MEng Chemical Engineering

Year of entry: 2023

Course unit details:
Chemical Engineering Optimisation

Course unit fact file
Unit code CHEN20051
Credit rating 10
Unit level Level 2
Teaching period(s) Semester 1
Offered by
Available as a free choice unit? No


Chapter 1: Introduction to Chemical Engineering Optimisation

  • Scope and hierarchy of engineering optimisation
  • Types of mathematical models in chemical engineering
  • Types of optimisation (programming) problems

Chapter 2: Construction of Mathematical Models

  • Formulation of general optimisation problems
  • Process models and constraints

Chapter 3: Fundamentals of Optimisation Theory

  • Degrees of freedom
  • Unimodality vs. Multimodality
  • Review of matrix algebra

Chapter 4: Convexity and Optimality

  • Convex functions and regions
  • Necessary & sufficient conditions for convexity
  • Necessary & sufficient conditions for an optimal solution

Chapter 5: Nonlinear Programming

  • Lagrange function for constrained optimisation
  • Necessary & sufficient conditions for constrained optimisation problems

Chapter 6: Nonlinear Programming Algorithms

  • General algorithms to solve an unconstrained optimisation problem
  • General algorithms to solve a constrained optimisation problem

Chapter 7: Linear Programming and Mixed-integer Programming

  • Introduction to linear programming
  • Graphical solution for two variable problems
  • Introduction to mixed-integer programming



The unit aims to:

This course introduces the main concepts of engineering optimisation theories (e.g. convexity, optimality) and general optimisation algorithms that are predominantly used in the chemical and biochemical industry. Its main aim is to equip the students with the essential mathematical skills for analysing, optimising, and designing (bio)chemical processes.

The course also provides a range of case studies during the class and coursework sessions to enable students to practise optimisation techniques and apply them to real chemical engineering problems. The students will learn about fundamental optimisation theories, how to formulate optimisation problems (both linear and nonlinear), select appropriate mathematical algorithms, implement curve fitting and data analysis, and calculate a high-quality numerical solution.

The course will also introduce use of advanced artificial intelligence techniques (e.g. machine learning, data-driven optimisation) for (bio)chemical process modelling and optimisation, and illustrate how these novel techniques can further improve process efficiency and sustainability


Learning outcomes

ILO 1: Demonstrate fundamental knowledge of optimisation theory.

ILO 2: Create and develop mathematical models for engineering optimisation problems.

ILO 3: Choose appropriate optimisation algorithms to calculate a high-quality optimal solution.

ILO 4: Extend knowledge to the concept of complexity and optimality.

ILO 5: Select classic methods to solve unconstrained and constrained optimisation problems

ILO 6: Describe the general procedure to solve linear programming, nonlinear programming, and mixed-integer programming problems.

ILO 7:Understand applications of modern artificial intelligence and machine learning techniques for chemical process modelling and optimisation.

Teaching and learning methods

Lectures provide fundamental aspects supporting the critical learning of the module and will be delivered as pre-recorded asynchronous short videos via our virtual learning environment.

Synchronous sessions will support the lecture material with Q&A and problem-solving sessions where you can apply the new concepts. Surgery hours are also available for drop-in support.

Feedback on problems and examples, feedback on coursework and exams, and model answers will also be provided through the virtual learning environment. A discussion board provides an opportunity to discuss topics related to the material presented in the module.

Students are expected to expand the concepts presented in the session and online by additional reading (suggested in the Online Reading List) in order to consolidate their learning process and further stimulate their interest to the module.

Study budget:

  • Core Learning Material (e.g. recorded lectures, problem solving sessions): 24 hours
  • Self-Guided Work (e.g. continuous assessment, extra problems, reading)     : 44 hours
  • Exam Style Assessment Revision and Preparation: 32 hours

Assessment methods

Assessment Types

Total Weighting

Continuous assessment


Exam style assessments


Please note that the exam style assessments weighting may be split over midterm and end of semester exams.

Recommended reading

Reading lists are accessible through the Blackboard system linked to the library catalogue.

Teaching staff

Staff member Role
Dongda Zhang Unit coordinator

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