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A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty

Charitopoulos, VM; Dua, V; (2017) A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty. Applied Energy , 186 (Part 3) pp. 539-548. 10.1016/j.apenergy.2016.05.082. Green open access

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Abstract

Process and energy models provide an invaluable tool for design, analysis and optimisation. These models are usually based upon a number of assumptions, simplifications and approximations, thereby introducing uncertainty in the model predictions. Making model based optimal decisions under uncertainty is therefore a challenging task. This issue is further exacerbated when more than one objective is to be optimised simultaneously, resulting in a Multi-Objective Optimisation (MO2MO2) problem. Even though, some methods have been proposed for MO2MO2 problems under uncertainty, two separate optimisation techniques are employed; one to address the multi-objective aspect and another to take into account uncertainty. In the present work, we propose a unified optimisation framework for linear MO2MO2 problems, in which the uncertainty and the multiple objectives are modelled as varying parameters. The MO2MO2 under uncertainty problem (MO2U2)(MO2U2) is thus reformulated and solved as a multi-parametric programming problem. The solution of the multi-parametric programming problem provides the optimal solution as a set of parametric profiles.

Type: Article
Title: A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.apenergy.2016.05.082
Publisher version: http://doi.org/10.1016/j.apenergy.2016.05.082
Language: English
Additional information: © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
Keywords: Multi-objective optimisationMulti-parametric programmingOptimisation under uncertaintyEnergy systems
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1508335
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