Multi-generation energy system based on geothermal source to produce power, cooling, heating, and fresh water: Exergoeconomic analysis and optimum selection by LINMAP method

Khanmohammadi, Shoaib; Musharavati, Farayi

doi:10.1016/j.applthermaleng.2021.117127

Cited by 53 publications

(11 citation statements)

References 24 publications

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“…In another investigation, MATLAB Software and the Gray Wolf algorithm were used for the cost optimization of a solar system during its life cycle in Brunei 19 . Multiobjective optimization is used for a trade‐off between several major objective functions 20,21 . Shahsavar et al 22 considered a building integrated photovoltaic/thermal (BIPV/T) collector and a thermal wheel (TW) system and carried out exergy analysis and multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

Using deep learning artificial intelligence and multiobjective optimization in obtaining the optimum ratio of a fuel cell to electrolyzer power in a hydrogen storage system

Mansir

Musharavati

Abubakar

2022

Intl J of Energy Research

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Summary Due to the depletion of fossil fuel sources and their pollution, renewable energy sources have attracted researchers' attention for energy supply; power generation using solar energy is one of the most important examples of these sources. This energy needs to be saved because of climate changes, to be used with high confidence. In this investigation, the heating and cooling of 100 Conex in Riyadh, Saudi Arabia, was studied, which used hydrogen storage as the central storage system. The main purpose of this project is to determine the ratio of fuel cell power to the electrolyzer power in this storage system, to reach the minimum dependency of the system on the urban power grid and at the same time, the minimum cost of electrolyzer and fuel cell. Because the shelters were simulated by Energy Plus and Open Studio Software and the energy supply system by TRNSYS Software, the optimization was not possible. Therefore, this system was changed into a network in MATLAB Software after simulation with deep learning artificial intelligence; then, it was optimized by a genetic algorithm. In order to accurately form a network, different parameters of artificial intelligence were studied precisely. The best result was obtained from a neural network with two hidden layers, each one containing 10 neurons, sigmoid activation function, and without dropout, with a correlation coefficient of 0.9998, which shows an excellent accuracy. The results of optimization also indicated that at all points, the Pareto front for fuel cell power is less than or equal to the power of the electrolyzer, and this ratio is 0.47 at the optimum point in terms of cost and electric consumption.

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Section: Introductionmentioning

confidence: 99%

Using deep learning artificial intelligence and multiobjective optimization in obtaining the optimum ratio of a fuel cell to electrolyzer power in a hydrogen storage system

Mansir

Musharavati

Abubakar

2022

Intl J of Energy Research

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“…The temperature ratio (

) of the WF and volume compression ratio (

) of the cycle will be selected as optimization variables, then the NSGA-II algorithm [ 79 , 80 , 81 , 82 ] will be applied to perform MOO on four OOs, that is,

, and dimensionless PD (

). The Pareto optimal solution of four-, three-, two-, and single-objective optimizations will be reached, and the optimal scheme will be reached by selecting the minimum deviation indexes (

) [ 83 ] with TOPSIS [ 84 , 85 , 86 ], LINMAP [ 87 , 88 ], and Shannon Entropy [ 89 , 90 ] decision-making strategies.…”

Section: Introductionmentioning

confidence: 99%

Four-Objective Optimization of an Irreversible Stirling Heat Engine with Linear Phenomenological Heat-Transfer Law

Chen

et al. 2022

Entropy

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This paper combines the mechanical efficiency theory and finite time thermodynamic theory to perform optimization on an irreversible Stirling heat-engine cycle, in which heat transfer between working fluid and heat reservoir obeys linear phenomenological heat-transfer law. There are mechanical losses, as well as heat leakage, thermal resistance, and regeneration loss. We treated temperature ratio x of working fluid and volume compression ratio λ as optimization variables, and used the NSGA-II algorithm to carry out multi-objective optimization on four optimization objectives, namely, dimensionless shaft power output P¯s, braking thermal efficiency ηs, dimensionless efficient power E¯p and dimensionless power density P¯d. The optimal solutions of four-, three-, two-, and single-objective optimizations are reached by selecting the minimum deviation indexes D with the three decision-making strategies, namely, TOPSIS, LINMAP, and Shannon Entropy. The optimization results show that the D reached by TOPSIS and LINMAP strategies are both 0.1683 and better than the Shannon Entropy strategy for four-objective optimization, while the Ds reached for single-objective optimizations at maximum P¯s, ηs, E¯p, and P¯d conditions are 0.1978, 0.8624, 0.3319, and 0.3032, which are all bigger than 0.1683. This indicates that multi-objective optimization results are better when choosing appropriate decision-making strategies.

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“…Nazari and Porkhial [ 20 ] came up with an updated multigeneration scheme using solar energy, wherein the thermodynamic properties were investigated in the two cases of biomass direct combustion and NG combustion separately. Khanmohammadi and Musharavati [ 21 ] proposed a trigeneration system with geothermal energy as the main heat source and applied Linear Programming Technique for Multidimensional Analysis of Preference method to carry out multiobjective optimization. Exergy efficiency and unit cost reached 15.86% and 0.107 $ kWh −1 , respectively, under optimal conditions.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Optimization of a Novel Combined Cooling, Heating, and Power System‐Integrated Rankine Cycle and Brayton Cycle Utilizing the Liquified Natural Gas Cold Energy

et al. 2022

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In recent years, the efficient utilization of energy has become a hot issue in scientific research. Society needs sustainable development. Fossil energy will gradually withdraw from the stage of energy utilization in the future, and the significance of clean energy becomes increasingly prominent. Natural gas (NG) stands out for its superior characteristics among many clean energy sources. It is generally accepted as the cleanest fossil energy source in the world, [1] which has advantages of high calorific value and economic and environmental protection. NG emits very little greenhouse gas during the combustion process, and its application can greatly reduce the global greenhouse effect. NG is liquid state when stored and when in short distance transported, liquefied natural gas (LNG) is colorless, nontoxic, odorless and noncorrosive, and is 600 times the volume of NG. [2,3] The temperature is À162 °C at 101.325 kPa, LNG will be gasified to more than 25 °C before being transported to the gas user terminal, and the huge cold energy will be released simultaneously. Utilizing this part of energy rationally will produce excellent benefits.At present, LNG cold energy is mainly used as a cold source and applied to the combined power generation, refrigeration, and heating cycle engineering fields with thermal energy (industrial waste heat, solar energy, geothermal energy, etc.) together. In the past decade, there have been extensive in-depth researches conducted in this direction by many scholars. Lee et al. [4] designed and

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Multi-generation energy system based on geothermal source to produce power, cooling, heating, and fresh water: Exergoeconomic analysis and optimum selection by LINMAP method

Cited by 53 publications

References 24 publications

Using deep learning artificial intelligence and multiobjective optimization in obtaining the optimum ratio of a fuel cell to electrolyzer power in a hydrogen storage system

Using deep learning artificial intelligence and multiobjective optimization in obtaining the optimum ratio of a fuel cell to electrolyzer power in a hydrogen storage system

Four-Objective Optimization of an Irreversible Stirling Heat Engine with Linear Phenomenological Heat-Transfer Law

Performance Analysis and Optimization of a Novel Combined Cooling, Heating, and Power System‐Integrated Rankine Cycle and Brayton Cycle Utilizing the Liquified Natural Gas Cold Energy

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