CT425 Optimize Your Process to Make Profits

Transcription

1 CO900H 1 CT425 Optimize Your Process to Make Profits Rockwell s Model Predictive Control Technology Aaron Dodgson Pavilion Business Development Western Region aadodgson@ra.rockwell.com PUBLIC

2 2 SOLUTIONS Helping you exceed your business goals The Model Predictive Control (MPC) application from Rockwell Automation helps manufacturers achieve precision in a dynamic market, reducing product variability, while increasing yield. Providing the world s leading model-based software to improve our customers profitability. Serving Customers since 1991 Commitment to Innovation Part of Rockwell Automation since 2007 Industry experts leveraging more than 160+ patents in the field of modeling, advanced control and optimization Enhance Profitability Increase Production Reduce Manufacturing Costs Improve Product Quality Reduce Environmental Risk

3 3 How MPC Generates Benefits Process/Specification Limit REDUCES Variability ACHIEVES Plant Obedience 98 MANAGES the process within constraints Variability under operator control Variability reduction with Pavilion MPC Benefits Push process toward limits Maintain quality just within specification ACHIEVES UPLIFT operate closer to specifications and performance limits while maintaining safety margins Before During After TIME

4 4 The Facility: Consists of CVs, MVs, DVs Controlled Variables (CVs) Process variables that need to be maintained at a target or within a set range Manipulated Variables (MVs) Process variables you can adjust that affect the CVs (typically PID set-points) EAMPLE CV CV = Speed (Maximize) Disturbance Variables (DVs) Measured process variables that affect the CVs that are not MVs CV CV = Oil Pressure MV MV = Accelerator DV DV= Wind, Road Slick, Other Cars

5 5 Controller Matrix MV1 CV1 CV2 CV3 Controller consists of a matrix of process model pairs that explain IMPORTANT INTERACTIONS in the process MV2 NO MODEL NO MODEL PREDICT future values of the CVs by movement of all the MVs and DVs MV3 DV1 DV2 NO MODEL PROACTIVE control to coordinate MV setpoints to minimize CV deviations from targets, hence reducing variability

6 6 So What Makes MPC Different? PID SINGLE VARIABLE IN & SINGLE VARIABLE OUT Set up a target and control process variable to the target. No awareness of how control changes impact other PID loops. FEEDBACK CONTROL The controller will take no action unless PV deviates from target INDIRECT CONTROL OF LAB MEASUREMENTS Control property variables through proxy (temperature, pressure, etc.) MPC MULTIVARIABLE IN & MULTIVARIABLE OUT Control strategy based on a holistic comprehension of key process variables and their interdependencies. PREDICTIVE CONTROL Dynamic models developed through process step tests Controller action based on current and anticipated future PV deviations from target. DIRECT CONTROL OF LAB MEASUREMENTS Controller predictions of lab measurements used for control updated as available POOR ABILITY TO HANDLE PROCESS DELAYS During complex dynamic interactions CONSTRAINTS Only Internal awareness of loop s limits on setpoint and output EPLICIT DYNAMIC MODELS Full understanding of process dynamics and interactions CONSTRAINTS Predict and monitor future values of constraints

7 Model Predictive Control (dynamic) What path gets us there? ControlLogix Module in the Chassis Small to Medium Sized Linear Processes (or adaptive systems) Fast Process Applications Server-based Solutions; Any DCS Medium to Large Sized Linear or Nonlinear Processes Complex Process Applications Scalable Products from Integrated to Comprehensive

8 8 Control Structure before MPC??!!??!! Step B,C,A,D?, Limits?, How Much? PID PID PID Lab Process Basic Regulatory Control

9 9 MPC/Optimization Architecture Overview Production Rate Max Higher Yields Lower Energy Virtual Online Analyzers MPC PID PID PID Lab Process

10 10 PlantPAx MPC Configuration Options Option 1 Logix Controller and 1756-MPC Module in one chassis. Ethernet on control LAN for CIP connectivity to backplane (workstation monitoring). Option 2 Logix Controller and 1756-MPC Module in two different chassis. Ethernet on control LAN for CIP connectivity to backplane (workstation monitoring). Option 3 Logix Controller and multiple 1756-MPC Modules in one or more chassis MPC

11 CO900H 11 MPC In The Process Application Examples and Controller Matrix Design PUBLIC

12 Optimizing a Broad Range of Industries Consumer Heavy Chemicals & Plastics Oil & Gas Marine Metals Mining, Minerals & Cement Pulp & Paper Semiconductor Water / Wastewater 52% 5% 28% 15% Other Entertainment Fibers & Textiles Food & Beverage Household & Personal Care Life Sciences Agriculture Education All Others Transportation Airports Automotive Tire Mass Transit

