Module code: ENG2127

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

Overall student workload

Independent Learning Hours: 73

Lecture Hours: 22

Tutorial Hours: 11

Laboratory Hours: 22

Guided Learning: 11

Captured Content: 11

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative content includes:

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



1. Fundamentals of optimisation

2. Unconstrained optimisation

3. Constrained optimisation


1. Data Manipulation

2. Expanding on built-in Functions

3. Plotting

4. Regression essentials

5. Parameter estimation

6. Introduction to Artificial Intelligence and data-driven modelling


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

Assessment type Unit of assessment Weighting
Coursework Weekly reflective short reports on module progress building up to a full portfolio 20
Coursework Coursework on data-based process modelling 20
Coursework Coursework on first principles process modelling, model identification and optimisation 20
Examination 2hr Invigilated Exam 40

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate the full range of learning outcomes, though coursework and reflective reports, in the application and use of computational skills and numerical methods in solving engineering problems. The summative assessment for this module consists of: 3 x 20% (LO1-LO8) Coursework split across three distinct assessments and an invigilated 120 min Exam.

Formative assessment None

Feedback Written feedback on the weekly reflective portfolio pieces (LO1- LO8) Verbal feedback during computer labs and tutorials (LO1-LO5) Written and verbal feedback on the coursework (LO1-LO4)

Module aims

  • Provide a systematic introduction to the concepts, principles and methods of computational approaches to solving mathematical problems present in engineering processes. This will include providing students with the knowledge of using standard numerical methods for solving engineering problems, in addition to introductions to process optimisation and statistical methods underpinning machine learning and artificial intelligence.

Learning outcomes

Attributes Developed
001 Use a range of standard numerical methods to solve complex engineering problems CKP
002 Use Microsoft Excel as both a data manipulation as well as data visualisation package CPT
003 Know about and perform several types of regression both in MS Excel and a programming language CPT
004 Use a programming language to perform basic process simulation, optimisation, statistical analysis and parameter estimation CP
005 Describe basic machine learning and artificial intelligence approaches to solving engineering problems KT
006 Select appropriate computational tools and methods to a proposed engineering problem KPT
007 Self-assess their own progress in learning, through the weekly reflective report T
008 Being able to articulate why computer simulation and modelling is important in the chemical engineering industry CK

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

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
Upon accessing the reading list, please search for the module using the module code: ENG2127

Other information

Employability: This module provides students with working knowledge of the role that computers play in modern engineering. Through students developing programming skills as well as the ability to work through open-ended coursework assignments, this increases their employability. Being able to comfortably use more advanced mathematics within computers and to identify which techniques and tools to use for specific problems will make the students better professionals. Additionally, the introduction to statistical modelling and machine learning as a trending new profession opens new opportunities for future employment.

Digital Capabilities: This module focuses heavily on enhancing and empowering our students with more advanced digital capabilities. Assessments are focused on applying mathematical concepts through computation and programming skills development. Knowing which tool to use, how these tools work at a fundamental level, and knowing how to build custom tools are all included within the module.

As with all modules, students are also expected to engage with online material and resources  via  SurreyLearn,  and  other  digital  platforms.

Sustainability: the module discusses the how to employ computational tools to optimise processes to obtain lower emissions, increased efficiency, and how to develop new routes for sustainable chemical industries.

Resilience and Resourcefulness: By setting open-ended projects, and encouraging students to learn programming through real-world problems and trail-and-error, the module increases students’ ability to be more resourceful and resilient.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Chemical and Petroleum Engineering BEng (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Chemical and Petroleum Engineering MEng 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Chemical Engineering BEng (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Chemical Engineering MEng 1 Compulsory A weighted aggregate mark of 40% is required to pass the module

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 2023/4 academic year.