COMPUTATION FOR CHEMICAL ENGINEERS - 2026/7

Module Overview

There is an increasing demand in the chemical and process engineering industry for computational tools to collect and analyse data, design, simulate and automate processes. To keep up with the development and implementation of new technological tools, the content new chemical engineers are taught needs to keep up with this constant change. This module builds on the modules ENG1084 Mathematics 1, ENG1085 Mathematics 2 and ENG1083 Transferable Skills and Laboratory Skills from FHEQ level 4 and contextualises this content with a focus on building the computational skills and digital capabilities for the modern chemical engineer. This, in turn, should lead to the students being able to correctly identify the computational tools required for a given problem, and allow them to use computational tools to model and simulate chemical engineering processes as well as solve process optimisation problems.

Module provider

Chemistry and Chemical Engineering

Module Leader

KANNUCHAMY Vasanth Kumar (Chst Chm Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

Module content

Indicative content includes:

Application of Numerical Methods

1. Roots of nonlinear equations

2. Solution of systems of linear and non-linear equations

3. Solution of ordinary differential equations 4. Numerical Integration

Optimisation

1. Fundamentals of optimisation

2. Unconstrained optimisation

3. Constrained optimisation

Machine Learning and Data Analysis

1. Big data and dsata-driven approaches in chemical engineering

2. Programming for data analysis using a programming language

3. Data manipulation and visualisation techniques

4. Supervised learning algorithms

5. Unsupervised learning algorithms

6. Application of machine learning to chemical engineering datasets

Introduction to process modelling and simulation

1. First-principles modelling

2. Applying numerical methods to realistic chemical engineering problems

3. Combining process modelling with parameter estimation and optimisation

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of computational methods and their ability to apply these methods to solve chemical engineering problems. Assessment focuses on students ability to select appropriate numerical, modelling, machine learning, and optimisation techniques and apply them using computational tools. The summative assessment for this module consists of a computer-based class test conducted during a laboratory session (35%), which assesses students ability to write code, implement computational methods, and select appropriate approaches to solve chemical engineering problems. In addition, a 2-hour invigilated final examination (65%) assesses students understanding of the underlying concepts, numerical methods, process modelling approaches, machine learning techniques, and optimisation methods introduced in the module. Together, these assessments evaluate students conceptual understanding, computational implementation skills, and their ability to apply appropriate techniques to engineering problems.

Module aims

• To provide students with a systematic introduction to computational approaches for analysing and solving chemical engineering problems. The module develops skills in programming, data analysis, machine learning, process modelling, and optimisation, enabling students to apply numerical and computational methods to engineering systems. Through these topics, the module introduces the role of digital and data-driven approaches in modern chemical engineering and prepares students to analyse complex engineering problems using computational tools.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to: Cover the necessary fundamental knowledge for the subject during lectures, whilst allowing the students to use the computer labs and tutorials to apply the knowledge covered in lectures. The weekly reflective report is designed to keep the student engaged with the module throughout the semester whilst letting them identify any problem areas they have with the module content and allowing them ample time to rectify them.

The learning and teaching methods include:

Lectures  2 hours per week for 11 weeks (average)

Tutorials 1 hour per week for 11 weeks (average)

Computer Labs  2 hours per week for 11 weeks (average) 

Independent Learning  6 hours per week for 12 weeks (average)

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENG2127

Other information

Employability: This module provides students with practical knowledge of the role that computational tools play in modern chemical engineering. By developing programming skills and learning how to solve open-ended engineering problems using computational methods, students strengthen their ability to analyse complex systems and make data-driven decisions. The ability to apply advanced mathematical concepts using computational tools and to select appropriate methods for solving engineering problems is an important professional skill in modern industry. In addition, the introduction to machine learning and artificial intelligence techniques provides students with exposure to rapidly growing areas in engineering and technology. These skills are increasingly sought after across many sectors, including process industries, digital manufacturing, data-driven process optimisation, and AI-enabled engineering systems, thereby expanding students¿ future employment opportunities. 

Digital Capabilities: This module develops students¿ digital capabilities by introducing computational and data-driven approaches used in modern chemical engineering and Industry 4.0 environments. Students will learn how to use programming tools to analyse engineering data, implement numerical methods, develop process models, and apply optimisation and machine learning algorithms. Through coding exercises and computational problem-solving activities, students gain experience in selecting appropriate digital tools, implementing algorithms, and interpreting results from large datasets and dynamic process models. These skills help prepare students to work in increasingly digitalised chemical and process industries where data analysis, modelling, and computational decision-making play an important role in process design and operation. 

Sustainability: This module highlights how computational tools can contribute to the development of more sustainable chemical and process industries. Through process modelling, optimisation methods (including linear programming), and machine learning techniques, students learn how engineering systems can be analysed and improved to enhance efficiency, reduce energy consumption, and minimise emissions. Computational modelling enables engineers to explore different operating conditions and design choices without extensive physical experimentation, supporting more efficient use of resources. Optimisation approaches help identify operating strategies that maximise performance while minimising environmental impact. These approaches contribute to global sustainability goals, particularly SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), by supporting more efficient, data-driven, and environmentally responsible industrial processes. 

Resilience and Resourcefulness: This module develops students¿ resilience and resourcefulness through computational problem solving and programming-based activities. Students will learn to implement numerical methods, machine learning techniques, process models, and optimisation algorithms to solve realistic chemical engineering problems. Through coding, testing, and refining their computational solutions, students develop the ability to troubleshoot errors, evaluate alternative approaches, and improve their solutions iteratively. This process encourages independent thinking, persistence, and adaptability when tackling complex engineering challenges. 

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.