Waste heat recovery cycles integration into a net-Zero emission solar-thermal multi-generation system; Techno-economic analysis and ANN-MOPSO optimization
This paper presents a novel solar-powered multi-generation system (MGS) integrated with a fuel cell, designed to enhance both sustainability and operational reliability. A significant limitation of solar energy is its intermittency, as sunlight is only available during specific hours of the day. To...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24017210 |
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| Summary: | This paper presents a novel solar-powered multi-generation system (MGS) integrated with a fuel cell, designed to enhance both sustainability and operational reliability. A significant limitation of solar energy is its intermittency, as sunlight is only available during specific hours of the day. To address this constraint, hydrogen energy is incorporated into the system to facilitate continuous operation through the fuel cell. The proposed MGS efficiently utilizes waste heat recovery cycles to simultaneously produce electricity, fresh water, cooling, and heating. The system consists of parabolic trough solar collectors, a steam Rankine cycle, an organic Rankine cycle, an absorption chiller, a reverse osmosis desalination unit, and a fuel cell coupled with an organic Rankine cycle-thermoelectric generator. Upon validating the primary components, the system is thoroughly evaluated in terms of energy, exergy, and economic performance, and a parametric study is conducted to assess the influence of key operational parameters. The analysis identifies the solar cycle as having the highest irreversibility, accounting for 55.2 %, and the highest cost rate, contributing 44.1 % among the subsystems. To optimize the system's performance, an artificial neural network is integrated with a multi-objective particle swarm optimization algorithm to reduce computational time from approximately 16 h to 4 min. Finally, under optimal conditions, the system achieves an exergy efficiency of 31.69 %, freshwater production of 19.53 kg/s, cooling production of 441.6 kW, and a total cost rate of $87.2/h. |
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| ISSN: | 2214-157X |