MEng Chemical Engineering

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.

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.

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.

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

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

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

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