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优化机器学习模型,从废水中去除类固醇激素

Farzaneh Mohammadi¹, Zeinab Yavari², Mohammad Reza Nikoo²

¹Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.

|September 23, 2023

概括

这项研究使用移动床生物膜反应器 (MBBR) 中的人工神经网络 (ANN) 在水生环境中模拟类固醇激素的去除. 优化的ANN模型准确地预测了雌激素 (E1),17β- Estradiol (E2) 和17α-乙烯基 Estradiol (EE2) 的去除.

科学领域:

环境科学环境科学
水处理技术水处理技术
内分泌学在内分泌学.

背景情况:

类固醇激素如雌激素 (E1),17β- Estradiol (E2) 和17α-乙烯基 Estradiol (EE2) 是水生环境中的内分泌干扰污染物.
移动床生物膜反应器 (MBBR) 是一个有前途的技术,用于去除这些持久性有机污染物.
精确的建模对于优化MBBR在类固醇激素消除中的性能至关重要.

研究的目的:

开发和验证人工神经网络 (ANN) 模型,用于预测MBBR系统中E1,E2和EE2的删除效率.
为了确定最大限度地消除类固醇激素和化学氧气需求 (COD) 的最佳操作条件.
为了确定影响MBBR性能的关键输入参数的相对重要性.

主要方法:

使用人工神经网络 (ANN),使用粒子群优化 (PSO) 和莱文伯格-马奎特 (LM) 算法进行训练.
模型使用了五个输入参数:COD加载率,E1,E2,EE2的初始度和液压保留时间 (HRT).
使用皮尔森相关性进行了敏感性分析 (SA),以对输入参数的重要性进行排名.

主要成果:

开发了高精度的ANN模型,实现了0.9978 (LM) 和0.9982 (PSO) 的相关系数 (R).
最佳的传输函数被确定为tan-sigmoid (隐藏层) 和线性 (输出层).
敏感性分析揭示了类固醇激素去除输入参数的相对重要性.

结论:

用PSO和LM算法训练的ANN模型有效预测MBBR系统中的类固醇激素去除.
该研究为优化MBBR操作以消除内分泌破坏性化合物提供了一个强大的框架.
开发的模型和灵敏度分析为针对类固醇污染物的水处理策略提供了宝贵的见解.

关键词:

人工智能的人工智能是人工智能.生物处理生物处理粒子小群优化优化粒子小群优化类固醇激素类激素的使用废水是废水,废水就是废水.

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