基于机器学习,模拟和优化用于酸性矿井排水 (AMD) 的多重透反应屏障 (多PRB)
Lai Zhou1,2, Jiliang Qian3,4, Yanzhuo Liu3,4
1Engineering Research Center of Ministry of Education for Mine Ecological Restoration, Xuzhou, 221116, Jiangsu, China. zhoulai99@cumt.edu.cn.
Environmental geochemistry and health
|February 6, 2026
概括
本研究介绍了一种针对多重透性反应性屏障 (多重PRB) 的优化设计,用于处理酸性矿井排水 (AMD). 机器学习显著改善了设计过程,提高了治疗效率和寿命.
科学领域:
- 环境工程 环境工程
- 水处理技术水处理技术
- 地质化学 地质化学
背景情况:
- 酸性矿井排水 (AMD) 带来了重大的环境挑战.
- 透反应屏障 (PRBs) 是一个有前途的在现场治疗AMD的技术.
- 目前多个PRB的实施受到缺乏机制理解和决策工具的限制.
研究的目的:
- 开发一个合过程数值模型来模拟多PRB中的协同TFe和SO42−去除.
- 为高效的多PRB设计提出一种机器学习集成优化方法 (ML-NSGAII).
- 确定关键的设计参数,并制定AMD治疗的实用设计策略.
主要方法:
- 开发用于多PRB模拟的合过程数值模型.
- 作为非主导排序遗传算法 (ML-NSGAII) 中的替代模型,集成一个逆向传播神经网络 (BPNN).
- 应用斯皮尔曼相关性,SHAP分析和TOPSIS-重法用于参数识别和最佳设计选择.
主要成果:
- 该ML-NSGAII方法减少了99.7%的计算负载,具有高度准确的替代模型 (R2>0.99).
- 液压负荷和填料尺寸被确定为最有影响力的设计参数.
- 优化的多PRB设计,使用石灰石,生物炭和D201树脂,显著提高了使用寿命和处理能力,同时降低了成本.
结论:
- 已经建立了一个计算效率高且实用的策略,用于设计用于AMD治疗的多PRB.
- 开发的ML-NSGAII框架为优化复杂的环境修复系统提供了一个强大的工具.
- 这项研究为在酸性矿井排水整治中有效实施多重PRB提供了宝贵的见解.
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