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Module

CME8124 : Multivariate Methods

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

Aims

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

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Assessment Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Reading Lists

Timetable