13 Dryer Optimization Challenges Increase Capacity Off-spec Increase Yield Excessive Energy Usage Objectives Manage Process Reduce Moisture Variability Control Variables Reduce Energy Benefits Increased Throughput 9% Reduced Off-spec 75% Increased Yield 1% Reduced Energy 9% Dryer Feed Pump Speed Dryer Inlet Air Flow CV Feed Rate DV Feed %Solids Dryer Inlet Temp Dryer Exhaust Temp CV Exhaust Fan Speed CV Sticky Point Product Moisture PUBLIC MV Manipulated Variable DV Disturbance Variable CV Controlled Variable 13

14 14 Dryer Matrix Nozzle Pressure Feed Pump Speed Feed Rate Sticky Point Product Moisture Exhaust Fan Speed BENEFITS Inlet Air Flow Inlet Temp Exhaust Temp After-Dryer Temps Feed %Solids 9% Increase in Throughput Potential 60% Decrease in Product Moisture Variability 1% Increase in Moisture (Yield) 75% Reduction in Off-Spec Ambient Humidity Upper & Lower Constraint Upper Constraint Lower Constraint Maximize Target

15 Dryer and Evaporator Line Optimization Challenges Milk Variability Energy Inefficiency Increase Throughput Moisture Variability Objectives Stabilize Process Push Evaporators Operate at Limits Dry to Specification Benefits Increased Milk Solids Reduced Energy 12% Increased Throughput 16% Decreased Variability 60% Evaporator Feed %Solids Evaporator TVR Steam Pressure Dryer Inlet Temp Dryer Exhaust Temp Dryer Feed Rate Evaporator Feed Flow Evaporator Stage Temps Dryer Inlet Air Flow Concentrate %Solids Dryer Feed Pump Speed Dryer Feed %Solids Evaporator Condenser Valve Sticky Point Product Moisture PUBLIC MV Manipulated Variable DV Disturbance Variable CV Controlled Variable 15

16 16 Dryer & Evaporator Line Matrix Dryer Inlet Air Flow Dryer Inlet Temp Dryer Exhaust Temp After-Dryer Temps Evaporator MVR Speed(s) Evaporator TVR Steam Pressure Evaporator Feed Flow Ambient Humidity Nozzle Pressure Dryer Feed Pump Speed Dryer Feed Rate Sticky Point Product Moisture Dryer Exhaust Fan Speed Balance Tank Level Evaporator Stage Temps Evaporator Condenser Valve Concentrate %Solids BENEFITS 15% Increased Throughput 16% Increased Outlet Air Relative Humidity 12% Reduced Energy Use 75% Reduced Off-spec Product 1% Increased Moisture Yield Dryer Feed %Solids Evaporator Feed %Solids 60% Reduced Moisture Variability Upper & Lower Constraint Upper Constraint Lower Constraint Maximize Target

17 17 17 Dryer/Evaporator MPC Results Dryer/Evaporator MPC Commissioned Energy Savings: 14.5% Less BTU/Gal Throughput Increase: +10.6% Gal/Year

18 Food Lines Challenges Inconsistent Production Excessive energy consumption Off-spec product Imbalanced Equipment Objectives Balance Production Rates Reduce Energy Reduce Process Variabilities Balance Equipment Benefits Increased Throughput 15% Energy Reduction 12% Decreased Variability 60% Reduce Product Quality Variability PUBLIC MV Manipulated Variable DV Disturbance Variable CV Controlled Variable 18

19 19 Food Lines Line Rate Defects (Crit, Maj, Tot) Sorter Activity Color Variation Color % Solids Product Temp (Exit Freezer) Fryer Steam Valves Precool/Freezer Bed Depth Compressor Load Compressor kw Steam/Lb Product Line Feed Rate Sorter Settings Blanch Time/Temp Dextrose Flow BENEFITS 15% Increased Throughput 12% Reduced Energy Use Dryer Times/Temps Fryer Times/Temps Precool/Freezer Times NH3 Suction Pressure 1% Increased Yield 60% Reduced Quality Variability Improved Operational Stability DV Feed Quality Upper & Lower Constraint Upper Constraint Lower Constraint Maximize Target

20 More Examples of MPC Applications Distillation Evaporation Drying Boilers Milling Aeration Decks Centrifuge Balancing Stripping Columns Compressor Reactor Furnaces And many more

