Growing concerns about greenhouse gas emissions have accelerated research into converting CO2 into valuable products like methanol. Catalytic hydrogenation, utilizing a catalyst in a thermochemical process, offers a promising solution for reducing atmospheric CO2 and combating climate change. However, optimizing operating conditions and selecting suitable catalysts for CO2 to methanol conversion remains challenging due to the complex interplay between catalyst properties and reaction performance. This research leveraged machine learning (ML) to model CO2 to methanol conversion using a comprehensive experimental database. ML models were developed to predict CO2 conversion efficiency, methanol selectivity, and CO selectivity, facilitating process optimization, techno-economic analysis, and life cycle assessment (LCA). The gradient boosting regression model emerged as the most accurate, with coefficients of determination (R2 > 0.86) and low error metrics (RMSE < 9.99, MAE < 5.99). De novo predictions demonstrated an acceptable linear relationship with the completely unseen dataset. Feature importance analysis identified temperature and gas hourly space velocity (GHSV) as the most significant descriptors. The optimal conditions for maximum CO2 conversion efficiency and methanol selectivity were identified as temperatures between 330 and 370 °C, a pressure of 50 bar, and a GHSV of 6,500–14,000 mL/g.h. The techno-economic analysis highlighted H2 purchase price, methanol selling price, and CO2 feedstock costs as critical economic factors, with a payback period of 4.6 years. The LCA demonstrated a 270 % reduction in carbon emissions through catalytic hydrogenation of CO2 to methanol. This study underscored the importance of using sustainable H2 and electricity sources to enhance the economic and environmental benefits of the process.

中文翻译：

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机器学习驱动的优化，通过催化加氢实现可持续的 CO2 制甲醇转化


对温室气体排放的日益担忧加速了将 CO2 转化为甲醇等有价值的产品的研究。催化加氢在热化学过程中利用催化剂，为减少大气中的 CO2 和应对气候变化提供了一种有前途的解决方案。然而，由于催化剂特性和反应性能之间存在复杂的相互作用，优化操作条件和选择合适的催化剂进行 CO2 制甲醇转化仍然具有挑战性。这项研究利用机器学习 （ML） 使用全面的实验数据库对 CO2 到甲醇的转化进行建模。开发了 ML 模型来预测 CO2 转化效率、甲醇选择性和 CO 选择性，从而促进工艺优化、技术经济分析和生命周期评估 （LCA）。梯度提升回归模型是最准确的，具有决定系数 （R2 > 0.86） 和低误差指标 （RMSE < 9.99， MAE < 5.99）。从头预测证明了与完全看不见的数据集存在可接受的线性关系。特征重要性分析确定温度和气体每小时空速 （GHSV） 是最重要的描述符。确定最大 CO2 转化效率和甲醇选择性的最佳条件为 330 至 370 °C 的温度、50 bar 的压力和 6,500–14,000 mL/g.h 的 GHSV。技术经济分析强调 H2 购买价格、甲醇销售价格和 CO2 原料成本是关键的经济因素，投资回收期为 4.6 年。LCA 表明，通过将 CO2 催化加氢制甲醇，碳排放量减少了 270%。 这项研究强调了使用可持续的 H2 和电力来源来提高该过程的经济和环境效益的重要性。