> > Matrikon Control Performance Monitor: APC Step Testing and Model Identification
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APC Step Testing & Model Identification (TaiJi MPC)
Matrikon™ Control Performance Monitor
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Reduce MPC testing and deployment times by 70%
Enable automated step testing & model identification
Simplify and improve long term MPC maintenance efforts
Reduce disturbances and interventions with closed-loop testing
Download Matrikon Control Performance Monitor Brochure
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Using industry leading TaiJi identification technology, Matrikon Control
Performance Monitor provides an alternative to traditional step testing and
model identification.
Matrikon Control Performance Monitor automatically
step tests multiple variables simultaneously, which affords a more concise model
identification data set. The result is reduced test times, analysis, model
identification and ultimately lower costs for implementing and maintaining
MPC applications.
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Determine expected gains vs benchmarked performance
Create a functional design and configure the plant test, including MVs, DVs, and CVs
Execute multivariate step-testing in closed-loop operations
Automatically initiate model identification and validation
Continually improve the implemented model
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Lower impact on the unit and product quality through non- disruptive testing
Execute automatic, multivariable step-testing
Use TaiJi process modeling technology
Verify MPC performance against the initial benchmarked values
Require less operator intervention than traditional testing to maintain signal-to- noise ratios while still maintaining CVs within operating constraints
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Control performance monitoring optimizes performance across all the layers of the
plant control hierarchy, regardless of hardware or software vendor. Matrikon's Control
Performance Monitoring Services will help you manage, enhance and sustain the benefits
of all regulatory and advanced control systems.
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Article | + View | | | Brochure | + View | | | Datasheet | + View | | | Podcast | + View | | | Presentation | + View |
| + | On-Demand Webcast: Asia/Pacific-Optimize and Tune In your PID Loops (May 2007) (7.0 MB, WMV) |
| + | On-Demand Webcast: Easily Identify Critical Performance Issues in Model Predictive Controllers (July 2007) (6.2 MB, WMV) |
| + | On-Demand Webcast: Optimize and Tune In your PID Loops (May 2007) (6.6 MB, WMV) |
| + | On-Demand Webcast: Successful Implementation of Predictive Asset Management by New Zealand's largest utility, Meridian Energy (Oct 2007) (15 MB, WMV) |
| + | Recorded Webcast - Optimize and Tune In your PID Loops! (5,808 KB, WMV) |
| + | Recorded Webcast: Best Practices with Control Performance Monitoring (7,300 KB, WMV) |
| + | Recorded Webcast: Closing The Loop - Manage Your Plantwide Tuning (10,671 KB, WMV) |
| + | WEBCAST - Closed Loop Identification in Industry: What makes it work? (4,295 KB, WMV) |
| | Success Story | + View |
| + | Abitibi-Consolidated Reduces Variability, Improves Product Quality and Prevents Unplanned Downtime (641 KB, PDF) |
| + | Achieve Positive Business Results by Insuring Controller Performance Benefits with Control Monitoring (1.1 MB, PDF) |
| + | AmerenUE Targets Key Control Tuning Issues and Extracts More Value From Assets (PID) (2.75 MB, PDF) |
| + | BHP Billiton’s Queensland Nickel Total Boiler Solution (1.1 MB, PDF) |
| + | BOC Improves Plant Stability, Throughput and Control Quality and Reduces Energy Consumption with Matrikon Control Performance Monitor (APC) (525 KB, PDF) |
| + | Control Improves for Major Polymer Producer (1.2 MB, PDF) |
| + | Cut Al-Pac Controller Variation in Half (PID) (5MB, PDF) |
| + | Easily Enable Both Open-Loop and Closed-Loop Testing Simultaneously (PID) (201 KB, PDF) |
