Enhancing energy efficiency with a photovoltaic-based multi-product system with integration of electrolysis and desalination
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Qian Huang
Abstract
Photovoltaic (PV)-based multi-product systems are the most effective strategies for optimizing energy consumption and enhancing energy production efficiency, which integrates multiple energy production processes within a single framework for maximizing the utilization of available energy. This research aims to provide an inclusive analysis of the PV-based multi-product system performance, which integrates electrolysis, and desalination units for simultaneous generation of power, hydrogen, and freshwater. Therefore, the optimized Extreme Gradient Boosting (XGBoost) model was carefully developed and trained to ensure high levels of accuracy and precision in predicting the power, fresh water, and hydrogen outputs of the system under varying operational conditions. This optimized XGBoost model is achieved through the hyperparameter tuning by the HenryGas Solubility Optimization Algorithm, which ensures high levels of accuracy and precision. The day-by-day analysis of power and hydrogen generation demonstrated that in winter, a minimum PV power of 6,200 kW and a hydrogen mass flow rate of 65 kg were witnessed due to low solar radiation and short daylight hours. In contrast, summer and spring registered the highest PV power levels of 8,000 kW and 7,500 kW, respectively, and hydrogen mass flow rates of 75 kg due to high solar radiation and prolonged daylight hours, hence enhancing system performance.
Acknowledgment
I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Qian HUANG”. Also the first draft of the manuscript was written by Qian HUANG commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The author declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The author do not have permissions to share data.
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