Computer Process System Engineering: Courses

高等程序控制
- (Fall 2024) Class Meeting Times & Locations:
  - TUE (14:00~16:00) 工419
  - THU (14:00~16:00) 工419
- Goal: In modern industries, there are many closed-loop, large-scale control systems that are highly complex and full of a large amount of measured variables. The improvement of the process operation is an important duty of process engineers. Process control can help process engineers to fulfill this duty. Since hardware and software platforms are becoming increasingly powerful, many industrial automation systems today can meet process control needs of industries. The purpose of this course is to provide an advanced treatment of the theory and practice of chemical process modeling and control, for advanced undergraduate and graduate students. An emphasis of this course is on model-based control system design and implementation.
- Prerequisites: It is assumed that the students have had an introductory course in process control. The course is open to all the post-graduate students. Some mathematical background and engineering knowledge are necessary. Also, the course requires only minimal computer background and Python programming experience although it certainly is advantageous to have prior exposure--not strictly required. The Python-based platform will be used for dynamic process simulation and control system development. Most homework assignments will require the use of Python and its relevant packages..
- Course Structure
  - (2024)
    - Introduction: from Simple Feedback Control to Advanced Process Control
  - (2024)
    - Review of Simple Feedback Control
    - Feedback Control: Advantages and Disadvantages
    - Dynamic Molding Simulation in Python
  - (2024)
    - Dynamic Model Simulation
    - ODEINT in Python
  - (2024)
    - Balance Equations
      
      Mass
      
      Momentum
      
      Energy
      
      Species
  - (2024)
    - Linearization of Nonlinear Dynamic Model
  - (2024)
    - Algebraic Equations --> Steady-State Conditions
  - (2024)
    - Sensor/Transmitter
    - Control Valve
  - (2024)
    - FOPDT
    - Fitting FOPDT
  - (2024)
    - SOPDT
    - Fitting SOPDT
  - (2024)
    - PID Controller
    - Integrating vs. Non-Integrating Process
    - Controller Performance
    - IMC
    - Anti-Reset Windup
    - Controller Stability Analysis
    - Root Locus
    - Bode Plot
  - (2024)
    - Cascade Control
  - (2024)
    - Feedforward Control
      
      Concept
      
      Linear Feedforward Control
      
      Lead-Lag Element
    - Design of Nonlinear Feedforward Control from Basic Process Principles
    - Comments on Feedforward Control
  - (2024)
    - Multiloop and Multivariable Control
  - (2024)
    - Overview of Model Predictive Control (MPC)
    - MPC (SISO)
      
      Impulse and Step Response Models
      
      Prediction Using Step Response Models
    - Model Predictive Control (Linear & Nonlinear)
  - (2024-)
  - - Adaptive Control
  - (2024-1005, 2020-1012, 2020-1019, 2020-1026)
    - Filtering
  - (2024-1026, 2020-1109, 2020-1116, 2020-1123, 2020-1130)
    - Assignment #3: Multiloop Control and MPC (due: 2020-12-7)
  - System Identification
    - Linear & Nonlinear
    - Parametric & Non-Parametric
    - Model Decomposition
  - (2024-1130, 2020-1107)
    - Real-Time Optimization
    - Assignment #4: Real-Time Optimization (due: 2020-12-11)
    - Process Monitoring (Stochastic Control)
  - (2024-1207, 2020-1214)
    - Batch Process Control
- Assignments
  - (Due -11-18-2024) Assignment #1: Design of Feedback Control Loops
  - (Due ??-2024) Assignment #2: Feedforward Control, and Cascade Control
- Grading Distribution
  - No quiz will be given. The overall performance is calculated based on absolute achievement (assignments) and relative achievement (performance in the class).
  - Assignments (4 to 6 times): 80%
  - Participation (class, Q&A): 20%