人工智能在电化学水处理过程中的应用

doi:10.16085/j.issn.1000-6613.2022-0028

化工进展 ›› 2022, Vol. 41 ›› Issue (S1): 497-506.DOI: 10.16085/j.issn.1000-6613.2022-0028

人工智能在电化学水处理过程中的应用

胡锦文¹(), 孟广源¹^,², 张之杰¹, 张宁¹, 张芯婉¹, 陈鹏¹^,², 李童³, 刘勇弟¹^,², 张乐华¹^,²()

^1.华东理工大学高浓度难降解有机废水处理技术国家工程实验室，上海 200237
^2.华东理工大学国家环境保护化工过程环境风险评价与控制重点实验室，上海 200237
^3.亳州大学继续教育中心，安徽亳州 236800

收稿日期:2022-01-05 修回日期:2022-03-14 出版日期:2022-10-20 发布日期:2022-11-10
通讯作者: 张乐华
作者简介:胡锦文（1998—），男，硕士研究生，研究方向为环境电化学。E-mail：825517503@qq.com。
基金资助:
国家重点研发计划(2019YFC0408202);国家自然科学基金(21876050)

Application of artificial intelligence model in electrochemical water treatment process

HU Jinwen¹(), MENG Guangyuan¹^,², ZHANG Zhijie¹, ZHANG Ning¹, ZHANG Xinwan¹, CHEN Peng¹^,², LI Tong³, LIU Yongdi¹^,², ZHANG Lehua¹^,²()

^1.National Engineering Laboratory for Industrial Wastewater Treatment, East China University of Science and Technology, Shanghai 200237, China
^2.State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, East China University of Science and Technology, Shanghai 200237, China
^3.Continuing Education Center, Bozhou University, Bozhou 236800, Anhui, China

Received:2022-01-05 Revised:2022-03-14 Online:2022-10-20 Published:2022-11-10
Contact: ZHANG Lehua

摘要/Abstract

摘要：

近年来，通过人工智能建立的模型可以对工业过程进行精确调控，人工智能应用于电化学水处理技术过程得到了广泛关注。在电化学水处理过程中，人工智能模型可以降低电化学过程的能耗，获取最优能效比。本文对人工智能在电化学水处理的应用进行了综述、分类和归纳，并介绍了其应用方法，概述了人工智能应用于电化学水处理过程的特点、优势以及局限性，比较了用于电化学水处理的人工智能建模与响应面模型、回归模型和经验动力学模型的优劣。进而提出了人工智能在工程应用上的改进思路，为相关研究提供了参考。

关键词: 电化学, 动态建模, 人工智能, 水处理, 环境工程

Abstract:

In recent years, the model established by artificial intelligence can accurately regulate and control the industrial process, and the application of artificial intelligence in electrochemical water treatment technology has received extensive attention. In the process of electrochemical water treatment, artificial intelligence model can reduce the energy consumption of electrochemical process and obtain the optimal energy efficiency ratio. In this paper, the application of artificial intelligence in electrochemical water treatment is summarized, classified and summarized, and its application methods are introduced. The characteristics, advantages and limitations of artificial intelligence in electrochemical water treatment process are summarized, and the advantages and disadvantages of artificial intelligence modeling, response surface model, regression model and empirical dynamic model used in electrochemical water treatment are compared. Furthermore, this paper puts forward the improvement ideas of artificial intelligence in engineering application, which provides a reference for related research.

Key words: electrochemistry, dynamic modeling, artificial intelligence, waste water treatment, environmental engineering

中图分类号:

胡锦文, 孟广源, 张之杰, 张宁, 张芯婉, 陈鹏, 李童, 刘勇弟, 张乐华. 人工智能在电化学水处理过程中的应用[J]. 化工进展, 2022, 41(S1): 497-506.

HU Jinwen, MENG Guangyuan, ZHANG Zhijie, ZHANG Ning, ZHANG Xinwan, CHEN Peng, LI Tong, LIU Yongdi, ZHANG Lehua. Application of artificial intelligence model in electrochemical water treatment process[J]. Chemical Industry and Engineering Progress, 2022, 41(S1): 497-506.

