COMPUTATION FOR CHEMICAL ENGINEERS - 2022/3

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 specific to chemical engineering. 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

SHORT Michael (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

Completion of the progression requirements to FHEQ Level 5 of the degree courses in Chemical Engineering, Chemical and Bio-Systems Engineering and Chemical and Petroleum Engineering, or equivalent.

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

 

Optimisation

1. Fundamentals of optimisation

2. Unconstrained optimisation

3. Constrained optimisation

Statistics

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

Alternative Assessment

Weekly reflective short reports will be replaced with a reflective report on the module progress

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

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  8 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

None

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 2022/3 academic year.