Maizura Mokhtar
Optimising a Waste Heat Recovery System using Multi-Objective Evolutionary Algorithm
Mokhtar, Maizura; Hunt, Ian; Burns, Stephen; Ross, Dave
Authors
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, July). Optimising a Waste Heat Recovery System using Multi-Objective Evolutionary Algorithm. Presented at Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
| Presentation Conference Type | Conference Paper (published) |
|---|---|
| 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 |