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A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins

Golizadeh Akhlaghi, Yousef; Badiei, Ali; Zhao, Xudong; Aslansefat, Koorosh; Xiao, Xin; Shittu, Samson; Ma, Xiaoli

Authors

Koorosh Aslansefat



Abstract

This study is pioneered in developing digital twins using Feed-forward Neural Network (FFNN) and multi objective evolutionary optimization (MOEO) using Genetic Algorithm (GA) for a counter-flow Dew Point Cooler with a novel Guideless Irregular Heat and Mass Exchanger (GIDPC). The digital twins, takes the intake air characteristics, i.e., temperature, relative humidity as well as main operating and design parameters, i.e., intake air velocity, working air fraction, height of HMX, channel gap, and number of layers as the inputs. GIDPC’s cooling capacity, coefficient of performance (COP), dew point efficiency, wet-bulb efficiency, supply air temperature and surface area of the layers are selected as outputs. The optimum values of aforementioned operating and design parameters are identified by the MOEO to maximise the cooling capacity, COP, wet-bulb efficiencies and to minimise the surface area of the layers in four identified climates within Köppen-Geiger climate classification, namely: tropical rainforest, arid, Mediterranean hot summer and hot summer continental climates. The system monthly and annual performances in the identified optimum conditions are compared with the base system and the results show the annual improvements of up to 72.75% in COP and 23.57% in surface area. In addition, the annual power consumption is reduced by up to 49.41% when the system is designed and operated optimally. It is concluded that identifying the optimum conditions for the GIDPC can increase the system performance substantially.

Journal Article Type Article
Publication Date May 1, 2020
Journal Energy Conversion and Management
Print ISSN 0196-8904
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 211
Article Number 112772
APA6 Citation Golizadeh Akhlaghi, Y., Badiei, A., Zhao, X., Aslansefat, K., Xiao, X., Shittu, S., & Ma, X. (2020). A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins. Energy Conversion and Management, 211, https://doi.org/10.1016/j.enconman.2020.112772
DOI https://doi.org/10.1016/j.enconman.2020.112772
Keywords Dew point cooler; Genetic algorithm; Multi objective evolutionary optimization; Neural network; Digital twins
Additional Information This article is maintained by: Elsevier; Article Title: A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins; Journal Title: Energy Conversion and Management; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.enconman.2020.112772; Content Type: article; Copyright: © 2020 Elsevier Ltd. All rights reserved.
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