T2004E (21e)

Step-test free APC implementation using dynamic simulation

Nicholas Alsop Process Control Manager

Preemraff Lysekill

JoseMaria Ferrer Business Consultant Aspen Technology

Prepared for presentation at the 2006 Spring National Meeting

Orlando, FL, April 24-27, 2006

Process Control

April 24, 2006

AIChE shall not be responsible for statements or opinions contained in papers or printed in its publications

Step-test free APC implementation using dynamic simulation

Nicholas Alsop

Process Control Manager Preemraff Lysekill

JoseMaria Ferrer Business Consultant Aspen Technology

Abstract

In a previous article [1], a design procedure was presented for advanced process controllers utilising first principles steady state and dynamic models as an alternative to empirical models identified from plant tests. The procedure was proposed for the challenging control problem posed by the propylene/propane splitter at Preemraff refinery in Lysekil, Sweden. At that time, a simple MISO controller was implemented in the operating plant. The present article shows the same procedure applied for the real APC implementation of the DMCplus® multivariable controller over the same unit. No step-test was needed in the real plant.

Introduction

A detailed description of the motivation and the methodology proposed for the challenging control problem of the Propylene/Propane splitter of the Preemraff Refinery is presented in [1]. In that article it was proposed that the simulation software HYSYS be integrated with the DMCplus controller as an alternative platform for step testing, which is most cumbersome task in an APC project with very long settling times, like this heat-pumped Propylene/Propane splitter of 180 trays with a settling time of 2 or 3 days.

Real plant Step-Tests present some challenges: - The need to excite sufficiently to obtain clear responses may produce off-

spec product - Long settling time responses are drowned by other process disturbances. - They must be of sufficient duration to capture the effect of random

movement in feedforward variables. HYSYS dynamics has been recently used in real industry APC projects [2, 3] to overcome those challenges. It also has served as a simulation platform to experiment with new control technologies [4, 5] for similar units. When a control engineer is prompted to use rigorous models for the Step Test, two main concerns arise: - How long it will take to build the rigorous dynamic model? - How close are the rigorous models to reality?

In the presented propylene/propane project, the authors detail the factors that have contributed to making the work worthwhile and advice as to best practice in using this alternative methodology.

Building and Calibration of the Rigorous Model

“Rubbish in, rubbish out”, this wise expression compiles well the fact that simulation results are only as good as the model that has been built and calibrated. Producing a rigorous model, which reflects well the actual plant conditions, is a very different exercise from producing a rigorous model when designing a new plant. Models of plants to be built don’t need to be reconciled with any historic plant data, the followed modelling methodology is described in Figure 1.

Figure 1. Dynamic modeling methodology.

The percentages shown in the Figure 1 represent the estimated spent time for every step; for the Propylene/Propane case the four steps took about two weeks for a Control Engineer with no previous experience in Dynamic Modeling. [6] provides a more detailed explanation of the steps. To obtain a dynamic model which reflects realistically the plant transients, there are a couple of factors that the modeler should take into account: Tray Hold-up calculation: The tray model has to represent the amount of liquid inside the column, since it will have an effect on the response times of the overall column (more mass = more inertia). The manufacturers column design data has to be introduced into the model, and the simulator calculates the hold-up in each tray for a given internal liquid flow. There are only three geometric parameters that will determine the tray liquid hold-up: column diameter, weir height and total weir length. The total weir length is the sum of the weirs’ length when multiple passes exist. With the previous data and the Francis equation for the height over the weir, the simulator calculated a hold-up volume of 0.77 m3 per tray (Diameter 3.2 m, weir height 50 mm, average total weir length 5.11 m), but the manufacturer was reporting a clear liquid volume of 0.39 m3, (based in the Colwell correlation [7]). The reason for this mismatch is the “Aeration Factor”, which represent the bubbles of the vapor going up through the liquid of the tray [7, 8]. This factor makes that the real amount of liquid in the tray is much lower than considering all the calculated hold-up volume to be full clear liquid. “Clear liquid” is the liquid to which the aerated mass would collapse in the absence of vapor flow. Normal values for this aeration factor are between 0.5 and 0.6. In our case the calculated aeration factor was 0.51 (0.39/0.77).

