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RSM-ANN-GA框架用于预测建模和优化使用ZnO@SiO₂纳米复合材料的sonocatalytic欧染料降解

G V Aatral¹, V Chitra Devi², S Mothil³

¹Department of Chemical Engineering, Erode Sengunthar Engineering College, Thudupathi, Perundurai, Erode 638 057, Tamil Nadu, India.

Journal of contaminant hydrology

|February 10, 2026

概括

这项研究优化了ZnO@SiO2声催化技术,用于从废水中去除欧黄色染料. 一种混合的人工神经网络 (ANN) 模型在流程优化和染料脱色方面被证明优于响应表面方法 (RSM).

科学领域:

环境化学环境化学
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:

合成染料,如黄,由于其低生物降解性和高水生毒性,造成重大环境风险.
工业废水处理通常难以有效地去除持久有机污染物.

研究的目的:

通过使用ZnO@SiO2催化剂,研究埃黄的声催化降解.
通过评估关键参数和开发预测模型来优化废水处理过程.
为了比较人工神经网络 (ANN) 和响应表面方法 (RSM) 的性能,以优化流程.

主要方法:

使用ZnO@SiO2复合催化剂在不同的超声波频率,pH值,催化剂剂量和初始染料度下进行声催化.
开发和评估一个混合建模框架,结合ANN和RSM,通过贝叶斯优化调整ANN超参数.
使用统计指标 (MAE,RMSE,R2),置信区间和预测区间评估模型性能.

主要成果:

该ZnO@SiO2声催化剂有效地去除了欧黄色染料,并减少了化学氧气需求 (COD).
与RSM相比,10层的ANN模型显示出更高的预测准确度和概括能力.
电解质添加被发现会影响反应动力学,影响着染料去除效率.

结论:

声触媒和计算智能的综合方法为优化染料废水处理提供了一个强大的框架.
开发的ANN模型为预测和控制声触媒降解过程提供了可靠的工具.
这项研究为解决工业废水中复杂染料污染挑战提供了可重复的方法.

关键词:

人工神经网络 (ANN) 是一个人工神经网络.氨酸黄色氨酸黄色这就是Sonocatalysis.污水处理污水处理污水处理在 ZnO@SiO2 纳米复合材料中.

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