图/表 7

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输入变量	输出变量	建模方法	数据集数量（占比）			MSE	R²	文献
输入变量	输出变量	建模方法	训练	验证	测试	MSE	R²	文献
pH，过硫酸盐浓度，外加电流，电解时间	磺胺甲唑去除效率	ANN	—	—	—	0.4×10^-4	0.9991	［26］
电流密度，pH，电解时间	硼去除率	BP-ANN	—	—	—	—	0.973	［24］
电流密度，pH，电解质浓度	瑟诺唑红染料去除率	ANN	70%	15%	15%	0.06	0.9999	［2］
NOR（诺氟沙星）浓度，初始pH，电流密度，实验时间	TOC去除率	BP-ANN	—	—	—	—	0.969	［28］
初始pH，电流密度，电解时间	COD去除率	ANN	50	—	—	0.134	0.9974	［29］
反应时间，染料浓度，电解质浓度，电流密度	染料去除率，COD去除率，能量损耗	ANN-PSO	70%	15%	15%	13.64	0.982	［30］
反应时间，电流强度，Fe²⁺剂量，苯酚初始浓度	苯酚去除效率	ANN	60	20	32	—	0.9742	［25］
电流密度，电解持续时间，电解质浓度	COD去除率	BPNN	100	—	24	—	0.998	［3］
pH，外加电流，电解质浓度，臭氧浓度，反应时间	敌草隆去除率	ANN	70%	15%	15%	0.000075	0.9878	［1］
表面活性剂的初始浓度，电流密度，初始pH值，反应时间	表面活性剂去除率、化学需氧量去除率	DNN	64	10	17	—	0.9938	［31］
流出物总固体，总悬浮固体，总溶解固体，浊度，初始化学需氧量，初始pH，电解时间，电极间距离，电流密度	COD	ANN	165	55	55	0.0037	0.9679	［32］
电压，反应时间	Cr(Ⅵ)去除效率、能耗	CCD	60	—	—	0.0242	0.9909	［33］
电流密度，时间，电极类型，pH，初始TC（总大肠杆菌），初始FC（最终大肠杆菌），初始COD，初始EC，初始TDS（总溶解固体）	COD	MLP-ANN	80%	10%	10%	0.225	0.959	［34］
	EC	MLP-ANN	80%	10%	10%	0.366	0.987	［34］
	TDS	MLP-ANN	80%	10%	10%	0.45	0.971	［34］
	TC	MLP-ANN	80%	10%	10%	0.608	0.568	［34］
	FC	MLP-ANN	80%	10%	10%	0.377	0.531	［34］
	COD	SVR（支持向量回归）	80%	10%	10%	0.039	0.977	［34］
	EC	SVR（支持向量回归）	80%	10%	10%	0.09	0.990	［34］
	TDS	SVR（支持向量回归）	80%	10%	10%	0.012	0.988	［34］
	TC	SVR（支持向量回归）	80%	10%	10%	0.2	0.920	［34］
	FC	SVR（支持向量回归）	80%	10%	10%	0.03	0.932	［34］
脱色时间，初始pH，电流，初始燃料浓度，初始Fe³⁺	染料去除百分比	DNN	76	26	26	—	0.986	［35］
电解时间，初始pH，外加电流，初始染料浓度	脱色效率	ANN	70	24	23	—	0.9713	［36］
初始透明质酸浓度，初始pH，电导率，电流密度，脉冲数	透明质酸去除率	GFF（广义前馈）	60%	20%	20%	0.003	0.984	［37］
电解质的性质，浓度，溶液的初始pH，电流强度，反应时间	OTC降解效率	ANN	—	—	—	—	0.99	［27］
温度，pH，初始COD，电流密度，电荷，氯苯酚化合物类型，硝基苯酚化合物类型	COD	MLP	378	42	—	—	0.9998	［38］
初始铜浓度，pH，电极间电压，处理时间	铜去除效率，能耗	ANN	202	—	—	0.571	0.982	［39］
反应时间，初始pH，外加电流，流速，初始FeCl₃浓度，初始DR23（Direct Red 23）浓度	DR23的除色效率	ANN	115	38	37	—	0.958	［32］

