Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach
<p>This paper presents the design, modeling, and multi-objective optimization of an advanced solar energy system based on concentrated solar power technology, aimed at sustainable electricity generation in urban environments. The proposed configuration integrates a high-temperature Rankine cyc...
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2025
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| _version_ | 1864513539898605568 |
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| author | Haitham Osman (11737057) |
| author2 | Abdelfattah Amari (17732601) Sarminah Samad (13811242) Abdellatif M. Sadeq (16931841) Ibrahim Mahariq (18591148) Farruh Atamurotov (19681594) Lola Safarova (22254529) |
| author2_role | author author author author author author |
| author_facet | Haitham Osman (11737057) Abdelfattah Amari (17732601) Sarminah Samad (13811242) Abdellatif M. Sadeq (16931841) Ibrahim Mahariq (18591148) Farruh Atamurotov (19681594) Lola Safarova (22254529) |
| author_role | author |
| dc.creator.none.fl_str_mv | Haitham Osman (11737057) Abdelfattah Amari (17732601) Sarminah Samad (13811242) Abdellatif M. Sadeq (16931841) Ibrahim Mahariq (18591148) Farruh Atamurotov (19681594) Lola Safarova (22254529) |
| dc.date.none.fl_str_mv | 2025-09-10T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.tsep.2025.104071 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Dynamic_performance_evaluation_and_machine_learning-assisted_optimization_of_a_solar-driven_system_integrated_with_PCM-based_thermal_energy_storage_A_case_study_approach/30135475 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Fluid mechanics and thermal engineering Concentrated solar power (CSP) Phase change material (PCM) Thermoelectric generator (TEG) Kalina cycle (KC) Thermoeconomic optimization Artificial neural networks (ANN) |
| dc.title.none.fl_str_mv | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This paper presents the design, modeling, and multi-objective optimization of an advanced solar energy system based on concentrated solar power technology, aimed at sustainable electricity generation in urban environments. The proposed configuration integrates a high-temperature Rankine cycle, a thermoelectric generator, a thermal energy storage section based on phase change material (PCM) to ensure continuous operation during periods of solar intermittency, and a low-temperature Kalina cycle. The incorporation of high-temperature PCM facilitates stable thermal buffering, extending operational hours and improving system reliability and dispatchability. The PCM tank is dynamically modeled to capture transient thermal charging and discharging processes, thereby enhancing energy continuity and operational flexibility. Sensitivity and parametric analyses identify key performance parameters. A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. Considering optimum conditions, the system attains an exergetic efficiency of 30.13 % and a power generation of 7.24 MW, with a cost rate of 232.06 $/h and a payback period of 4.09 years. Seasonal simulations for Riyadh, Saudi Arabia, confirm robust system performance, with electricity generation peaking at 128.76 MWh in July. The findings underscore the synergistic contribution of PCM-based thermal storage and multi-cycle integration in delivering a reliable, dispatchable, and economically viable solar power solution suited for arid and high-irradiance regions.</p><h2>Other Information</h2> <p> Published in: Thermal Science and Engineering Progress<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.tsep.2025.104071" target="_blank">https://dx.doi.org/10.1016/j.tsep.2025.104071</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_da408ff44d944b744cceeaf2d586d331 |
| identifier_str_mv | 10.1016/j.tsep.2025.104071 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30135475 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approachHaitham Osman (11737057)Abdelfattah Amari (17732601)Sarminah Samad (13811242)Abdellatif M. Sadeq (16931841)Ibrahim Mahariq (18591148)Farruh Atamurotov (19681594)Lola Safarova (22254529)EngineeringElectrical engineeringFluid mechanics and thermal engineeringConcentrated solar power (CSP)Phase change material (PCM)Thermoelectric generator (TEG)Kalina cycle (KC)Thermoeconomic optimizationArtificial neural networks (ANN)<p>This paper presents the design, modeling, and multi-objective optimization of an advanced solar energy system based on concentrated solar power technology, aimed at sustainable electricity generation in urban environments. The proposed configuration integrates a high-temperature Rankine cycle, a thermoelectric generator, a thermal energy storage section based on phase change material (PCM) to ensure continuous operation during periods of solar intermittency, and a low-temperature Kalina cycle. The incorporation of high-temperature PCM facilitates stable thermal buffering, extending operational hours and improving system reliability and dispatchability. The PCM tank is dynamically modeled to capture transient thermal charging and discharging processes, thereby enhancing energy continuity and operational flexibility. Sensitivity and parametric analyses identify key performance parameters. A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. Considering optimum conditions, the system attains an exergetic efficiency of 30.13 % and a power generation of 7.24 MW, with a cost rate of 232.06 $/h and a payback period of 4.09 years. Seasonal simulations for Riyadh, Saudi Arabia, confirm robust system performance, with electricity generation peaking at 128.76 MWh in July. The findings underscore the synergistic contribution of PCM-based thermal storage and multi-cycle integration in delivering a reliable, dispatchable, and economically viable solar power solution suited for arid and high-irradiance regions.</p><h2>Other Information</h2> <p> Published in: Thermal Science and Engineering Progress<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.tsep.2025.104071" target="_blank">https://dx.doi.org/10.1016/j.tsep.2025.104071</a></p>2025-09-10T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.tsep.2025.104071https://figshare.com/articles/journal_contribution/Dynamic_performance_evaluation_and_machine_learning-assisted_optimization_of_a_solar-driven_system_integrated_with_PCM-based_thermal_energy_storage_A_case_study_approach/30135475CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301354752025-09-10T15:00:00Z |
| spellingShingle | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach Haitham Osman (11737057) Engineering Electrical engineering Fluid mechanics and thermal engineering Concentrated solar power (CSP) Phase change material (PCM) Thermoelectric generator (TEG) Kalina cycle (KC) Thermoeconomic optimization Artificial neural networks (ANN) |
| status_str | publishedVersion |
| title | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| title_full | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| title_fullStr | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| title_full_unstemmed | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| title_short | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| title_sort | Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach |
| topic | Engineering Electrical engineering Fluid mechanics and thermal engineering Concentrated solar power (CSP) Phase change material (PCM) Thermoelectric generator (TEG) Kalina cycle (KC) Thermoeconomic optimization Artificial neural networks (ANN) |