化工进展 ›› 2022, Vol. 41 ›› Issue (S1): 497-506.DOI: 10.16085/j.issn.1000-6613.2022-0028
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()
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:
HU Jinwen1(), MENG Guangyuan1,2, ZHANG Zhijie1, ZHANG Ning1, ZHANG Xinwan1, CHEN Peng1,2, LI Tong3, LIU Yongdi1,2, ZHANG Lehua1,2()
Received:
2022-01-05
Revised:
2022-03-14
Online:
2022-10-20
Published:
2022-11-10
Contact:
ZHANG Lehua
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。
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.
输入变量 | 输出变量 | 建模方法 | 数据集数量(占比) | MSE | R2 | 文献 | ||
---|---|---|---|---|---|---|---|---|
训练 | 验证 | 测试 | ||||||
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 | [ |
电流密度,pH,电解时间 | 硼去除率 | BP-ANN | — | — | — | — | 0.973 | [ |
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 | [ |
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 | [ |
初始pH,电流密度,电解时间 | COD去除率 | ANN | 50 | — | — | 0.134 | 0.9974 | [ |
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 | [ |
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 | [ |
电流密度,电解持续时间,电解质浓度 | COD去除率 | BPNN | 100 | — | 24 | — | 0.998 | [ |
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 | [ |
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 | [ |
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 | [ |
电压,反应时间 | Cr(Ⅵ)去除效率、能耗 | CCD | 60 | — | — | 0.0242 | 0.9909 | [ |
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 | [ |
EC | MLP-ANN | 80% | 10% | 10% | 0.366 | 0.987 | [ | |
TDS | MLP-ANN | 80% | 10% | 10% | 0.45 | 0.971 | [ | |
TC | MLP-ANN | 80% | 10% | 10% | 0.608 | 0.568 | [ | |
FC | MLP-ANN | 80% | 10% | 10% | 0.377 | 0.531 | [ | |
COD | SVR(支持向量回归) | 80% | 10% | 10% | 0.039 | 0.977 | [ | |
EC | SVR(支持向量回归) | 80% | 10% | 10% | 0.09 | 0.990 | [ | |
TDS | SVR(支持向量回归) | 80% | 10% | 10% | 0.012 | 0.988 | [ | |
TC | SVR(支持向量回归) | 80% | 10% | 10% | 0.2 | 0.920 | [ | |
FC | SVR(支持向量回归) | 80% | 10% | 10% | 0.03 | 0.932 | [ | |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
表1 人工智能模型在电化学水处理技术中的应用
输入变量 | 输出变量 | 建模方法 | 数据集数量(占比) | MSE | R2 | 文献 | ||
---|---|---|---|---|---|---|---|---|
训练 | 验证 | 测试 | ||||||
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 | [ |
电流密度,pH,电解时间 | 硼去除率 | BP-ANN | — | — | — | — | 0.973 | [ |
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 | [ |
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 | [ |
初始pH,电流密度,电解时间 | COD去除率 | ANN | 50 | — | — | 0.134 | 0.9974 | [ |
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 | [ |
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 | [ |
电流密度,电解持续时间,电解质浓度 | COD去除率 | BPNN | 100 | — | 24 | — | 0.998 | [ |
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 | [ |
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 | [ |
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 | [ |
电压,反应时间 | Cr(Ⅵ)去除效率、能耗 | CCD | 60 | — | — | 0.0242 | 0.9909 | [ |
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 | [ |
EC | MLP-ANN | 80% | 10% | 10% | 0.366 | 0.987 | [ | |
TDS | MLP-ANN | 80% | 10% | 10% | 0.45 | 0.971 | [ | |
TC | MLP-ANN | 80% | 10% | 10% | 0.608 | 0.568 | [ | |
FC | MLP-ANN | 80% | 10% | 10% | 0.377 | 0.531 | [ | |
COD | SVR(支持向量回归) | 80% | 10% | 10% | 0.039 | 0.977 | [ | |
EC | SVR(支持向量回归) | 80% | 10% | 10% | 0.09 | 0.990 | [ | |
TDS | SVR(支持向量回归) | 80% | 10% | 10% | 0.012 | 0.988 | [ | |
