CHE692 – PROCESS MODELLING AND SIMULATION

CHAPTER 1 - INTRODUCTION

1.1 Uses of Mathematical Models

• Mathematical models are useful in chemical engineering, – research and development– designing to plant operations, and – even in business in economic studies

Research and development

• Determining chemical kinetics mechanisms and parameters from laboratory or pilot plant reaction data.

• Exploring the effects of different operating conditions for optimization and control studies.

• Aiding in scale-up calculations.

Design

• Exploration the sizing and arrangement of processing equipment for dynamics performance.

• The study of interactions of various parts of the process, particularly when the materials recycle or heat integration is used.

• Evaluation of alternative process and control structures and strategies.

• Simulating start-up, shutdown and emergy situation and procedures.

Plant operation

• Troubleshooting control and processing problems.

• Aiding in start-up and operator training.

• Studying the effects of and the requirements for expansion of projects

• Optimizing plant operation

Conclusion

• It is usually cheaper, safer and faster to conduct the studies on a mathematical model rather than experimentally on an operating unit.

1.2 Principles of Formulation

• Basis• The bases for mathematical models are the fundamental

physics and chemical laws such as laws of conservation of mass, energy and momentum.

• Assumptions– Types of valid assumptions that can be made of a process –

difficult task.– Too details – results in a tedious model and take a long time

to solve.– An engineering compromise between a rigorous description

and getting an answer that is good enough is always required.

• Mathematical consistency of Model– Number of variables equals the number of

equations– DOF must be zero to obtain a solution– Checking the units of all terms in all equations

are consistent is another trivial step.

• Solution of the model equations– Available solution techniques and tools must

be kept in mind as a mathematical model is developed. Either determined experimentally or a solution algorithm

• Verification– Important part but often being neglected:

proving the model describes the real world.– Design stage – this cannot be done but

usually data from pilot plant or similar plant study could be used.