The version of the simulator used (HYSYS2004) did not consider this factor, which also affects to the pressure drop calculation of the tray (dry hole DP + static liquid head). To properly handle this factor in the simulation it is necessary to artificially reduce the weir height (and/or weir length) until we reach the right holdup volume. Reducing the weir height from 50 mm to 3 mm, the right hold up volume is achieved (0.39 m3 average for the section above the feed). Figure 2 shows the real height of the froth or foam on the trays and the equivalent clear liquid volume after discounting the aeration factor. In the simulation this is achieved by reducing the weir height until the right clear liquid hold-up is reached.

Figure 2. Real froth and simulated clear liquid.

Integrator step-size and tray residence time: After considering the above mentioned “aeration factor” some steps-test moves were done into the model, but surprisingly the response time was about 10 days, which is four times longer than expected. Clearly something else was not properly considered in the model. In order to run the simulations fast (75 realtime factor), the integrator step-size of the simulator pressure-flow solver was initially set to 4 seconds. This step-size seemed to be right for most of the units and controllers, but it was discovered to be the cause of the very long response times of the column. The simulator has the option to manually configure the execution rate of the Pressure Flow Solver (which should be always 1), the controllers (1 is default value), the energy balances (2 is default value) and the composition and flash calculations (10 is default value). For an integrator step-size of 4 seconds, it means that the Pressure-flow solver is executed every 4 simulation seconds, the control layer every 4 seconds (4 per 1), the energy balances every 8 seconds (4 per 2) and the compositional calculations every 16 seconds (4 per 4). This last factor is the most important to take into account here, and it will be called “compositional step-size”, which was 16 seconds in the initial simulations. Figure 3 shows the initially used execution rates as changeable blue numbers in HYSYS.

Figure 3. Initially used execution rates per integrator time step.

The column internal liquid volume flow is 333 m3/h and the tray holdup is 0.39 m3, so the resulting residence time of the liquid on the tray is about 4 seconds. This value reveals that the configured “compositional step-size” of 16 seconds is too large for a residence time of 4 seconds. In order to assure a correct transient simulation, at least four compositional calculations should be made per residence time. This means that, if the residence time is 4 seconds, the compositional step-size should be 1 second or lower. To reach that, the integrator step size is reduced from 4 seconds to 1 second, and the composition and flash calculation execution rate of figure 3 is reduced from 4 to 1. These changes affect the simulation speed, which is slowed down from 75 to 12 realtime factor on a Pentium IV 2 Ghz.

Figure 4. “compositional step-size” versus process response time.

Figure 4 represents the propane molar fraction in the bottom when a change of – 0.1 m3/h is made in the propane bottom flow (FC-232). Three simulations were run for the same change using different compositional step-sizes (4s x 4 = 16s, 1s x 4 = 4s, 1s x 1 = 1s). A fourth simulation was run with a smaller compositional step-size (0.25s x 1 = 0.25s, not represented in the figure), but the result was very similar to the 1s trend line. Therefore, the true response time (as defined by convergence within the ±3% band) of the system was 2.8 days. The incorrect response time of 9.3 days was obtained using a compositional step-size that was too large.

Selecting the Right Base Regulatory Control Layout

In the previous article [1], the basic control layout used for the MISO controller of the minor reflux is show in the figure 5.

Figure 5. Previous control layout with MISO controller.

The bottom level is controlled by the Propane flow, the major reflux is kept constant and the MISO controller uses minor reflux to control the bottom’s impurity with feed-forward static compensators for the atmospheric temperature

and feed rate [1]. This control layout would not be the best for the DMCplus controller as the 2x2 control matrix of top and bottom quality control variables and minor and major reflux manipulated variables is very nearly collinear. This means that aggressive moves in both refluxes will be made by any such DMCplus controller trying to keep top and bottom qualities on spec simultaneously. The complexity of the control problem can also be illustrated by considering a change in total reflux (manipulating the minor reflux) by a miniscule amount (0.25m3 of 369m3/h, equivalent to 0.06%), which results in a large change in the bottoms composition (2% to 6%) after a long time. This is a very high gain (or a very stiff problem), which is typical of superfractionators in this mode which are difficult to control without a better regulatory layout. Two alternative control layouts are represented in Figure 6, which will be called “A”, and Figure 7, which will be called “B”:

Figure 6. “A” control layout configuration.