TC | SVR(支持向量回归) | 80% | 10% | 10% | 0.2 | 0.920 | [ | |
FC | SVR(支持向量回归) | 80% | 10% | 10% | 0.03 | 0.932 | [ | |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
初始pH,电流密度,电解时间 | COD去除率 | 0.9742 | — | 0.9525 | — | [ |
pH,电流密度,电凝时间 | COD去除率 | 0.9974 | 0.9605 | — | — | [ |
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 | — | — | [ |
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 | — | [ |
电压,反应时间 | Cr(Ⅵ)去除效率、能耗 | 0.9909 | 0.99 | — | — | [ |
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 | — | [ |
表2 模型回归系数(R2)对比
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 | [ |
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 | [ |
初始pH,电流密度,电解时间 | COD去除率 | 0.9742 | — | 0.9525 | — | [ |
pH,电流密度,电凝时间 | COD去除率 | 0.9974 | 0.9605 | — | — | [ |
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 | — | — | [ |
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 | — | [ |
电压,反应时间 | Cr(Ⅵ)去除效率、能耗 | 0.9909 | 0.99 | — | — | [ |
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 | — | [ |
1 | ASGARI Ghorban, Abdolmotaleb SEID-MOHAMMADI, RAHMANI Alireza, et al. Diuron degradation using three-dimensional electro-peroxone (3D/E-peroxone) process in the presence of TiO2/GAC: Application for real wastewater and optimization using RSM-CCD and ANN-GA approaches[J]. Chemosphere, 2021, 266: 129179. |
2 | KHAN Saad Ullah, KHAN Hammad, ANWAR Sajid, et al. Computational and statistical modeling for parameters optimization of electrochemical decontamination of synozol red dye wastewater[J]. Chemosphere, 2020, 253: 126673. |
3 | Ahmed BASHA C, SOLOMAN P A, VELAN M, et al. Electrochemical degradation of specialty chemical industry effluent[J]. Journal of Hazardous Materials, 2010, 176(1/2/3): 154-164. |
4 | FAN Mingyi, HU Jiwei, CAO Rensheng, et al. A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence[J]. Chemosphere, 2018, 200: 330-343. |
5 | SINGH Anil Kumar, BILAL Muhammad, IQBAL Hafiz M N, et al. Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook[J]. The Science of the Total Environment, 2021, 770: 144561. |
6 | HEJABI Nasim, SAGHEBIAN Seyed Mahdi, AALAMI Mohammad Taghi, et al. Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study)[J]. Water Science and Technology, 2021, 83(7): 1633-1648. |
7 | PICOS-BENÍTEZ Alain R, LÓPEZ-HINCAPIÉ Juan D, CHÁVEZ-RAMÍREZ Abraham U, et al. Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment[J]. Water Science and Technology, 2017, 75(5/6): 1351-1361. |
8 | NAGHIBI Seyyed Ahmad, SALEHI Ehsan, KHAJAVIAN Mohammad, et al. Multivariate data-based optimization of membrane adsorption process for wastewater treatment: multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system[J]. Chemosphere, 2021, 267: 129268. |
9 | WANG Zifei, MAN Yi, HU Yusha, et al. A deep learning based dynamic COD prediction model for urban sewage[J]. Environmental Science: Water Research & Technology, 2019, 5(12): 2210-2218. |
10 | ZENG Wenru, GUO Zhiwei, WANG Jianhui, et al. A convolutional neural network-based prediction mechanism for sewage treatment[J]. IOP Conference Series: Materials Science and Engineering, 2020, 790(1): 012151. |
11 | WANG Qi, DOU Chang, XIN Changchun, et al. Monitoring of membrane integrity based on electrical measurement and deep learning[J]. IEEE Sensors Journal, 2021, 21(6): 8020-8029. |
