通过电凝去除四级化合物:使用响应表面方法和机器学习进行机械分析和多响应优化
Ali Tavakoli1, Mohammad Reza Alavi Moghaddam1, Javad Abdollahi1
1Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran.
Water research
|November 18, 2025
概括
这项研究优化了电凝,从水中去除四级化合物 (QAC). 一种混合机器学习和多响应优化方法实现了94.95%的删除,大大降低了成本.
科学领域:
- 环境化学环境化学
- 水处理技术水处理技术
- 过程优化 过程优化
背景情况:
- 由于广泛使用,四级化合物 (QAC) 越来越多地出现在水生环境中.
- 对于含有QACs的工业排放物,如甲 (CTAB),需要有效的处理策略.
- 电凝 (EC) 是一种有前途的技术,用于去除新出现的污染物.
研究的目的:
- 开发和优化一个电凝 (EC) 过程,以去除乙烯三甲基化物 (CTAB),一个模型 QAC.
- 为了比较响应表面方法 (RSM) 和机器学习 (ML) 模型的预测准确度,用于EC优化.
- 通过混合ML-MRO框架来确定最佳的EC运行条件,以成本有效地去除污染物.
主要方法:
- 采用中央复合设计,收集关于CTAB去除,能量和电极消耗的实验数据.
- 响应表面方法 (RSM) 和机器学习 (ML) 模型 (线性回归) 已被开发用于预测.
- 使用Pyomo的多响应优化 (MRO) 用于找到帕雷托最佳解决方案.
- 机械学研究包括动力学,污泥表征和密度函数理论 (DFT) 计算.
主要成果:
- 与RSM相比,基于ML的线性回归模型在CTAB移除和电极消耗方面显示出更高的准确性.
- 优化的EC过程实现了94.95%的CTAB去除.
- 在最佳条件下 (100 mg L-1 CTAB, 1.5 A, 2.5 g L-1 Na2 SO4 15 分钟) 与RSM相比,治疗成本降低了38.5%.
- 机械学研究表明,通过静电吸附和扫分片来去除.
结论:
- 一个混合ML-MRO框架有效地优化了EC流程的QAC删除.
- 开发的框架为设计具有成本效益的电化学处理系统提供了一个可扩展的策略.
- 这种方法可以帮助减轻水生环境中新出现的污染物的影响.
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