In the “A” configuration, the bottom level is cascaded to the major reflux

controller, the minor reflux is a simple flow controller (SP is set by the operator and later by DMCplus), and the bottom quality is adjusted by the bottom flow controller (SP is set by the operator and later by DMCplus). This configuration can be extended by adding a new master controller which will act on the minor

reflux controller to control the total internal reflux controller (i.e.:major reflux + minor reflux + subcooling effect), which is very similar to the “B” configuration.

Figure 7. “B” control layout configuration.

In the “B” configuration, the bottom level is cascaded to the minor reflux controller, the major reflux valve controls the total internal reflux (SP is set by the operator and later by DMCplus to adjust top quality) and the bottom quality is adjusted by the bottom flow controller (SP is set by the operator and later by DMCplus). In the B configuration bottoms level is controlled indirectly by major reflux, because the internal reflux controller will move the major reflux whenever the minor reflux is adjusted by the level contoller. In both configurations, it is better to control internal reflux, since on a high reflux column, a few degrees of reflux subcooling is equivalent to a significant amount of additional reflux. Both configurations were tested in the simulator and both handled well the step test introduced in the model. The “A” configuration was selected for the DMCplus project.

Step-Tests in the HYSYS Model

The simulator can be configured to automatically run a large sequence of events and record all the selected variables, even those not available in the plant like internal column flows or temperatures, which is directly imported into the DMCplus Model environment. The performed step tests (one step up and one down) in the HYSYS dynamic model were:

1. MV1 (FC-232.SP): Bottom flow (4 days step, 0.1 m3/h) 2. MV2 (FC-187.SP): Minor reflux (1 day step, 0.3m3/h) 3. MV3 (PC-414.SP): Compressor discharge pressure (1 day step, 0.2bar) 4. FF1 (FC-185.SP): Feed flow (5 days step, 0.25m3/h) 5. FF2: C3 in feed (4 days step, 1%) 6. FF3: Atmospheric Temp (1 day, 15 Deg C)

Figure 8. Simulated Step-Test sequence in HYSYS.

After each step, the impurities were found to stabilize after 2-3 days. The total simulated time for all the step was 32 days, which took about 2.5 real days (a weekend) to run in the PC. All the step test results are automatically recorded

in a formatted *.clc file which is directly imported in the DMCPlus Model application, as show in the Figure 9:

Figure 9. HYSYS Simulated Step-Tests imported in DMCplus Model application. The FC-185 doesn’t exist in the real plant, since the feed flow is imposed by the FCC unit.

Creation of the DMCplus Controller Once the HYSYS step test data was imported into DMCplus, the task of dynamic model identification took on the appearance of any other DMCplus project, with the exception that the process data was noise free. The absence of noise is illustrated in Figure 10, which shows overlapping FIR step response curves for various time-to-steady states for the effect of feed rate on top quality. As shown the total time to steady state is of the order of 4200 minutes.

Figure 10. Noise free models using HYSYS test vectors. The complete dynamic 6x6 model matrix is shown in Figure 11. Note that the fifth step test variable, feed temperature, was dropped as a candidate feedforward variable as noise in this instrument has a significant effect on prediction of both the top and bottom qualities. Variation in feed quality is captured instead by use of a prediction error measurement of differential temperature between two points in the column. The points were chosen on the basis of both speed and magnitude of dynamic response to feed quality changes. The complete list of independent variables is:

1. MV1 (FC-232.SP): Bottom flow (m3/hr) 2. MV2 (FC-187.SP): Minor reflux (m3/hr) 3. MV3 (PC-414.SP): Compressor discharge pressure (bar) 4. FF1: Feed flow (m3/h) 5. FF2: Atmospheric temperature (Deg C) 6. FF3: Column differential temperature prediction error (Deg C)

The complete list of dependent variables is:

1. CV1: Top quality (mol % propane in propylene), logarithmic transform 2. CV2: Bottom quality (mol % propylene in propane), logarithmic transform

3. CV3: Column differential pressure (bar) 4. CV4: Compressor amps (amp) 5. CV5: Reboiler approach temperature (Deg C) 6. CV6: Column differential temperature (Deg C), used for generation of the prediction error vector

Figure 11. Full DMCplus dynamic control matrix. Historised process data was imported into DMCplus in order to validate the dynamic models. A test period was chosen which consisted of several operator moves in the manipulated and feedforward variables. Predicted output data, as generated by the prediction facility in DMCplus, was compared to actual output data. Very reasonable agreement for each prediction was obtained, an example of which is shown in Figure 12.

