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Engineering
Chemical Engineering
Process Control and Optimization
1. Introduction to Process Control
2. Mathematical Modeling of Chemical Processes
3. Feedback Control Systems
4. Stability of Closed-Loop Systems
5. Advanced Control Strategies
6. Model Predictive Control (MPC)
7. Process Optimization
8. Real-Time Optimization (RTO)
9. Practical Implementation and Applications
6.
Model Predictive Control (MPC)
6.1.
Core Concepts of MPC
6.1.1.
Prediction Model
6.1.1.1.
Model Types Used in MPC
6.1.1.2.
Step Response Models
6.1.1.3.
State-Space Models
6.1.1.4.
Model Identification
6.1.2.
Objective Function
6.1.2.1.
Setpoint Tracking
6.1.2.2.
Control Effort Minimization
6.1.2.3.
Constraint Violations
6.1.3.
Receding Horizon Control
6.1.3.1.
Prediction Horizon
6.1.3.2.
Control Horizon
6.1.3.3.
Implementation Strategy
6.2.
Formulation of the MPC Problem
6.2.1.
Mathematical Formulation
6.2.2.
Quadratic Programming Formulation
6.2.3.
Constraints Handling
6.2.3.1.
Input Constraints
6.2.3.2.
Output Constraints
6.2.3.3.
Rate of Change Constraints
6.2.4.
Optimization Algorithms in MPC
6.3.
Types of MPC
6.3.1.
Linear MPC
6.3.2.
Nonlinear MPC
6.3.3.
Robust MPC
6.3.4.
Adaptive MPC
6.4.
Tuning MPC Controllers
6.4.1.
Selection of Horizons
6.4.2.
Weighting Factors
6.4.3.
Constraint Tuning
6.4.4.
Model Update Strategies
6.5.
Advantages and Disadvantages of MPC
6.5.1.
Benefits in Industrial Applications
6.5.2.
Limitations and Challenges
6.5.3.
Computational Requirements
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7. Process Optimization