12 | SATOH Hisashi, KASHIMOTO Yukari, TAKAHASHI Naoki, et al. Deep learning-based morphology classification of activated sludge flocs in wastewater treatment plants[J]. Environmental Science: Water Research & Technology, 2021, 7(2): 298-305. |
13 | OULEBSIR Rafik, LEFKIR Abdelouahab, SAFRI Abdelhamid, et al. Optimization of the energy consumption in activated sludge process using deep learning selective modeling[J]. Biomass and Bioenergy, 2020, 132: 105420. |
14 | HWANGBO Soonho, Resul AL, CHEN Xueming, et al. Integrated model for understanding N2O emissions from wastewater treatment plants: a deep learning approach[J]. Environmental Science & Technology, 2021, 55(3): 2143-2151. |
15 | NARAYANA P L, MAURYA A K, WANG Xiaosong, et al. Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass[J]. Environmental Research, 2021, 199: 111370. |
16 | SHI Yaoke, WANG Zhiwen, DU Xianjun, et al. Recent advances in the prediction of fouling in membrane bioreactors[J]. Membranes, 2021, 11(6): 381. |
17 | ZHANG Liu, CUI Binhua, YUAN Buxian, et al. Denitrification mechanism and artificial neural networks modeling for low-pollution water purification using a denitrification biological filter process[J]. Separation and Purification Technology, 2021, 257: 117918-. |
18 | FACCHINI Francesco, RANIERI Luigi, VITTI Micaela. A neural network model for decision-making with application in sewage sludge management[J]. Applied Sciences, 2021, 11(12): 5434. |
19 | FU Z, CHENG J, YANG M, BATISTA J, et al. Prediction of industrial wastewater quality parameters based on wavelet de-noised ANFIS Model[C]//Las Vegas, Paper presented at the 8th IEEE Annual Computing and Communication Workshop and Conference (CCWC), 2018: 301-306. |
20 | TAHERI Ensiyeh, AMIN Mohammad Mehdi, FATEHIZADEH Ali, et al. Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production[J]. Journal of Environmental Management, 2021, 292: 112759. |
21 | RAMACHANDRAN Anjali, RUSTUM Rabee, ADELOYE Adebayo J. Review of anaerobic digestion modeling and optimization using nature-inspired techniques[J]. Processes, 2019, 7(12): 953. |
22 | SUO Guanyu, LEI Jing, CHEN Liangehao, et al. Corrosion prediction model of circulating water in refinery unit based on PCA-PSO-BP[C]//2021 IEEE Asia Conference on Information Engineering (ACIE). January 29-31, 2021. Sanya, China. IEEE, 2021: 60-64. |
23 | HUSSAIN Sajjad, KHAN Hammad, Saima GUL, et al. Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study[J]. Chemosphere, 2021, 276: 130151. |
24 | SILVA RIBEIRO Thiago DA, GROSSI Caroline Dias, MERMA Antonio Gutiérrez, et al. Removal of boron from mining wastewaters by electrocoagulation method: Modelling experimental data using artificial neural networks[J]. Minerals Engineering, 2019, 131: 8-13. |
25 | RADWAN Mahmoud, ALALM Mohamed GAR, ELETRIBY Hisham. Optimization and modeling of electro-Fenton process for treatment of phenolic wastewater using nickel and sacrificial stainless steel anodes[J]. Journal of Water Process Engineering, 2018, 22: 155-162. |
26 | ZHANG Lingling, DING Wei, QIU Jiantao, et al. Modeling and optimization study on sulfamethoxazole degradation by electrochemically activated persulfate process[J]. Journal of Cleaner Production, 2018, 197: 297-305. |
27 | BELKACEM Sarah, BOUAFIA Souad, CHABANI Malika. Study of oxytetracycline degradation by means of anodic oxidation process using platinized titanium (Ti/Pt) anode and modeling by artificial neural networks[J]. Process Safety and Environmental Protection, 2017, 111: 170-179. |
