干旱区生态环境知识资源中心(Arid): A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins

Arid

DOI	10.1016/j.enconman.2020.112772
	A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins
	Akhlaghi, Yousef Golizadeh; Badiei, Ali; Zhao, Xudong; Aslansefat, Koorosh; Xiao, Xin; Shittu, Samson; Ma, Xiaoli
通讯作者	Zhao, XD ; Xiao, X
来源期刊	ENERGY CONVERSION AND MANAGEMENT
ISSN	0196-8904
EISSN	1879-2227
出版年	2020
卷号	211
英文摘要	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 on 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 Koppen-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.
英文关键词	Dew point cooler Genetic algorithm Multi objective evolutionary optimization Neural network Digital twins
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000530067800029
WOS关键词	INDIRECT EVAPORATIVE COOLER ; MASS-TRANSFER ; NUMERICAL-ANALYSIS ; COOLING SYSTEM ; HEAT ; PERFORMANCE ; EXCHANGERS ; CONFIGURATION ; PREDICTION ; DESIGN
WOS类目	Thermodynamics ; Energy & Fuels ; Mechanics
WOS研究方向	Thermodynamics ; Energy & Fuels ; Mechanics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324392
作者单位	[Akhlaghi, Yousef Golizadeh; Badiei, Ali; Zhao, Xudong; Xiao, Xin; Shittu, Samson; Ma, Xiaoli] Univ Hull, Ctr Sustainable Energy Technol, Energy & Environm Inst, Kingston Upon Hull HU6 7RX, N Humberside, England; [Aslansefat, Koorosh] Univ Hull, Dept Comp Sci, Kingston Upon Hull HU6 7RX, N Humberside, England
推荐引用方式 GB/T 7714	Akhlaghi, Yousef Golizadeh,Badiei, Ali,Zhao, Xudong,et al. A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins[J],2020,211.
APA	Akhlaghi, Yousef Golizadeh.,Badiei, Ali.,Zhao, Xudong.,Aslansefat, Koorosh.,Xiao, Xin.,...&Ma, Xiaoli.(2020).A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins.ENERGY CONVERSION AND MANAGEMENT,211.
MLA	Akhlaghi, Yousef Golizadeh,et al."A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins".ENERGY CONVERSION AND MANAGEMENT 211(2020).