2024/25 Undergraduate Module Catalogue

CAPE3321 Process Optimisation and Control

20 Credits Class Size: 150

Module manager: Dr BH Xu
Email: b.h.xu@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2024/25

This module is not approved as a discovery module

Objectives

- Introduce students to the basic principles and practical methodologies for process integration, optimisation and control.
- Train students the skills to apply the principles and methodologies to practical applications.
- Introduce students to the concepts necessary to analyse industrial data with the objective of process characterisation and understanding.
- Train students to develop robust linear process models with application for process control and optimisation.
- Prepare students with the working knowledge for the application of the methodologies to industrial problems.

Learning outcomes

On completion of this module, students should have a working knowledge of:
- Pinch analysis and process integration and proven techniques in deterministic optimisation algorithms for both linear and nonlinear problems.
- Process control objectives and theory; control system components; control strategies for unit operations; and how to construct P&ID diagrams.
- Methodologies for the pre-processing of industrial data, application of multivariate statistical techniques for enhanced process understanding and linear process data modelling.

Skills outcomes

On completion of this module, students should have the following skills:
- Be able to set energy targets for simple processes based on thermodynamic principles.
- Formulate and solve both linear and non-linear basic optimisation problems using deterministic optimisation algorithms.
- Develop control strategies for a given process, read and draw P&ID diagrams, being able to analyse system dynamics and develop appropriate control strategies.
- Analyse industrial data with the goal of developing robust models for process control and optimisation.

Syllabus

Process Integration and Optimisation:
- Pinch analysis and process integration: minimum utility loads and pinch temperature, estimations of heat exchanger surface areas and number of heat-exchangers, loops and paths used to restore the minimum approach temperature.
- Optimisation theory and methods: classic theory for unconstrained extremum, Lagrange multiplier methods, linear programming and its applications, the Simplex algorithm, Integer programming, dynamic programming.

Process Understanding and Control:
- Signal and Block Diagrams, PID controllers, controller tuning.
- Control strategy: Formulation of P&I diagrams.
- Types of process control systems and components.
- Control schemes for a variety of items of plant.
- Pre-processing of industrial data.
- Enhanced process understanding through multivariate statistical techniques including principal component analysis.
- Linear modelling techniques including multiple linear regression and projection to latent structures.

Teaching Methods

Delivery type Number Length hours Student hours
Class tests, exams and assessment 1 2 2
Lecture 22 1 22
Practical 6 1 6
Tutorial 5 1 5
Private study hours 165
Total Contact hours 35
Total hours (100hr per 10 credits) 200

Private study

The students are expected to read the recommended textbooks together with the handouts for developing an understanding of the topics covered in the formal classes. They should look at the worked out example problems in textbooks to learn how theory can be applied to solve practical problems. To develop problem solving skills they should independently try to solve the problems given in the Problem Sheets provided by the lecturers and also textbook exercise problems prior to the tutorial class.

Opportunities for Formative Feedback

Students' progress will be monitored via:
- feedback on coursework.

Methods of Assessment

Coursework
Assessment type Notes % of formal assessment
Assignment Optimisation: Pinch Analysis & Process Integration 20
Assignment Enhanced Process Understanding: Data analysis and principal component analysis 25
Assignment Process Data Modelling: Application of linear modelling techniques to industrial data 25
Total percentage (Assessment Coursework) 70

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams
Exam type Exam duration % of formal assessment
Standard exam (closed essays, MCQs etc) 1.0 Hrs Mins 30
Total percentage (Assessment Exams) 30

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading List

The reading list is available from the Library website

Last updated: 4/29/2024

Errors, omissions, failed links etc should be notified to the Catalogue Team