Exploring multi-objective trade-offs in the design space of a waste heat recovery system. (1st June 2017)

• Record Type: Journal Article
• Authors: Mokhtar, Maizura; Burns, Stephen; Ross, Dave; Hunt, Ian
• Is Part Of: Applied energy. Volume 195(2017)
• Page Start: 114
• Page End: 124
• Publication Date: 2017-06-01
• Subjects: Waste heat recovery -- Optimisation -- Multi-objective evolutionary algorithm -- Mutually exclusive objective functions; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.03.030
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Waste heat recovery system optimisation is a multi-objective optimisation problem. An MOEA is used to optimise a waste heat recovery system. Clustering discovers representative trade-offs amongst Pareto-optimal solutions. Combining clustering with parallel coordinates eases the analysis of trade-offs. Abstract: A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system's parameters and their trade-offs, providing another source of information for prospective installations.