在智能工厂中整合直观模糊和MCDM方法来实现可持续能源管理
Mohd Anjum1, Naoufel Kraiem2, Hong Min3
1Department of Computer Engineering, Aligarh Muslim University, Aligarh, India.
PloS one
|January 14, 2025
概括
本研究介绍了一种使用直觉模糊集 (IFS) 和多标准决策 (MCDM) 的新框架,以提高智能工厂的能源效率. 发现替代方案5是改善能源性能和可持续性的最有效解决方案.
科学领域:
- 运营研究 运营研究
- 工业工程 工业工程 工业工程
- 环境科学 环境科学
背景情况:
- 智能工厂面临着越来越大的压力,需要提高能源效率,以实现可持续性和卓越运营.
- 减少碳足迹和管理运营成本是现代制造业的关键挑战.
- 遵守严格的环境标准需要先进的能源管理决策工具.
研究的目的:
- 开发一种新的决策框架,以优化智能制造中的能源效率.
- 整合直觉模糊集 (IFS) 与多标准决策 (MCDM) 技术,以实现可靠的能源管理.
- 提供决策支持工具,在复杂的制造环境中优先考虑节能解决方案.
主要方法:
- 确定和评估八个主要的能源绩效标准,包括可再生能源的使用和生产效率.
- 应用方法来对客观标准进行权衡.
- 利用CRADIS技术对节能替代品进行排名.
主要成果:
- 拟议的IFS-MCDM框架成功评估了复杂的能源管理场景.
- 敏感性分析验证了决策方法的稳定性.
- 选项5被确定为解决方案,在能源效率方面取得了最显著的改善.
结论:
- 开发的框架为智能工厂的能源优化提供了一个全面和可适应的工具.
- 这些发现支持行业从业人员向更可持续的生产方法过渡.
- 这项研究为更广泛的工业可持续性和能源效率议程提供了有价值的见解.
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