An Artificial Intelligence Approach for Thermodynamic Modeling of Geothermal Based-Organic Rankine Cycle Equipped with Solar System

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An Artificial Intelligence Approach for Thermodynamic Modeling of Geothermal Based-Organic Rankine Cycle Equipped with Solar System. / Khosravi, Ali; Syri, Sanna; Zhao, Xiaowei; El Haj Assad, Mamdouh.

In: GEOTHERMICS, Vol. 80, 01.07.2019, p. 138-154.

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@article{a8c76c1867404efc82dba569e6cbd04a,
title = "An Artificial Intelligence Approach for Thermodynamic Modeling of Geothermal Based-Organic Rankine Cycle Equipped with Solar System",
abstract = "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 ×〖10〗^(-4), 3.278 ×〖10〗^(-4) and 1.332 ×〖10〗^(-4), respectively.",
keywords = "Geothermal organic Rankine cycle, Adaptive neuro-fuzzy inference system, Multilayer neural network, Particle swarm optimization, Solar thermal collector",
author = "Ali Khosravi and Sanna Syri and Xiaowei Zhao and {El Haj Assad}, Mamdouh",
year = "2019",
month = "7",
day = "1",
doi = "10.1016/j.geothermics.2019.03.003",
language = "English",
volume = "80",
pages = "138--154",
journal = "GEOTHERMICS",
issn = "0375-6505",
publisher = "Elsevier Limited",

}

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TY - JOUR

T1 - An Artificial Intelligence Approach for Thermodynamic Modeling of Geothermal Based-Organic Rankine Cycle Equipped with Solar System

AU - Khosravi, Ali

AU - Syri, Sanna

AU - Zhao, Xiaowei

AU - El Haj Assad, Mamdouh

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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 ×〖10〗^(-4), 3.278 ×〖10〗^(-4) and 1.332 ×〖10〗^(-4), respectively.

AB - 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 ×〖10〗^(-4), 3.278 ×〖10〗^(-4) and 1.332 ×〖10〗^(-4), respectively.

KW - Geothermal organic Rankine cycle

KW - Adaptive neuro-fuzzy inference system

KW - Multilayer neural network

KW - Particle swarm optimization

KW - Solar thermal collector

UR - http://www.scopus.com/inward/record.url?scp=85062995552&partnerID=8YFLogxK

U2 - 10.1016/j.geothermics.2019.03.003

DO - 10.1016/j.geothermics.2019.03.003

M3 - Article

VL - 80

SP - 138

EP - 154

JO - GEOTHERMICS

JF - GEOTHERMICS

SN - 0375-6505

ER -

ID: 29787598