基于机器学习的生命周期评估,以优化食品供应链的环境可持续性
Amin Nikkhah1,2,3, Mahdi Esmaeilpour4, Armaghan Kosari-Moghaddam4
1Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
Integrated environmental assessment and management
|June 14, 2024
概括
本研究引入了一种使用德尔菲方法的新型混合框架,以优化农业食品系统的资源配置. 这种方法显著减少了环境影响,例如,石榴生产的全球变暖潜力减少了46%.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 有效的资源配置对于可持续的农业食品供应链和减少环境影响至关重要.
- 将生命周期评估 (LCA) 与机器学习相结合,为评估和改进食品供应链提供了一种强有力的方法.
- 对变量定义准确的优化边界至关重要,但通常依赖于主观调查数据.
研究的目的:
- 开发和应用混合环境评估框架,以优化农业食品生产系统.
- 引入Delphi方法,可对优化变量边界进行可靠的确定.
- 在案例研究中评估框架在减轻环境影响方面的有效性.
主要方法:
- 开发了一个混合框架,结合了生命周期评估 (LCA),多层感知器人工神经网络,Delphi方法和遗传算法.
- 为了确定优化变量的可靠最小和最大边界,采用了Delphi方法.
- 该框架的应用是为了优化石榴生产系统.
主要成果:
- 混合框架显示了减轻环境影响的巨大潜力.
- 在石榴生产案例研究中,全球变暖影响的潜在减少达到了46%.
- 加入德尔菲方法提高了资源配置优化的准确性.
结论:
- 拟议的混合框架整合了LCA,机器学习和Delphi方法,有效地优化了农业食品系统.
- 德尔菲方法为优化过程中确定变量边界提供了一种新且强大的方法.
- 这种方法为实现更可持续和循环的食品供应链提供了一个有希望的途径.
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