Utilization of machine learning techniques in the analysis and enhancement of poly-generation energy systems improves their efficiency and sustainability. Also, waste-to-energy systems propose a hopeful answer for both waste management and sustainable energy and water production. The production of hydrogen and freshwater through these systems not only provides valuable resources but also contributes to environmental sustainability. This research utilizes artificial intelligence's machine learning algorithms to examine and enhance a waste-to-energy system within a poly-generation energy system that generates hydrogen, freshwater, power, oxygen, and hot air. The integrated system consists of a waste combustion chamber, a proton exchange membrane electrolyzer, a supercritical carbon dioxide Brayton cycle, and a desalination system. Based on the findings, the machine learning algorithms exhibiting R-squared values exceeding 99% are considered strong fits. Additionally, all algorithms with high predicted R-squared values have the capability to accurately forecast new data using the provided training data. The emissions of 669.7 g/kWh and an efficiency of 69.62% are achieved when the pressure ratio is 10.73 and the temperature is 863.6 °C under optimal conditions. The accuracy and validity of the machine learning techniques are further confirmed by the strong agreement with thermodynamic modeling.

中文翻译：

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氢/淡水/电力/氧气/热能生产的垃圾发电多联产方案；通过回归机器学习算法优化

利用机器学习技术分析和增强多联产能源系统可以提高其效率和可持续性。此外，废物转化能源系统为废物管理和可持续能源和水生产提供了一个充满希望的答案。通过这些系统生产氢气和淡水不仅提供了宝贵的资源，而且还有助于环境的可持续性。这项研究利用人工智能的机器学习算法来检查和增强多联产能源系统中的废物能源系统，该系统产生氢气、淡水、电力、氧气和热空气。该集成系统由废物燃烧室、质子交换膜电解槽、超临界二氧化碳布雷顿循环和海水淡化系统组成。根据研究结果，R 平方值超过 99% 的机器学习算法被认为是强拟合。此外，所有具有高预测 R 平方值的算法都能够使用提供的训练数据准确预测新数据。在最佳工况下，当压力比为10.73、温度为863.6℃时，排放量为669.7 g/kWh，效率为69.62%。与热力学建模的强烈一致性进一步证实了机器学习技术的准确性和有效性。