输入变量	输出变量	建模方法	数据集数量（占比）			MSE	R²	文献
输入变量	输出变量	建模方法	训练	验证	测试	MSE	R²	文献
pH，过硫酸盐浓度，外加电流，电解时间	磺胺甲唑去除效率	ANN	—	—	—	0.4×10^-4	0.9991	［26］
电流密度，pH，电解时间	硼去除率	BP-ANN	—	—	—	—	0.973	［24］
电流密度，pH，电解质浓度	瑟诺唑红染料去除率	ANN	70%	15%	15%	0.06	0.9999	［2］
NOR（诺氟沙星）浓度，初始pH，电流密度，实验时间	TOC去除率	BP-ANN	—	—	—	—	0.969	［28］
初始pH，电流密度，电解时间	COD去除率	ANN	50	—	—	0.134	0.9974	［29］
反应时间，染料浓度，电解质浓度，电流密度	染料去除率，COD去除率，能量损耗	ANN-PSO	70%	15%	15%	13.64	0.982	［30］
反应时间，电流强度，Fe²⁺剂量，苯酚初始浓度	苯酚去除效率	ANN	60	20	32	—	0.9742	［25］
电流密度，电解持续时间，电解质浓度	COD去除率	BPNN	100	—	24	—	0.998	［3］
pH，外加电流，电解质浓度，臭氧浓度，反应时间	敌草隆去除率	ANN	70%	15%	15%	0.000075	0.9878	［1］
表面活性剂的初始浓度，电流密度，初始pH值，反应时间	表面活性剂去除率、化学需氧量去除率	DNN	64	10	17	—	0.9938	［31］
流出物总固体，总悬浮固体，总溶解固体，浊度，初始化学需氧量，初始pH，电解时间，电极间距离，电流密度	COD	ANN	165	55	55	0.0037	0.9679	［32］
电压，反应时间	Cr(Ⅵ)去除效率、能耗	CCD	60	—	—	0.0242	0.9909	［33］
电流密度，时间，电极类型，pH，初始TC（总大肠杆菌），初始FC（最终大肠杆菌），初始COD，初始EC，初始TDS（总溶解固体）	COD	MLP-ANN	80%	10%	10%	0.225	0.959	［34］
	EC	MLP-ANN	80%	10%	10%	0.366	0.987	［34］
	TDS	MLP-ANN	80%	10%	10%	0.45	0.971	［34］
	TC	MLP-ANN	80%	10%	10%	0.608	0.568	［34］
	FC	MLP-ANN	80%	10%	10%	0.377	0.531	［34］
	COD	SVR（支持向量回归）	80%	10%	10%	0.039	0.977	［34］
	EC	SVR（支持向量回归）	80%	10%	10%	0.09	0.990	［34］
	TDS	SVR（支持向量回归）	80%	10%	10%	0.012	0.988	［34］
	TC	SVR（支持向量回归）	80%	10%	10%	0.2	0.920	［34］
	FC	SVR（支持向量回归）	80%	10%	10%	0.03	0.932	［34］
脱色时间，初始pH，电流，初始燃料浓度，初始Fe³⁺	染料去除百分比	DNN	76	26	26	—	0.986	［35］
电解时间，初始pH，外加电流，初始染料浓度	脱色效率	ANN	70	24	23	—	0.9713	［36］
初始透明质酸浓度，初始pH，电导率，电流密度，脉冲数	透明质酸去除率	GFF（广义前馈）	60%	20%	20%	0.003	0.984	［37］
电解质的性质，浓度，溶液的初始pH，电流强度，反应时间	OTC降解效率	ANN	—	—	—	—	0.99	［27］
温度，pH，初始COD，电流密度，电荷，氯苯酚化合物类型，硝基苯酚化合物类型	COD	MLP	378	42	—	—	0.9998	［38］
初始铜浓度，pH，电极间电压，处理时间	铜去除效率，能耗	ANN	202	—	—	0.571	0.982	［39］
反应时间，初始pH，外加电流，流速，初始FeCl₃浓度，初始DR23（Direct Red 23）浓度	DR23的除色效率	ANN	115	38	37	—	0.958	［32］

输入变量	输出变量	人工智能模型	响应面模型	回归模型	经验动力学模型	参考文献
pH，过硫酸盐浓度，外加电流，电解时间	磺胺甲唑去除效率	0.9991	0.9841	—	0.8751	［26］
电流密度，pH，电解质浓度	瑟诺唑红染料去除率	0.9999	0.954	—	0.87	［2］
初始pH，电流密度，电解时间	COD去除率	0.9742	—	0.9525	—	［25］
pH，电流密度，电凝时间	COD去除率	0.9974	0.9605	—	—	［29］
pH，外加电流，电解质浓度，臭氧浓度，反应时间	敌草隆去除率	0.9878	0.9977	—	—	［1］
表面活性剂的初始浓度，电流密度，初始pH，反应时间	表面活性剂去除率、化学需氧量去除率	0.9938	—	0.9384	—	［32］
电压，反应时间	Cr(Ⅵ)去除效率、能耗	0.9909	0.99	—	—	［34］
到达ORP谷的反应时间（min），DO上升点的时间（min），ORP谷的ORP值（mV）	所需Fe²⁺浓度的，COD去除效率	0.9944	—	0.9563	—	［46］

输入变量	输出变量	人工智能模型	响应面模型	回归模型	经验动力学模型	参考文献
pH，过硫酸盐浓度，外加电流，电解时间	磺胺甲唑去除效率	0.9991	0.9841	—	0.8751	［26］
电流密度，pH，电解质浓度	瑟诺唑红染料去除率	0.9999	0.954	—	0.87	［2］
初始pH，电流密度，电解时间	COD去除率	0.9742	—	0.9525	—	［25］
pH，电流密度，电凝时间	COD去除率	0.9974	0.9605	—	—	［29］
pH，外加电流，电解质浓度，臭氧浓度，反应时间	敌草隆去除率	0.9878	0.9977	—	—	［1］
表面活性剂的初始浓度，电流密度，初始pH，反应时间	表面活性剂去除率、化学需氧量去除率	0.9938	—	0.9384	—	［32］
电压，反应时间	Cr(Ⅵ)去除效率、能耗	0.9909	0.99	—	—	［34］
到达ORP谷的反应时间（min），DO上升点的时间（min），ORP谷的ORP值（mV）	所需Fe²⁺浓度的，COD去除效率	0.9944	—	0.9563	—	［46］

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人工智能在电化学水处理过程中的应用

Application of artificial intelligence model in electrochemical water treatment process

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