Module Catalogue 2025/26

Pre Requisite Comment

Basic knowledge of statistics from A-level mathematics or equivalent

Co Requisite Comment

N/A

Aims

This module aims to provide a practical introduction to a variety of data analysis techniques that can be used for modelling and analysis of large datasets, aka “big data”, typically encountered in the process industries.

Outline Of Syllabus

Key themes for the module: Multivariate Data Analysis: Introduction: What problems can be addressed using these techniques; Preliminary Data Analysis – Handling of Inhomogeneous Data (Missing Data; Outliers; Noisy Data; Time Alignment); Graphical Procedures. Dimensionality Reduction (Principal Component Analysis); Modelling techniques: Multiple linear regression, Principal component regression; Projection to Latent Structures. Multivariate Statistical Performance Monitoring – Continuous and Batch Processes. Model simplification. Analysis of Variance. Confidence Intervals. Non-linear modelling techniques. Machine Learning techniques.

Intended Knowledge Outcomes

To develop an awareness of the advantages and disadvantages of the different methodologies (data pre-screening, feature extraction and process modelling) presented for the analysis of industrial data (M1).

To develop the knowledge of the students, through their exposure to a raft of methodologies (data pre-screening, feature extraction and process modelling) that are applicable both in the laboratory and the production plant, thereby enabling them to judge appropriate techniques to use to reach substantiated conclusions which could lead to the delivery of enhanced process performance, process understanding and/or process optimisation (M2).

To develop the critical ability of the students enabling them to select and apply the most appropriate methodologies for the problem to be addressed (data pre-screening, feature extraction and process modelling) (M3).

Intended Skill Outcomes

The ability to utilise the statistical techniques that form the basis of multivariate methods (M12) and to select appropriate methods for a given problem recognising their limitations (M13).

The ability to interrogate the results from the execution of a multivariate data analysis in the context of the problem being addressed, e.g. to realise an enhanced understanding of process operation, and to evaluate the effectiveness of the methods used to model the problem (M17).

Teaching Rationale And Relationship

Lectures convey the statistical concepts and theory and their application in process engineering. Hands on practical sessions support the learning introduced in lectures through the students having the opportunity to apply the concepts to a number of problems varying in terms of complexity. The practical workshop sessions allow the completion of some of the assignment work.

Assessment Rationale And Relationship

Assignments allow engineering problems to be set and solved using computer software. They also provide the opportunity for the key skills listed above to be assessed and implemented. The Formative assessment will run as a lead-in to the summative assessment and will be used to assess the students comprehension of the techniques discussed in the lectures whilst preparing their data for subsequent analysis (AHEP 4 learning outcomes: M1, M2, M3, M12, M13, M17).

General Notes

N/A