对废水处理中的环状液体颗粒进行案例研究的增强化学凝结方法的评估:统计优化分析和机器学习的实施
Behnam Fayyaz Shahandashty1, Narges Fallah1, Mohammad Shamsi2
1Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran.
Journal of environmental management
|September 11, 2024
概括
这项研究优化了煤矿废水处理的化学凝固,使用化铁和中性花剂实现了高污染物去除. 优化过程在减少总悬浮固体和度方面表现出卓越的效率.
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.
- 水处理技术 水处理技术
背景情况:
- 煤炭开采是主要的能源,产生大量的废水,需要有效的处理.
- 化学凝固是一种经过验证的工业废水处理方法,包括煤矿的废水.
研究的目的:
- 为了优化化学凝固,用于处理煤矿废水.
- 确定影响凝血过程的关键因素,并对其性能进行建模.
- 评估污染物清除的效率和预测模型的可靠性.
主要方法:
- 使用铁基和基凝固剂与带电的聚烯胺花剂.
- 应用Plackett-Burman查以确定显著因素 (铁化物,中性花粉剂,混合时间).
- 采用Box-Behnken设计用于过程建模和优化,以沉积速度作为输出.
主要成果:
- 确定了最佳条件:106.3 mg/L 铁化物,3.98 mg/L 中性花剂,缓慢混合 29.6 分钟,产生 1.10 cm/min 沉积速度.
- 实现了高污染物去除:100%的TSS,100%的度,87%的TSS,93%的COD,81%的BOD.
- 人工神经网络 (ANN) 模型显示出优越的性能 (AAD% 0.66,MSE 0.0001,R2 0.99) 超过盒子-Behnken设计 (BBD).
结论:
- 优化的化学凝固有效地处理煤矿废水,显著减少关键污染物.
- 该ANN模型提供了一个高度可靠的工具,用于预测废水处理过程的性能.
- 该研究证实了开发的废水处理优化模型的高可靠性和适用性.
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