López-Flores Francisco Javier, Ramírez-Márquez César, Rubio-Castro Eusiel, Ponce-Ortega José María
Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica, S/N, Ciudad Universitaria, Edificio V1, Morelia, Mich., 58060, Mexico.
Chemical and Biological Sciences Department, Universidad Autónoma de Sinaloa, Av. de las Américas S/N, Culiacán, Sinaloa, 80010, Mexico.
Environ Res. 2024 Apr 1;246:118047. doi: 10.1016/j.envres.2023.118047. Epub 2023 Dec 29.
This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10 m/kWh) and the emissions generated (1.12 × 10 TonCO/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.
本研究考察了墨西哥大规模生产太阳能光伏板的潜力，并强调了该国的独特特质，使其成为该领域强大的制造候选地。已开发出一种基于人工神经网络的先进模型来预测太阳能光伏板工厂的指标。该模型集成了一个使用Pyomo的先进非线性规划框架以及一个创新的优化和机器学习工具库。这种方法为包括生产时间表在内的各个光伏电站创建了替代模型。虽然通过广泛模拟和精细计算进行的这项研究表明，在全球市场的硅、晶圆、电池和组件生产中，拉丁美洲的占比一直严重不足；但它也证明了在墨西哥扩大光伏板生产的巨大潜力，这将带来显著的经济、社会和环境效益。通过超参数优化，开发出了一个出色且具有竞争力的人工神经网络模型，所有输出变量的决定系数值均高于0.99。研究发现，光伏板生产过程中的水和能源消耗显著。然而，能源生产过程中的水消耗（33.16×10立方米/千瓦时）和产生的排放量（1.12×10吨二氧化碳/千瓦时）明显低于传统发电厂。值得注意的是，结果突出了一个积极的经济趋势，即在所有生产阶段中，组件生产厂的利润最高（35.7%），而多晶硅生产厂的收益相对较低（13.0%）。此外，本研究强调了光伏板生产过程中的一个关键因素，即电池生产厂因其复杂的多阶段工艺而对能源消耗贡献最大（39.7%）。机器学习与优化模型的融合预示着可再生能源领域资源分配的新时代，带来更光明、更绿色的未来。
