Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand.
Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
Bioresour Technol. 2023 Jun;378:128961. doi: 10.1016/j.biortech.2023.128961. Epub 2023 Mar 25.
The growing demand for fossil fuels has motivated the search for a renewable energy source, and biodiesel has emerged as a promising and environmentally friendly alternative. In this study, machine learning techniques were employed to predict the biodiesel yield from transesterification processes using three different catalysts: homogeneous, heterogeneous, and enzyme. Extreme gradient boosting algorithms showed the highest accuracy in predictions, with a coefficient of determination accuracy of nearly 0.98, as determined through a 10-fold cross-validation of the input data. The results indicated that linoleic acid, behenic acid, and reaction time were the most crucial factors affecting biodiesel yield predictions for homogeneous, heterogeneous, and enzyme catalysts, respectively. This research provides insights into the individual and combined effects of key factors on transesterification catalysts, contributing to a deeper understanding of the system.
日益增长的化石燃料需求促使人们寻找可再生能源，生物柴油作为一种有前途且环保的替代品应运而生。在这项研究中，使用三种不同的催化剂：均相、多相和酶，采用机器学习技术来预测酯交换过程中的生物柴油产率。极端梯度提升算法在预测中表现出最高的准确性，通过对输入数据进行 10 倍交叉验证，确定其决定系数准确性接近 0.98。结果表明，对于均相、多相和酶催化剂，亚油酸、山嵛酸和反应时间分别是影响生物柴油产率预测的最关键因素。这项研究深入了解了关键因素对酯交换催化剂的单独和综合影响，有助于更深入地理解该系统。
