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CME8124 : Multivariate Methods

  • Offered for Year: 2020/21
  • Module Leader(s): Dr Chris O'Malley
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semester 1 Credit Value: 10
ECTS Credits: 5.0


To introduce students to multivariate statistical methodologies that allow them to analyse industrial data thereby realising an enhanced understanding of the process. The course covers three aspects in terms of focus – data pre-screening, feature extraction and process modelling.

This module addresses a real challenge that is of extreme relevance to the process industries. There are many cases where it is not possible to undertake experimental design and utilise the resulting data to attain enhanced process understanding, or the development of models for process monitoring or optimisation. The only data available may be that collected directly on the plant. Consequently it is not necessarily of an appropriate form, in the first instance, to execute the required task, be it finger printing or modelling. Therefore to extract the maximum amount of information, necessitates in the first instance the cleaning of the data (removing outliers, filtering the data, in-filling for missing data, time shifting variable, for example) prior to the application of appropriate statistical methodologies, such as principal component analysis if the objective is feature extraction. Multivariate statistical control of both continuous and batch industrial processes will be explored through the extension to principal component analyses. Additionally for process control and optimisation, it is essential to develop a robust model of the process of interest. The course introduces a number of linear modelling techniques to help realise the aforementioned objectives.

Outline Of Syllabus

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.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion14:304:30Problem Solving Exercise 2 and subsequent writing up in report format
Guided Independent StudyAssessment preparation and completion14:304:30Problem Solving Exercise 1 and subsequent writing up in report format
Scheduled Learning And Teaching ActivitiesLecture161:0016:00N/A
Scheduled Learning And Teaching ActivitiesSmall group teaching162:0032:00Numerical practice sessions
Guided Independent StudyIndependent study143:0043:00Review lecture material and prepare for small group teaching
Teaching Rationale And Relationship

Lectures convey the statistical concepts and theory and their application in process engineering. Numerical practice 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 numerical practice sessions allow the completion of assignment work.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises1M50Assessed report - Data Prescreening, Principal Component Analysis & process monitoring (maximum 25 pages)
Prob solv exercises1M50Assessed report - Process Data Modelling (set week 11), maximum 20 pages
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.

Reading Lists