21 23 PAVILION8 + PREDICTIVE QUALITY SOFT SENSORS The MPC Solution performs as well as your best operator, 24 hrs per day, 7 days a week resulting in the highest Quality product - consistently Geoff Rome Automation & Utilities Manager Murray Goulburn Optimizes the Drying Process PAVILION8 OPTIMIZES Moisture Control REDUCED Moisture Variability 52% REDUCED Energy Consumption 10% Increased product throughput 1 tonnes per day

22 EnWin Utilities - Water Pumping Application Minimize Pressure: Achieve minimum requirements at the end of many lines but reduce leakage and pipe breaks. Coordinate Pumps: Respond to pumps turning on and off to meet varied demands. Adapt Model: to different pump sizes and design (VSD, FCV). 21% reduced breaks and maintenance costs reported. 24

23 CO900H 25 Wrap-up & Discussion PUBLIC

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25 27 Application Support Disaster Recovery in the event of hardware or software failure MPC System recovery assistance from server failures Web-based Pavilion8 Support Knowledgebase and annual onsite visits available for APC and live telephone support Quarterly Pavilion8 MPC status reporting and troubleshooting APC application issues Service-Pack Releases provide updates and system changes

26 CO900H 28 Appendix All Application Specific Content & Supplemental Slides PUBLIC

27 29 Why Model Predict Control? DYNAMIC Effectively handle long process delays/deadtimes Capable of controlling long/slow process dynamics Address multiple types of process dynamics MULTIVARIABLE Address complex process interactions Replace multiple single control loops with one controller Determine the best set of targets to achieve all objectives Eliminate fighting between control loops. DISTURBANCE Predict future impact of process disturbances Minimize the impact of disturbances on process Stabilize process operations Dryer Optimization Powder moisture measured at end of fluid bed after dryers Chamber versus fluid beds, seconds versus minutes Nozzle pressure 2 nd order with overshoot vs moisture 1 st order Inlet temp, exhaust temp both effect rates, moisture, stickiness {NA we leave all loops in place} Maintain moisture at target and push rates at highest thermal efficiency {NA we leave all loops in place} Feed Solids Ambient Humidity CONSTRAINT Predict and Monitor future values of constraints Maintain margin of safety for process constraints Consistently push closer to constraints Drive higher throughput of operations Sticky point, avoid blockages and unplanned downtime Nozzle pressure Chamber pressure control (ie exhaust fan speed)

28 30 Why Model Predict Control? DYNAMIC Effectively handle long process delays/deadtimes Capable of controlling long/slow process dynamics Address multiple types of process dynamics Evaporator Control Deadtime from feed to concentrate solids too much for PID Preheating feed by condensing vapors creates drawn out 2 nd order responses versus very fast 1 st order from TVR on final stage MULTIVARIABLE Address complex process interactions Replace multiple single control loops with one controller Determine the best set of targets to achieve all objectives Eliminate fighting between control loops. Must adjust TVR steam for feed change but not at same time Intermediate and final density control loops Balance intermediate MVR stages with TVR finisher stage DISTURBANCE Predict future impact of process disturbances Minimize the impact of disturbances on process Stabilize process operations Feed Solids Condensing capacity CONSTRAINT Predict and Monitor future values of constraints Maintain margin of safety for process constraints Consistently push closer to constraints Drive higher throughput of operations Stage temperatures (higher temps cause faster fouling) Condensing capacity MVR speeds

29 31 Why Model Predict Control? DYNAMIC Effectively handle long process delays/deadtimes Capable of controlling long/slow process dynamics Address multiple types of process dynamics MULTIVARIABLE Address complex process interactions Replace multiple single control loops with one controller Determine the best set of targets to achieve all objectives Eliminate fighting between control loops. Food Line Optimization Often an hour or more from front of line to product quality sample Equipment constraints (fast) versus product quality (slow) Material mixing (1 st order) versus temperature (2 nd order) Rate vs quality {NA we leave all loops in place} Maintain product quality while increasing rates at higher energy efficiency and higher yield {NA we leave all loops in place} DISTURBANCE Predict future impact of process disturbances Minimize the impact of disturbances on process Stabilize process operations Feed quality Ambient conditions Line breaks Downstream limits and outages CONSTRAINT Predict and Monitor future values of constraints Maintain margin of safety for process constraints Consistently push closer to constraints Drive higher throughput of operations Heat input (ie steam valves 100% open) Cooling capacity Material handling Downstream constraints (ie packaging machines)