Figure 12. Agreement between predicted and actual bottom quality.

Implementation of the DMCplus Controller

Tuning of the dynamic elements of the DMCplus controller was performed by extensive simulation of the controller in the DMCplus simulate environment. Tuning factors were established which provided an appropriate trade off between aggressiveness and speed. Steady state optimisation factors, that is “cost factors”, were obtained by case runs of the steady state HYSYS model. Profitability curves for each manipulated variable were generated from which cost factors were obtained by linearisation about a base operating point. Actual market prices for products were used in establishing the profitability curves for each manipulated variable. Finally, a DMCplus controller was built and connection to the DeltaV DCS was established. The DMCplus web interface is shown in Figure 13.

Figure 13. Production Control Web Interface. Due to the extensive simulation effort both in HYSYS and DMCplus simulate, and the fact that the underlying HYSYS model of the propylene/propane splitter provided a sound basis on which to build the controller, next to no additional tuning of the controller was necessary during commissioning. Figure 14 shows the top and bottom quality trends before and after implementation of the controller. The controller was turned off for a period of about a week due to upsets in the FCC unit which is upstream of the propylene/propane splitter. As shown, control of top and bottom quality is resumed immediately the controller is placed on line again.

Figure 14. Nine months time impurities view (red: Propylene, green: propane).

Conclusions

The design procedure, presented in a previous article [1], for advanced process controllers utilising first principles steady state and dynamic models as an alternative to empirical models identified from plant tests, was followed for the DMCplus controller project for the Propylene/Propane splitter, confirming that it is feasible in an industrial environment. Special considerations should be taken into account to properly simulate dynamic behaviour of the column. Rigorous models can minimize, and sometimes eliminate, plant step-tests, but are also useful to analyze alternative control layouts, develop inferentials,

Commissioning complete

Controller off due to FCC upset

train personnel on the new controller and tune the controller. The approach is especially interesting when is used in combination with advanced Automated Testing tools [3, 9] to obtain the initial seed model.

References

1. Alsop, N., Ferrer, J.M., “What Dynamic Simulation brings to a Process Control

Engineer: Applied Case Study to a Propylene/Propane Splitter”, May 2004, ERTC Computing, London, UK.

2. Trivella, F., Marchetti, G. “Integration for innovation”, Hydrocarbon Engineering, November 2004.

3. Mantelli, V., Racheli, M., Bordieri, R., Aloi, N., Trivella, F., Masiello, A., “Integration of Dynamic Simulation and APC: a CDU/VDU case study”, May 25th 2005, ERTC Conference, Budapest, Hungary.

4. Pannocchia, G., Micchi, A., Bulleri, R., Brambilla, A. "Multivariable subspace identification and predictive control of a heat-integrated superfractionator." April 2-5 2006, ADCHEM 2006. Gramado. Brazil.

5. Pannocchia, G., Gallinelli, L., Brambilla, A., Marchetti, G., Trivella, F. "Rigorous simulation and model predictive control of a crude distillation unit." April 2-5 2006, ADCHEM 2006. Gramado. Brazil.

6. Gonzalez, R., Ferrer, J.M., “First-Principles Dynamic Simulation, Worth for Control Engineers? – Depropanizer Case Study”. Jan 2006, Chemical Process Control 7th (CPC7). Lake-Louise, Alberta, Canada.

7. Kister, H.Z., “Distillation Design”, 1992, page 313, McGraw-Hill, ISBN 0-07-034909-6.

8. Lierberman, N.P.&E.T., “Working guide to process equipment”, 2nd edition, 2003, page 18. McGraw-Hill, ISBN 0-07-139087-1.

9. Kalafatis, A., Patel, K., Harmse, M., Zheng, Q., Craik, M., “Multivariable step testing for MPC projects reduces crude unit testing time“, February 2006, Hydrocarbon Processing.