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Data Analytics Process Improvements & Event Detection

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Proficy CSense Solution

Optimize Process

Pro

ce

ss

KP

I

Optimal Performance

Current Performance

3. Control & Optimize Advanced process control (e.g., MPC)

Real-time set-point optimization

2. Monitor, Diagnose & Predict Reduce variation by real-time monitoring and diagnostics

Control loop performance monitoring

Predict lab measurements (soft sensors) for control purposes

Advanced regulatory control

1. Troubleshoot Identify & understand causes of variation

What If Scenario analysis & Benefit estimation

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Analyze, Troubleshoot , & Data mine

Develop & Simulate

Proficy CSense Solution

Deploy, Execute & Report

Monitor KPIs & Control Loops

Predict KPIs (Soft Sensors)

Advanced Process Control

De

velo

pe

r E

dit

ion

Troubleshooter Edition

Run-time Edition

For develop & test

For production use

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Industry examples Industries Problem Key Process / Asset

Brewery

Variation in Beer quality; Reduce chill haze in beer Improve product consistency and reduce waste Optimize Fermentation & Filtration Reduce energy consumption

Mash Tun Fermentation Filtration

Need to increase MER (Mean Effective Rate) Reduce scrap and downtime Determine V-profile (critical machine)

Packaging lines

Food High variation in Powder Moisture (Quality) Reduce energy consumption

Spray Dryer

Unstable exhaust temperatures Spray Dryer

High variation in Density (Quality) Need to increase throughput (or reducing batch cycle time) Need to reduce energy consumption

Evaporator

Water / Waste Water

Ensure Water Quality & Compliance (Drinking Water & Waste Water Effluent) Reduce Operating Cost (e.g. via reduced Energy & Chemicals)

Aeration tanks Settling tanks

Paper & Pulp Paper machine optimization; Moisture control; Brightness Control Paper machine

Coffee Improving yield and gel quality Processing (wet, dry or semi-dry process), Milling, Roasting

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Industrial

Data Intelligence

Food examples: Spray Dryers

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Spray Dryer Diagram

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Spray Drying Process

The drying time for a single droplet may be estimated by the following equation:

t = [r2dLÆHV ] x [mi-mf] / [3h(ÆT)] x [1+mi] , where: t= time (hr); r = radius of droplet; dL = density of liquid (lb/ft

3); ÆHV = latent heat of vaporization (Btu/lb); mi = initial moisture content (lb H2O/lb dry food); mf = final moisture content (lb H2O/lb dry food); h = film coefficient for heat transfer (Btu/ft2/hr/°F); ÆT = temperature difference between initial and final stages (°F).

Separation of Dry Particles; Charm (1971) has given an equation which relates the dimensions of a cyclone to the smallest particle (Dp) which can be separated:

Dp2 = (3.6 Ai D0 µ )/( ¹ZDV0ds ), where : Dp = diameter of particle; Ai = inlet cross sectional area of cyclone; D0 = diameter of outlet of cyclone; µ = viscosity of the fluid; Z = depth of the separator; D = diameter of the separator; V0 = velocity of air/powder mixture entering the cyclone; and ds = density of the particle.

Feed

Drying Air

Compressed Air

Drying Air

Cooling Air

Fluid bed

Dried product Outlet

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Spray Drying Process Average Spray Drying Conditions for Milk

Temperature of air (ambient) 25.0 C Temperature of feed: 60.0 C Temperature of inlet air: 150.0 C Temperature of outlet air: 82.0 C Temperature of drop surface (const. zone) 45.0 C Relative humidity (ambient air) 55.0 % Relative humidity (inlet air, psychrom.) 0.3 % Relative humidity (outlet air, psychrom.) 12.0 % Moisture content of milk: 87.0 % Moisture content of concentrate: 45.0 % Moisture content of powder: 4-5.0 % Moisture zone (constant rate): 9030.0 % Moisture zone (falling rate): 30 5.0 % Droplet size (initial), av. diameter: 40.0 µ Particle size (final) , av. diameter: 20.0 µ Density of milk 1.33 g/ccm Density of milk powder (bulk): .33 g/ccm Velocity of air: 61.0 meters/sec Velocity of droplet(initial): 17,000.0 cm/sec Velocity of droplet (free fall): Å 1.0 cm/sec Drying time (constant rate zone): .0023 sec Drying time (falling rate zone): .0014 sec Drying time (total): .0037 sec Travel distance for drying: 13.5 cm

Feed

Drying Air

Compressed Air

Drying Air

Cooling Air

Fluid bed

Dried product Outlet

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What is the problem? Product quality (Powder Moisture) too low after spray drying, resulting in inefficient utilization of power and hence high operating costs.

