Model Predictive Control
Adaptive MPC Control of a Nonlinear Chemical Reactor:
- Using Online Model Estimation - Example
- Using Successive Linearization - Example
- Using Linear Parameter Varying System - Example
System Identification
Design a PID controller for a model that cannot be linearized. Use system identification to identify a plant model from simulation input-output data.
- Fault Detection Using Data Based Models - Example
- Fault Detection Using an Extended Kalman Filter - Example
- Condition Monitoring and Prognostics Using Vibration Signals - Example
- System Identification | Data-Driven Control (4:12) - Video
- Estimating Transfer Function Models for a Heat Exchanger - Example
- Nonlinear State Estimation Using an Unscented Kalman Filter - Example
- Fault Detection and Diagnosis in Chemical and Petrochemical Processes (3 videos) - Video Series
Process Control
Decouple the two main feedback loops in a distillation column.
- Process Control with Reinforcement Learning (15:34) - Video
- Simulink CSTR Simulation and Control - File Exchange
- Gain-Scheduled Control of a Chemical Reactor - Example
- Optimize Multi-Loop Controller Parameters of a Distillation Column Producing Methanol - Example
- Independently Control Feedback Loops in a Distillation Column - Example
- Temperature Control in a Heat Exchanger - Example
- Distillation Controller Tuning - Example
Other Resources
Linearize a pulp and paper process model at steady state operating point.
- Optimize the Dimensions of a CSTR to Minimize Cost and Variation in Concentration with Changing Feed Stock - Example
- Chemical Kinetics with MATLAB - File Exchange
- Regression Strategies for Large Datasets - Article
- MATLAB for Chemical Reaction Engineering - Community
- Cybersecurity of Chemical Plants - Community
- Optimizing Scheduling and Blending Operations in the Process Industries (32:00) - Video
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