School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China.
School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China.
J Environ Manage. 2024 Jun;360:121152. doi: 10.1016/j.jenvman.2024.121152. Epub 2024 May 17.
Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.
生命周期评估（LCA）在绿色制造中起着至关重要的作用，可揭示减轻制造过程环境负担的关键方面。然而，制造过程的生命周期清单（LCI）数据稀缺是一个相当大的挑战。本文提出了一种新的方法来推断制造过程的 LCI 数据。利用 Ecoinvent 数据集的 LCI 数据，开发了基于决策树的有监督机器学习模型，即决策树、随机森林、梯度提升和自适应提升，以推断温室气体（即二氧化碳、氧化亚氮、甲烷和水蒸气）排放的 LCI 数据。首先，进行相关性分析以得出对制造活动产生的温室气体数量有影响的最主要因素。首先，对收集的数据进行预处理，并将其分为训练集和测试集（分别为 70%和 30%）。其次，应用五折交叉验证方法调整模型的超参数。然后，使用最佳超参数重新训练模型，并使用测试集进行评估。结果表明，梯度提升模型在推断温室气体排放数据方面具有卓越的预测性能，在测试集上的平均确定系数（R）<0.95。此外，模型预测涉及测试集上平均均方根误差和平均平均百分比误差的相对低值。相关性和特征重要性分析强调，工件材料和制造技术对自然资源消耗（即进入过程的能源、材料和水的流入）有较大影响。同时，能源消耗、水的使用和原铝消耗是温室气体排放的最主要影响因素。最后，使用经过验证的模型对新制造活动的流入和流出进行了推断。所提出的 GraBoost 模型提供了一种计算补充方法，可在 LCI 数据不完整或不存在于 LCI 数据库中的情况下，估计和推断不同制造过程的温室气体排放。