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Preparation Multiple trends

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Modeling Model results:

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Knowledge Extraction Enables us to visually see the IO relationship, causes as well as leverages.

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Knowledge Extraction Active rules automatically generated for each scenario.

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Knowledge Extraction Cause analysis.

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Benefit Estimation

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CSense Real-time Causal Analysis

The model provides the basis to built a Cause+ model that can be deployed in real-time.

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Example of Analytics Model

Historical used in

simulation

Process model

simulates reaction

of process

APC Controller

responds to process

reaction

Results written to

SCADA via OPC

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CSense Real-time Causal Analysis The rules are activated each time the set condition is violated and a message is displayed on the corrective action the Operator needs to take.

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Finger print PCA model – optimal vs non optimal

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Finger print PCA model – variables contributing to non optimal operation

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Decision Tree – classifying desirable/undesirable moisture levels

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Industrial

Data Intelligence

Water examples

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• Ensure Water Quality & Compliance

(Drinking Water & Waste Water

Effluent)

• Reduce Operating Cost (of which

Energy & Chemicals are important

components)

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… save up to 30% in Aeration energy consumption

DO Optimization – Wastewater Reduced variations in Dissolved Oxygen during the

aeration process through Advanced Analytics…

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Settled Solids Control – Wastewater Settled solids measurements are generally available after a few of days …

… predicted settled solids values can be used immediately to optimize the control of the plant

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Optimize Chemical Usage Optimize chemical addition while ensuring effluent compliance

Reduced quality deviation in effluent

Optimal amount of chemicals added only when required, resulting in average reduction in usage

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Optimize Energy Efficiency Identify areas and causes of high energy usage

HIGH Total Energy Usage

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Asset Monitoring: Slurry Pump

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Pump Monitoring Problem Definition

Centrifugal pump

Typical measurements

Process

• Inlet pressure (Pi)

• Head (H)

• Fluid flow rate (F)

• Power/Current draw (P/I)

• Variable speed (S)

Mechanical

• Drive end bearing temperature (TDE)

• Non-drive end bearing temperature (TNDE)

• Vibration (V)

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Advanced Troubleshooting & Optimization Capabilities

Cause Analysis – focus on causes for process deviation

Predicted KPI Output & Target trend

Process rule that fires at the current timestamp

What If Scenario Analysis

Input-Output Relationship Analysis

Input trends

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Pump Monitoring Solution

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Current increasing (blue), pressure (cyan) and flow (orange) constant. Indicating impeller wear

Fingerprint model picking up deviation from normal

Impeller replacement

detected 5 days in advance

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Introducing…

Facilities Watch Monitoring HVAC related equipment to ensure Performance & Efficiency

Identify Facilities risk

Proficy Advanced Alarming Reasoner Plug-in

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Example - Facility Analytic Reasoners Real Time Operational Information

Supply airflow is greater

than 10% away from set-point for longer than 0.5 hours

Calculated heat wheel

efficiency is significantly below design parameters

Fan VFD speed not resetting when discharge static pressure is greater than set-point

Outdoor air damper greater than 5% open for

at least 15 minutes during morning warm up

Discharge temperature

less than mixed temperature with cooling valve closed

Advanced Analytics

100+ Intelligent Checks

SMART Air Handler

Smart Air Handlers Operational Efficiency

Using Advanced Analysis

Down

Optimized for Energy Savings & Performance

Running - not Optimized

“An air handler chilled-water valve with a

faulty control code issue — the valve was

always 20 percent open, wasting thousands of dollars in energy. This issue was not easily visible before, but the analytics software was able to detect it

immediately!”

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Proficy Advanced Analytics

PID Watch DOG (Dynamic Operations Guide for Control Loop Performance Monitoring)

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Detailed Report for a Flow Rate Control Loop

BEFORE

AFTER

Process+: Weighted Weekly Out of Limits

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

14 16 18 20 22 24 26 28

Weeks

Pe

rce

nta

ge

Weekly

Linear (Weekly)

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The Story of Lanxess Chemicals

BEFORE: 60% of loops in manual & rest out of control 20% of the time, resulting in sub-optimal process stability and performance

AFTER 6 months: 90% of loops in automatic and in control 90% of the time (and continuously improving) , resulting in significantly and continuously improving process stability and performance

100% ROI in less than 6 months