MODULE DETAILS

 

Module 11: Practical Advanced Process Control for

Engineers and Technicians

 

Nominal duration: 3 weeks (36 hours total time commitment)

 

This time commitment includes the preparation reading, attendance at each webinar (1 hour plus 15-30 minutes for discussion), and the time necessary to complete the assignments and further study.

 

MODULE PURPOSE

 

This module covers the concepts of Advanced Process

Control (APC) as applied in Industrial plants.

 

PRE-REQUISITES MODULE, UNITS /

 

CO-REQUISITES

 

Module 10

 

ASSESSMENT STRATEGY

 

To evaluate the achievement of the learning outcomes; written assignments, group projects and practical exercises are set.

 

SUMMARY OF LEARNING OUTCOMES

 

1. Examine Internal Model Control (IMC)

 

2. Examine Model Predictive Control (MPC)

 

3. Outline the use of Reference Models

 

4. Formulate the control problem

 

5. Examine the process of MPC steady state optimization

 

Learning Outcome 1

 

Examine Internal Model Control (IMC)

 

Assessment Criteria

 

1.1

 

Compare the differences between IMC and classical control

 

 

1.2

 

Illustrate how IMC deals with disturbance rejection and control

 

 

1.3

 

Outline the concepts of IMC delays and feedforward

 

 

 

Learning Outcome 2

 

Examine Model Predictive Control (MPC)

 

Assessment Criteria

 

2.1

 

Examine the basic concepts of MPC

 

 

2.2

 

Outline the concepts of: (a) State space

(b) Transfer function

 

(c) Impulse response representations

 

 

2.3

 

Examine MPC models in terms of:

 

(a) The ‘what’ and the ‘how’ of the model

 

(b) Black vs. grey box models

 

(c) Causality graphs

 

 

2.4

 

Examine MPC observers in terms of: (a) Overall formulation

(b) Purpose

 

(c) The Kalman algorithm

 

 

2.5

 

Examine MPC control in terms of: (a) overall formulation

(b) Constraints

 

(c) Horizon

 

Learning Outcome 3

 

Outline the use of Reference Models

 

Assessment Criteria

 

3.1

 

Outline the handling of setpoints on controlled variables

 

 

3.2

 

Outline the methods of rejecting measured as well as unmeasured disturbances

 

 

3.3

 

Examine the handling of soft constraints on controlled variables

 

 

 

Learning Outcome 4

 

Formulate the control problem

 

Assessment Criteria

 

4.1

 

Outline control problem formulation, with reference to:

 

(a)  Quadratic Criterion vs. Geometric Control

 

(b)  Horizon length

 

(c)  Weight matrix

 

(d)  Output constraints

 

(e)  Projection of measured and unmeasured disturbances along the horizon

 

 

4.2

 

Formulate and resolve the Final Quadratic problem

 

 

4.3

 

Perform:

 

(a) Off-line pre-processing

 

(b) On-line calculations

 

Learning Outcome 5

 

Examine the process of MPC steady state optimization

 

Assessment Criteria

 

5.1

 

Outline the process of MPC steady-state optimization, in terms of:

 

(a) Degrees of Freedom and rationale for optimization

(b) Submission of economic output to the setpoint

(c)  Slogans for maximization and minimization

(d) The bridge from optimization to control

(e) Identification of reachable targets for economic variables

(f)  The horizon for economic variables

(g) How to change the control formulation problem

 

Delivery mode

 

A combination of asynchronous and synchronous e-learning delivery comprising a judicious mix of interactive online web conferencing, simulation (virtual labs) software, remote online labs, online videos, PowerPoint slides, notes, reading and study materials (in PDF, HTML and Word format) accessed through the Moodle Learning Management System (LMS).

 

Software/Hardware Used

Software

  • N/A

Hardware

  •  N/A