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Optimising a Waste Heat Recovery System using Multi-Objective Evolutionary Algorithm

Mokhtar, Maizura; Hunt, Ian; Burns, Stephen; Ross, Dave

Authors

Maizura Mokhtar

Ian Hunt

Stephen Burns

Dave Ross



Abstract

A waste heat recovery system (WHRS) on a process with variable output, is an example of an intermittent renewable process. WHRS recycles waste heat into usable energy. As an example, waste heat produced from refrigeration can be used to provide hot water. However, consistent with most intermittent renewable energy systems, the likelihood of waste heat availability at times of demand is low. For this reason, the WHRS may be coupled with a hot water reservoir (HWR) acting as the energy storage system that aims to maintain desired hot water temperature Td (and therefore energy) at time of demand. The coupling of the WHRS and the HWR must be optimised to ensure higher efficiency given the intermittent mismatch of demand and heat availability. Efficiency of an WHRS can be defined as achieving multiple objectives, including to minimise the need for back-up energy to achieve Td, and to minimise waste heat not captured (when the reservoir volume Vres is too small). This paper investigates the application of a Multi Objective Evolutionary Algorithm (MOEA) to optimise the parameters of the WHRS, including the Vres and depth of discharge (DoD), that affect the WHRS efficiency. Results show that one of the optimum solutions obtained requires the combination of high Vres, high DoD, low water feed in rate, low power external back-up heater and high excess temperature for the HWR to ensure efficiency of the WHRS.

Citation

Mokhtar, M., Hunt, I., Burns, S., & Ross, D. (2016). Optimising a Waste Heat Recovery System using Multi-Objective Evolutionary Algorithm. In GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion (913-920). https://doi.org/10.1145/2908961.2931646

Conference Name Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
Start Date Jul 20, 2016
End Date Jul 24, 2016
Acceptance Date Apr 19, 2016
Online Publication Date Jul 20, 2016
Publication Date Jul 20, 2016
Deposit Date Dec 8, 2017
Publisher Association for Computing Machinery (ACM)
Pages 913-920
Book Title GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
Chapter Number n/a
ISBN 9781450343237
DOI https://doi.org/10.1145/2908961.2931646
Keywords Energy, power engineering, renewable energy,
Public URL http://researchrepository.napier.ac.uk/Output/832539