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通过金属有机框架复合吸收剂从废水中去除重金属 (Cu2+):基于模拟的人工神经网络和响应表面方法

Feng Han¹, Ahmad Saeed Hessen², Abdelfattah Amari³

¹The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.

Environmental research

|December 23, 2023

概括

金属有机框架 (MOF) 纳米复合材料有效地从废水中去除铜 (Cu2+). 响应表面方法 (RSM) 和人工神经网络 (ANN) 准确地建模和优化了这种重金属去除过程.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.

背景情况:

基于金属有机框架 (MOF) 的复合材料越来越多地用于污染物吸附.
重金属污染,特别是铜 (Cu2+),对水体构成重大风险.
开发有效的方法来从废水中去除重金属对于环境保护至关重要.

研究的目的:

模拟,预测和优化使用MOF纳米复合材料从废水中去除重金属离子 (Cu2+).
评估人工神经网络 (ANN) 和响应表面方法 (RSM) 对吸附过程的预测能力.
确定最大限度地提高Cu2+去除效率的最佳条件.

主要方法:

利用响应表面方法 (RSM) 进行建模和优化.
采用人工神经网络 (ANN) 算法来预测吸附效率.
研究了时间,吸附剂剂量和pH对Cu2+去除的影响.

主要成果:

RSM和ANN都准确地预测了Cu2+去除效率 (RE).
在150分钟,0.008g的吸附剂剂量和pH值为6时,达到最大的RE.
一个ANN模型显示高R2值 (0.9992-0.9996),表明出色的预测准确度.

结论:

MOF纳米复合材料有效地从废水中去除Cu2+.
RSM和ANN是模拟和优化重金属吸附的可靠工具.
确定了Cu2+去除的最佳条件,为实际应用铺平了道路.

关键词:

人工神经网络的人工神经网络重金属是一种重金属.金属有机框架的框架.建模建模模型是什么响应表面的方法响应表面方法污水处理污水处理污水处理

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