14:10:00 353.00 351.57 346.43 02-Oct-17 10:11:30 332.00 336.16 333.84
09-Oct-17 14:10:30 353.00 351.59 346.48
02-Oct-17 10:12:00 332.00 336.15 333.83
09-Oct-17 14:11:00 353.00 351.61 346.51 02-Oct-17 10:12:30 332.00 336.15 333.81
09-Oct-17 14:11:30 353.00 351.64 346.54
02-Oct-17 10:13:00 332.00 336.15 333.81
09-Oct-17 14:12:00 353.00 351.65 346.56 02-Oct-17 10:13:30 332.00 336.14 333.79
09-Oct-17 14:12:30 353.00 351.68 346.52
02-Oct-17 10:14:00 332.00 336.14 333.76
09-Oct-17 14:13:00 353.00 351.72 346.55 02-Oct-17 10:14:30 332.00 336.15 333.78
09-Oct-17 14:13:30 353.00 351.75 346.55
02-Oct-17 10:15:00 332.00 336.14 333.76
09-Oct-17 14:14:00 353.00 351.78 346.65 02-Oct-17 10:15:30 332.00 336.14 333.75
09-Oct-17 14:14:30 353.00 351.78 346.61
02-Oct-17 10:16:00 332.00 336.14 333.75
09-Oct-17 14:15:00 353.00 351.82 346.66 02-Oct-17 10:16:30 332.00 336.14 333.70
09-Oct-17 14:15:30 353.00 351.82 346.60
02-Oct-17 10:17:00 332.00 336.14 333.71
09-Oct-17 14:16:00 353.00 351.83 346.62 02-Oct-17 10:17:30 332.00 336.14 333.71
09-Oct-17 14:16:30 353.00 351.85 346.61
02-Oct-17 10:18:00 332.00 336.14 333.70
09-Oct-17 14:17:00 353.00 351.85 346.56 02-Oct-17 10:18:30 332.00 336.15 333.71
09-Oct-17 14:17:30 353.00 351.86 346.60
02-Oct-17 10:19:00 332.00 336.15 333.69
09-Oct-17 14:18:00 353.00 351.87 346.60 02-Oct-17 10:19:30 332.00 336.14 333.68
09-Oct-17 14:18:30 353.00 351.88 346.61
02-Oct-17 10:20:00 332.00 336.15 333.70
09-Oct-17 14:19:00 353.00 351.87 346.58 02-Oct-17 10:20:30 332.00 336.15 333.67
09-Oct-17 14:19:30 353.00 351.88 346.60
02-Oct-17 10:21:00 332.00 336.15 333.66
09-Oct-17 14:20:00 353.00 351.90 346.68 02-Oct-17 10:21:30 332.00 336.16 333.68
09-Oct-17 14:20:30 353.00 351.90 346.66
02-Oct-17 10:22:00 332.00 336.17 333.71
09-Oct-17 14:21:00 353.00 351.92 346.72 02-Oct-17 10:22:30 332.00 336.17 333.71
09-Oct-17 14:21:30 353.00 351.92 346.68
02-Oct-17 10:23:00 332.00 336.17 333.71
09-Oct-17 14:22:00 353.00 351.94 346.71 02-Oct-17 10:23:30 332.00 336.17 333.72
09-Oct-17 14:22:30 353.00 351.96 346.73
02-Oct-17 10:24:00 332.00 336.17 333.72
09-Oct-17 14:23:00 353.00 351.98 346.73 02-Oct-17 10:24:30 332.00 336.17 333.68
09-Oct-17 14:23:30 353.00 352.02 346.73
02-Oct-17 10:25:00 332.00 336.17 333.68
09-Oct-17 14:24:00 353.00 352.03 346.84 02-Oct-17 10:25:30 332.00 336.18 333.68
09-Oct-17 14:24:30 353.00 352.06 346.84
87