Objectives and Syllabus

Introduces the methods and techniques of model building skills and the use of the SIMULINK package for dynamic modelling. Topics covered are - purposes, uses & benefits of system modelling: model development; empirical and first principles models, steady state and dynamic models, time domain solutions, model validation: modelling techniques; lumped parameter models, absolute & deviation variables, linearisation: models of process systems; hydrodynamic, multistage, reacting, multivariable, distributed parameter, discrete event: transfer function models; block diagram representation, modelling of control loop elements, integration of process & control models: simulation; continuous system simulation, selection of numerical integration routines, discrete event simulation, functional testing.

Practical work consists of exercises based on the use of the SIMULINK dynamic simulation package.

Module Details
Code: CME 8380 (formerly ACS 680)
Time Allocation: Lectures 40 hours
Assignments 40 hours
Private Study 70 hours
Prerequisites: Mathematics and Matlab (CME 8360)
Chemical Engineering Principles (CME 8362)
Weighting: 7.5 credits
Assessment: By report on assignment
By 1 x 2 hour examination
Advanced Process Automation


To develop an ability to build effective first principles dynamic models of items of process plant for analysis and control system design purposes.  The emphasis is on input-output relationships.  The intent is that it may be assumed, in other modules, that students understand how models of processes and plant are developed and so can concentrate on using them.


  • To introduce the methods and techniques of first principles modelling of plant and process dynamics. 
  • To develop a feel for the dynamics of a variety of process elements and systems. 
  • To understand the structure of plants and processes in terms of controlled, manipulated and disturbance variables. 
  • To appreciate the scope for making assumptions and approximations and to become aware of the limitations and usefulness of modelling. 
  • To further develop skills in the use of the Matlab and Simulink packages for dynamic simulation. 
  • To introduce dynamic modelling using packages such as Hysys.


Prerequisite to this module are the Mathematics and Matlab (CME 8360) and Chemical Engineering Principles (CME 8362) modules.
Note: modelling of multistage systems is covered in Dynamics & Control of Distillation Columns (CME 8384).

Study Modes

This module is of one week's full-time intensive study consisting of a variety of lectures, informal tutorials for problem solving and computer based lab sessions.  It is followed by an assignment to be carried out in the student’s own time.


The time allocation for practical work provides for simulation exercises making use of the Matlab and Simulink packages.  The exercises are structured to reinforce the material covered in the lectures and tutorials.

Recommended Texts

  • Dabney J B & Harman T L,  The Student Edition of Simulink (Version 2) User’s Guide,  Prentice Hall,  1998. 
  • Hanselman D & Littlefield B,  The Student Edition of Matlab (Version 5) User’s Guide,  Prentice Hall,  1997. 
  • Love J,  Process Automation Handbook,  Springer,  2007
  • Roffel B & Betlem B,  Process Dynamics and Control,  John Wiley & Sons,  2006. 
  • Seborg D, Edgar T, Mellichamp, D and Doyle F,  Process Dynamics and Control,  3rd Edition,  Wiley,  2011. 
  • Wilkie, J, Johnson M and Katebi R,  Control Engineering: An Introductory Course,  McMillan (Palgrave),  2002.

Topics Included

Introduction:  Purpose, uses and benefits of system modelling.  Use of models for design, real time training and optimisation.  Types of model: first principles versus stochastic, heuristic, empirical, etc.  Physical equations of systems: algebraic and differential.  Constraint equations: equality and inequality.  Time domain solutions: steady state and dynamic.

Modelling techniques:  Formation of lumped parameter models.  Classical assumptions.  Accumulation equals input minus output.  Analogies with electrical and mechanical systems.  Significance of capacity for energy storage.  Absolute and deviation variables.  Linearisation.  Scope for approximation.  Laplace transformations.  Conversion into transfer function models.  Translation into block diagrams.

System models:  Modelling of control loop elements.  Integration of process and control models.  System block diagrams.  Validation of models.  Zero capacity systems.  Hydrodynamic and electromechanical models.  Models of reacting systems.  Multiloop systems.  State space modelling of multivariable systems.  Models of distributed parameter systems.  Discrete event modelling.

Process models:  Dynamic models of a variety items of plant: eg stirred tanks, jacketed vessels, pressure systems, heat exchangers, packed columns, etc.  Models of a variety of operations: eg mixing, heating, blending, pumping, reaction, distillation, etc.

Simulation:  Use of continuous simulation languages.  Simulation of linear & non-linear dynamic systems.  Selection of numerical integration routines.  Choice of step length & run time.  Setting up initial and boundary conditions.  Applying forcing functions and disturbances.  Use of discrete event simulation languages (eg Stateflow).  Documentation & flow charts.  Interpretation of error messages & debugging.  Functional testing and validation.