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机器学习算法在轮机行业的环境可持续性评估中发挥作用:对离心压缩机的应用
Alessandro Giraldi1, Riccardo Barbieri1, Luca Lombardozzi2
1Department of Industrial Engineering, University of Florence, Via di S. Marta 3, 50139, Florence, Italy.
本研究介绍了一种用于评估工业产品环境可持续性的预测模型,超越传统的生命周期评估 (LCA),以支持碳中和目标.
科学领域:
- 工业工程 工业工程 工业工程
- 环境科学 环境科学
- 可持续制造 可持续制造 可持续制造
背景情况:
- 越来越多的客户对产品环境影响评估的需求.
- 政府指令强调产品和工艺中的工业环境可持续性.
- 传统生命周期评估 (LCA) 的局限性是由于其具有追溯性.
研究的目的:
- 实施一个预测模型来评估离心压缩机转子组件的环境可持续性.
- 克服传统LCA的回顾性限制.
- 为设计工程师提供工具,以评估产品的生态概况,并指导选择实现碳中和.
主要方法:
- 在工业环境中开发预测模型.
- 与初级库存流量量定制的测量活动并行实施.
- 对模型开发和功能进行详细的方法方法.
主要成果:
- 成功实施环境可持续性评估的预测模型.
- 展示模型的功能,以支持设计工程师.
- 通过案例研究来说明模型的应用.
结论:
- 预测模型提供了一种积极的方法来评估产品的环境可持续性.
- 开发的模型有助于满足客户对影响评估的需求.
- 该模型支持设计工程师实现碳中和目标.
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