Introduction

Model Predictive Control (MPC) is an advanced control strategy used in industries to manage complex systems with constraints, multivariable interactions, and varying process conditions. MPC allows controllers to predict future process behavior and optimize control actions to achieve desired objectives while handling process constraints in real-time. This course covers the theory behind MPC, its practical application, and how to implement it for various industrial processes. Participants will gain hands-on experience with MPC implementation and optimization through simulations and real-world case studies.

Course Objectives

By the end of this course, participants will be able to:

• Understand the theory and key concepts behind Model Predictive Control (MPC).
• Develop process models for use in MPC, including linear and nonlinear models.
• Implement and tune an MPC controller to control industrial processes with constraints.
• Use optimization algorithms in MPC to manage multi-variable control systems.
• Apply MPC to real-world processes, such as chemical, petrochemical, and manufacturing systems.
• Analyze and troubleshoot MPC systems to ensure stable and optimal performance.
• Understand the limitations and advantages of MPC compared to traditional control methods.

Who Should Attend?

This course is ideal for:

• Process control engineers, instrumentation engineers, and automation engineers
• Control system engineers working with advanced control strategies
• Operations managers and engineers in industries such as chemical, oil & gas, power, and manufacturing
• Anyone interested in learning how to design and implement MPC systems
• Engineers and technical staff involved in optimizing and maintaining industrial control systems

5-Day Training Course Outline

Day 1: Introduction to Model Predictive Control (MPC)

• Overview of Model Predictive Control: Definition and Key Concepts
• Benefits of MPC over Traditional Control Methods (PID, etc.)
• Process Dynamics and Model Building for MPC: Linear vs. Nonlinear Models
• Control Horizon, Prediction Horizon, and Sampling Time
• The Optimization Problem in MPC: Cost Function, Constraints, and Objective Functions
• Types of MPC: Model-based MPC, Nonlinear MPC, and Robust MPC
• Hands-on: Introduction to MPC Software (MATLAB/Simulink, etc.) and Simple Model Implementation

Day 2: Process Modeling for MPC

• Building Process Models for MPC: Linear Models, Transfer Functions, and State-Space Models
• Identifying Model Parameters from Process Data: Step Tests, Frequency Response, and System Identification
• Handling Nonlinearities: Piecewise Linear Models, Polynomial Approximations
• Dynamic Simulation of Processes: Using MATLAB/Simulink for Modeling Process Behavior
• Model Validation: Verifying Model Accuracy and Performance in Simulation
• Hands-on: Developing a Simple Process Model (e.g., Heat Exchanger or Reactor) for MPC

Day 3: Implementing MPC in Real-Time Control Systems

• Formulation of the MPC Problem: Predicting Future Behavior, Controlling Variables, and Constraints
• Controller Design: Setting the Control Horizon, Objective Function, and Constraints
• Tuning MPC Controllers: Adjusting Weights and Cost Function for Optimal Performance
• Constraints Handling in MPC: Hard vs. Soft Constraints, and Multi-Variable Constraints
• MPC for Multivariable Control: Handling Interactions Between Multiple Process Variables
• Case Study: Applying MPC to a Simple Industrial Process (e.g., Temperature Control in a Furnace)
• Hands-on: Implementing MPC with Constraints in a Simulated Environment

Day 4: Advanced MPC Concepts and Optimization

• Advanced Optimization Algorithms for MPC: Quadratic Programming (QP), Sequential Quadratic Programming (SQP)
• Adaptive and Robust MPC: Handling Process Disturbances, Model Uncertainty, and Parameter Variations
• Optimization in Real-Time: Solving the MPC Optimization Problem at Each Time Step
• Large-Scale Systems: MPC for Large-Scale Processes with Multiple Inputs and Outputs
• Model Predictive Control vs. Traditional Control: Strengths, Weaknesses, and Practical Considerations
• Case Study: Using MPC for Energy Optimization in a Chemical Process
• Hands-on: Designing and Implementing Robust MPC for a Multivariable Process

Day 5: Practical Applications, Troubleshooting, and Final Project

• Practical Applications of MPC: Use Cases in the Chemical, Petrochemical, Power, and Food Industries
• Integrating MPC with Other Control Strategies: Combining MPC with PID, Cascade, or Feedforward Control
• Troubleshooting MPC Systems: Common Issues in Tuning, Model Identification, and Optimization
• Performance Monitoring and Control: Key Performance Indicators (KPIs) and System Evaluation
• Real-Time Implementation Challenges: Latency, Computational Requirements, and Real-World Constraints
• Final Project: Group Work on Implementing an MPC Solution for a Simulated Industrial Process (e.g., Distillation Column or Furnace)
• Group Presentations: Present MPC Design, Tuning Results, and Optimization Strategies
• Final Assessment, Q&A, and Certification