globalchange  > 过去全球变化的重建
DOI: 10.1016/j.geothermics.2019.03.003
WOS记录号: WOS:000466822100014
论文题名:
An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system
作者: Khosravi, A.1; Syri, S.1; Zhao, X.2; Assad, M. E. H.3
通讯作者: Khosravi, A.
刊名: GEOTHERMICS
ISSN: 0375-6505
EISSN: 1879-3576
出版年: 2019
卷: 80, 页码:138-154
语种: 英语
英文关键词: Geothermal organic Rankine cycle ; Adaptive neuro-fuzzy inference system ; Multilayer neural network ; Particle swarm optimization ; Solar thermal collector
WOS关键词: MACHINE LEARNING ALGORITHMS ; RENEWABLE ENERGY ; NEURAL-NETWORK ; POWER-PLANT ; THERMOECONOMIC ANALYSIS ; HYDROGEN-PRODUCTION ; EXERGY ANALYSIS ; WORKING FLUIDS ; WIND-SPEED ; OPTIMIZATION
WOS学科分类: Energy & Fuels ; Geosciences, Multidisciplinary
WOS研究方向: Energy & Fuels ; Geology
英文摘要:

Geothermal energy is a renewable resource that is constantly available. The low geothermal well operating lifetime is the main challenge in using this type of renewable energy. This problem can be covered by the aid of solar system (hybrid system). For complicated renewable energy systems, finding the optimum design parameters and operating conditions require to develop experimental apparatus or sophisticated thermodynamic models. Hence, in this study, artificial intelligence (AI) approach is proposed for modeling the geothermal organic Rankin cycle (GORC) equipped with solar thermal unit. Indeed, the current study depicts how AI methods can meticulously simulate the operation of a complicated renewable energy system. The developed intelligent methods are adaptive neuro-fuzzy inference system (ANFIS) optimized with particle swarm optimization (PSO) algorithm (ANFIS-PSO) and multilayer perceptron (MLP) neural network optimized with PSO algorithm (MLP-PSO). The models are composed based on the main design parameters of the geothermal system that are solar radiation, well temperature, working fluid mass flow rate, turbine output pressure, surface area of the solar collector and preheater inlet pressure. The intelligent models use the mentioned input variables to predict the net power output, energy efficiency, exergy efficiency and levelized cost of energy (LCOE) of the GORC. Energy, exergy and economic analyses are carried out for the low global warming potential (GWP) refrigerants. It was found out that although the intelligent models can meticulously predict the targets, ANFIS-PSO performs better than MLP-PSO for modeling the GORC with solar system. Root mean square error of this model for prediction of power generation, energy efficiency, exergy efficiency and LCOE was 12.023 (kW), 3.587 x 10(-4), 3.278 x 10(-4) and 1.332 x 10(-4), respectively.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/141160
Appears in Collections:过去全球变化的重建

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作者单位: 1.Aalto Univ, Sch Engn, Dept Mech Engn, Espoo, Finland
2.Univ Warwick, Sch Engn, Coventry, W Midlands, England
3.Univ Sharjah, Dept Sustainable & Renewable Energy Engn, Sharjah, U Arab Emirates

Recommended Citation:
Khosravi, A.,Syri, S.,Zhao, X.,et al. An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system[J]. GEOTHERMICS,2019-01-01,80:138-154
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