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开发一种优化遗传算法方法,用于估计城市固体废物成分

Mohsen Banifateme¹, Peyman Zaroorian¹, Ali Behbahaninia¹

¹Department of Energy System Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Waste management (New York, N.Y.)

|December 25, 2025

概括

这项研究引入了一种基于遗传算法的新方法,用于在没有物理采样的情况下估计城市固体废物成分. 这种方法为废物管理和资源回收提供了一个稳定而准确的替代方案.

科学领域:

环境科学环境科学
化学工程是化学工程的重要组成部分.
计算机科学计算机科学

背景情况:

准确的城市固体废物 (MSW) 组成估计对于有效的废物管理和资源回收至关重要.
传统的采样方法是劳动密集型,昂贵和耗时的.
对于MSW成分分析的非侵入性,准确和稳定的方法存在需求.

研究的目的:

开发和验证一种基于遗传算法 (GA) 的反向方法,用于在没有直接采样的情况下估计MSW组成.
通过使用数值模拟来评估拟议的GA方法的准确性和稳定性.
将经过验证的方法应用于废物转化能源工厂的现实数据.

主要方法:

使用GA开发了一个反向建模方法,以从可测量的参数 (烟气,工作液体,灰,液) 估计MSW组成.
用5种不同的MSW组合,被随机错误扰乱的数值模拟用于验证.
通过增加方程数量,提高准确性和减少错误来稳定反向问题.

主要成果:

基于GA的逆溶液表现出稳定性,并准确地复制了模拟的MSW样本的平均成分.
估计的MSW成分与实际值非常接近,证实了GA方法的可行性.
在阿拉德库赫废物转化能源厂的应用产生了特定的成分值:C 27.14%,O 33.29%,H 3.16%,S 0.3%,水分 15.41%,灰 20.21%.

结论:

基于GA的反向方法为MSW组成估计提供了稳定和准确的非采样方法.
通过增加方程来稳定反向问题,可以显著提高准确性并减少估计错误.
该方法为废物管理操作提供了实用和高效的工具,以其在德黑兰废物转化能源工厂的成功应用为例.

关键词:

遗传算法反向解决方案的方法城市固体废物分析发电厂发电厂发电厂是一个资源回收利用的方法优化废物管理优化废物管理废物转化为能源

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