| + | Ensure Control Loop Health for the Pulp and Paper Industry (PID) (1.1 MB, PDF) |
| + | Ensure Effective APC Implementation for BOC Gases (1242KB, PDF) |
| + | Improve pH Control in Zinc Production (1.2 MB, PDF) |
| + | Improved Controller Performance Yields Dividends for Oil & Gas Company (APC) (5MB, PDF) |
| + | Major Refinery Achieves Control Optimization Benefits with Matrikon Control Performance Monitor (PID) (1.1 MB, PDF) |
| + | Older Coal-Fired Plants Implements Matrikon Control Performance Monitor (PID) (433 KB, PDF) |
| + | Rio Tinto Iron Ore (Pilbara Iron) Centralized Monitoring (1.4 MB, PDF) |
| + | The Improvement of Naphtha Recovery at Suncor (PID) (4MB, PDF) |
| | Technical Paper | + View |
| + | A Condition Based Approach to Control Performance Analysis and Monitoring (674 KB, PDF) |
| + | A Picture Worth a Thousand Control Loops (685 KB, PDF) |
| + | A Practical Approach for Large-Scale Controller Performance Assessment, Diagnosis, and Improvement (933 KB, PDF) |
| + | Application of Control Loop Performance Assessment to an Industrial Acid Leaching Process (111 KB, PDF) |
| + | Applying Six Sigma (65 KB, PDF) |
| + | Challenges in the Detection, Diagnosis and Visualization of Controller Performance Data (469 KB, PDF) |
| + | Closed Loop Identification for Model Predictive Control: Application to Air Separation Process (605 KB, PDF) |
| + | Closed Loop Identification for Multivariable Model Predictive Controller on a Catalytic Gasoline Splitter (946 KB, PDF) |
| + | Closed Loop Identification of a FCC Unit (2.2 MB, PDF) |
| + | Closed-Loop Identification at the Hovensa Refinery (1.7 MB, PDF) |
| + | Control Loop Performance Assessment: An Enterprise Asset Management Solution (742KB, PDF) |
| + | Detection and Diagnosis of System Non-Linearities Using Higher Order Statistics (1.8 MB, PDF) |
| + | Detection of Distributed Oscillations and Root Cause Diagnosis (127 KB, PDF) |
| + | Increase MPC Project Efficiency by using a Modern Identification Method (3.6 KB, PDF) |
| + | Integrating Control Performance Monitoring and Plant Work Processes (321 KB, PDF) |
| + | Locating the Cause of Poor Control Performance, and Obtaining Operational Excellence (696 KB, PDF) |
| + | Multivariable and Closed-Loop Identification for Model Predictive Control (521 KB, PDF) |
| + | Multivariable Process Identification for MPC: The Asymptotic Method and its Applications (211 KB, PDF) |
| + | Multivariate Controller Performance Analysis: Methods, Applications and Challenges (383 KB, PDF) |
| + | Online Monitoring of Multivariable Control Utilization and Benefits (281 KB, PDF) |
| + | Performance Analysis of Model-based Predictive Controllers (223 KB, PDF) |
| + | Performance Evaluation of two Industrial MPC Controllers (446 KB, PDF) |
| + | Process Control: Potential Benefits and Wasted Opportunities (367 KB, PDF) |
| | Whitepaper | + View |
| + | A Loop in Time Saves Nine (548 KB, PDF) |
| + | An Experience with Mill Wide Process Control (1.5 MB, PDF) |
| + | Driving Pulp & Paper Performance through Variation Reduction: Part 1 of 2 - The Business Case (200 KB, PDF) |
| + | Driving Pulp & Paper Performance through Variation Reduction: Part 2 of 2 - Hunting Variation Where it Lives (109 KB, PDF) |
| + | Finding the Needle in the Haystack: Visualizing Control Performance Problems (1.4 MB, PDF) |
| + | Five Steps to Success with Matrikon Control Performance Monitor (86 KB, PDF) |
| + | Implementation and Performance Monitoring of Control Systems (727 KB, PDF) |
| + | Industrial application of Multivariable Controller Analysis and Monitoring Techniques (MPC) (559 KB, PDF) |
| + | Maintaining Your Advanced Control Applications: Idustrial Experiences and Best Practices (260 KB, PDF) |
| + | Matrikon™ Control Performance Monitor and Six Sigma (114KB, PDF) |
| + | The Control Performance Monitoring End Game (421 KB, PDF) |
| + | What is Condition-Based Maintenance? (91 KB, PDF) |
| | Webcast | + View | | |
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