基于决策树的方法来推断生产过程的生命周期库存数据
Mohamed Saad1, Yingzhong Zhang1, Jia Jia1
1School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China.
Journal of environmental management
|May 17, 2024
概括
本研究引入了一种机器学习方法来估计缺失的制造生命周期库存 (LCI) 数据. 渐变增强模型有效预测温室气体 (GHG) 排放,帮助绿色制造的努力.
科学领域:
- 环境科学 环境科学
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
背景情况:
- 生命周期评估 (LCA) 对绿色制造至关重要,但有限的生命周期库存 (LCI) 数据阻碍了环境负担分析.
- 现有的LCI数据库往往缺乏针对特定制造工艺的全面数据,这构成了重大挑战.
研究的目的:
- 开发和验证一种新的机器学习方法来推断LCI数据,特别是温室气体 (GHG) 排放.
- 确定影响制造过程中温室气体排放和资源消耗的关键因素.
主要方法:
- 利用基于决策树的监督机器学习模型 (决策树,随机森林,梯度提升,自适应提升) 在Ecoinvent LCI数据上进行训练.
- 采用相关性分析来确定影响因素,数据预处理,列车测试分割 (70/30) 和超参数调整的五倍交叉验证.
- 基于使用R平方,根平均平方误差和平均百分比误差的预测性能的评估模型.
主要成果:
- 渐变增强模型在推断温室气体排放数据方面表现出卓越的性能,在测试组中达到R平方值<0.95.
- 相关性分析显示,工件材料和制造技术显著影响资源消耗 (能源,材料,水).
- 能源消耗,用水和原始耗量被确定为温室气体排放的关键驱动因素.
结论:
- 拟议的渐变增强 (GraBoost) 模型提供了一种可靠的计算方法,用于估计和推断温室气体排放,当LCI数据稀缺或不可用时.
- 这种方法支持更准确的LCA,并促进在绿色制造业中改善环境管理.
- 识别影响因素有助于制定有针对性的战略,以减少制造业对环境的影响.
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