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基于响应表面方法和人工神经网络的nZVI激活硫酸盐的有机污染物降解的建模和优化研究:作为模型污染物的案例研究

Moye Luo^1,2, Xiaodong Zhang², Tao Long²

¹Department of Environmental Science and Engineering, School of Energy and Environment, Southeast University, Nanjing, China.

Frontiers in chemistry

|November 6, 2023

概括

结合响应表面方法 (RSM) 和人工神经网络 (ANN) 的数学模型有效地预测了地下水中纳米零价值铁 (nZVI) 激活硫酸盐 (PS) 的二烯降解. 这种方法有助于优化有机污染物的修复策略.

科学领域:

环境化学环境化学
水资源整治技术的应用.
化学工程是化学工程的重要组成部分.

背景情况:

地下水中的有机污染物带来了复杂的运输和反应性挑战.
有效的整治规划需要准确的数学模型来预测污染物的行为.
纳米零价值铁 (nZVI) 激活硫酸盐 (PS) 是一种有前途的现场化学氧化系统.

研究的目的:

开发和验证用于预测降解的RSM-ANN-动力建模方法.
优化针对地下水污染物的nZVI激活PS系统的过程参数.
了解在优化条件下氧化过程的动力学.

主要方法:

响应表面方法 (RSM) 和人工神经网络 (ANN) 的合用于过程建模.
批量实验以在优化条件下确定化学氧化动力学.
应用伪二次 (PSO) 动力模型来描述二烯降解.

主要成果:

与RSM (R2 = 0.948) 相比,ANN模型 (R2 = 0.980) 显示出与RSM (R2 = 0.948) 相比,二降解的预测准确度更高.
优化的条件预测了1.45mMPS的完全氧化,PS/nZVI比率为 4:1,pH为3和21.9°C.
烯降解遵循2型和3型伪二阶动力学 (R2 > 0.999).

结论:

综合的RSM-ANN-动力建模方法为预测污染物降解提供了一个强大的框架.
这种方法支持对地下水中的有机污染物进行有效的现场整治规划.
该研究强调了nZVI激活的PS在有效地治理地下水方面的潜力.

关键词:

活性化硫酸盐是什么人工神经网络的人工神经网络二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯二烯运动学的动力学.响应表面方法的方法.

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