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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


Yousef Golizadeh Akhlaghi

Ali Badiei

Koorosh Aslansefat

Xin Xiao

Samson Shittu


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.


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, Article 112772.

Journal Article Type Article
Acceptance Date Mar 23, 2020
Online Publication Date Apr 3, 2020
Publication Date May 1, 2020
Deposit Date Apr 28, 2020
Publicly Available Date Apr 4, 2022
Journal Energy Conversion and Management
Print ISSN 0196-8904
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 211
Article Number 112772
Keywords Dew point cooler; Genetic algorithm; Multi objective evolutionary optimization; Neural network; Digital twins
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