This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

CHE6884 - Process modeling and optimisation

0 points, SCA Band 2, 0.000 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Engineering

Organisational Unit

Department of Chemical Engineering

Coordinator(s)

Associate Professor Andrew Hoadley

Offered

Clayton

First semester 2016 (Day)

Notes

This unit is available only to Engineering PhD students.

Synopsis

The unit will cover the purpose and methods of modelling chemical and biochemical processes. It includes the development of constitutive relations, model building, evaluation and sensitivity analysis. Numerical techniques will include the solution of systems of linear, non-linear and algebraic equations. Models are subjected to optimisation.

The basic principles of optimisation including the types of variables, linear and non-linear models, constraints and objective functions will be covered. Various optimisation algorithms for linear, non-linear problems and mixed integer problems are presented in the context of chemical process design. Multi-objective optimisation is used to explore trade-offs involved with sustainable process development.

Outcomes

On successful completion of this unit, students will be able to:

build models of chemical and biological processes which respect conservation laws, apply suitable constraints and constitutive relations and choose an appropriate solution algorithm
analyse complex models of chemical processes with an understanding of the mathematical structure of the model and the convergence methods used to obtain the model solution
apply the appropriate optimisation strategy for linear, non-linear, unconstrained, constrained and mixed integer models from a fundamental understanding of functional and constraint convexity, or can choose the appropriate evolutionary solution strategy when convexity is not assured
optimise both single objective and multi-objective process models to improve the process objective(s).

Assessment

Continuous assessment: 40%
Final examination (3 hours): 60%

Students are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit.

Workload requirements

3 hours lectures, 3 hours tutorial and 6 hours of private study per week.

Chief examiner(s)

Professor Karen Hapgood