Artificial intelligence methods in process plant layout

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McBrien, Andrew (1994) Artificial intelligence methods in process plant layout. PhD thesis, University of Nottingham.

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Abstract

The thesis describes "Plant Layout System" or PLS, an Expert System which automates all aspects of conceptual layout of chemical process plant, from sizing equipment using process data to deriving the equipment items' elevation and plan positions. PLS has been applied to a test process of typical size and complexity and which encompasses a wide range of layout issues and problems. The thesis presents the results of the tests to show that PLS generates layouts that are entirely satisfactory and conventional from an engineering viewpoint.

The major advance made during this work is the approach to layout by Expert System of any kind of process plant. The thesis describes the approach in full, together with the engineering principles which it acknowledges.

Plant layout problems are computationally complex. PLS decomposes layout into a sequence of formalised steps and uses a powerful and sophisticated technique to reduce plant complexity. PLS uses constraint propagation for spatial synthesis and includes propagation algorithms developed specifically for this domain. PLS includes a novel qualitative technique to select constraints to be relaxed. A conventional frame based representation was found to be appropriate, but with procedural knowledge recorded in complex forward chaining rules with novel features. Numerous examples of the layout engineer's knowledge are included to elucidate the epistemology of the domain.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Mecklenburgh, J.
Pulford, C.
Keywords: Chemical process plants, Computer-aided design, Plant Layout System, Expert systems
Subjects: T Technology > TP Chemical technology
Faculties/Schools: UK Campuses > Faculty of Engineering > Department of Chemical and Environmental Engineering
Item ID: 14403
Depositing User: EP, Services
Date Deposited: 08 Jul 2014 07:57
Last Modified: 15 Dec 2017 13:39
URI: https://eprints.nottingham.ac.uk/id/eprint/14403

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