28 | YU Han, ZHANG Zhuang, ZHANG Linus, et al. Improved Norfloxacin degradation by urea precipitation Ti/SnO2–Sb anode under photo-electro catalysis and kinetics investigation by BP-neural-network-physical modeling[J]. Journal of Cleaner Production, 2021, 280: 124412. |
29 | Juan Morales-Rivera, Belkis Sulbarán-Rangel, Gurubel-Tun Joel Kelly, et al. Modeling and optimization of COD removal from cold meat industry wastewater by electrocoagulation using computational techniques[J]. Processes, 2020, 8(9): 1139. |
30 | VIANA Danilo F, SALAZAR-BANDA Giancarlo R, LEITE Manuela S. Electrochemical degradation of Reactive Black 5 with surface response and artificial neural networks optimization models[J]. Separation Science and Technology, 2018, 53(16): 2647-2661. |
31 | RADWAN M, ALALM M GAR, ELETRIBY H. Optimization and modeling of electro-Fenton process for treatment of phenolic wastewater using nickel and sacrificial stainless steel anodes[J]. Journal of Water Process Engineering, 2018, 22: 155-162. |
32 | YU Naichuan, LU Xinyu, SONG Fei, et al. Electrocatalytic degradation of sulfamethazine on IrO2-RuO2 composite electrodes: Influencing factors, kinetics and modeling[J]. Journal of Environmental Chemical Engineering, 2021, 9(4): 105301 . |
33 | VALENTE G F S, MENDONÇA R C S, PEREIRA J A M, et al. Artificial neural network prediction of chemical oxygen demand in dairy industry effluent treated by electrocoagulation[J]. Separation and Purification Technology, 2014, 132: 627-633. |
34 | BHATTI Manpreet S, REDDY Akepati S, KALIA Rajeev K, et al. Modeling and optimization of voltage and treatment time for electrocoagulation removal of hexavalent chromium[J]. Desalination, 2011, 269(1/2/3): 157-162. |
35 | CURTEANU Silvia, GODINI Kazem, PIULEAC Ciprian G, et al. Electro-oxidation method applied for activated sludge treatment: Experiment and simulation based on supervised machine learning methods[J]. Industrial & Engineering Chemistry Research, 2014, 53(12): 4902-4912. |
36 | ZAREI M, KHATAEE A R, ORDIKHANI-SEYEDLAR R, et al. Photoelectro-Fenton combined with photocatalytic process for degradation of an azo dye using supported TiO2 nanoparticles and carbon nanotube cathode: Neural network modeling[J]. Electrochimica Acta, 2010, 55(24): 7259-7265. |
37 | SALARI Darioush, NIAEI Aligoli, KHATAEE Alireza, et al. Electrochemical treatment of dye solution containing C.I. Basic Yellow 2 by the peroxi-coagulation method and modeling of experimental results by artificial neural networks[J]. Journal of Electroanalytical Chemistry, 2009, 629(1/2): 117-125. |
38 | HASANI Gona, DARAEI Hiua, SHAHMORADI Behzad, et al. A novel ANN approach for modeling of alternating pulse current electrocoagulation-flotation (APC-ECF) process: Humic acid removal from aqueous media[J]. Process Safety and Environmental Protection, 2018, 117: 111-124. |
39 | PIULEAC C G, RODRIGO M A, CAÑIZARES P, et al. Ten steps modeling of electrolysis processes by using neural networks[J]. Environmental Modelling and Software, 2010, 25(1): 74-81. |
40 | BHATTI Manpreet S, KAPOOR Dhriti, KALIA Rajeev K, et al. RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: Multi objective optimization using genetic algorithm approach[J]. Desalination, 2011, 274(1/2/3): 74-80. |
41 | KHATAEE A R, VAHID B, BEHJATI B, et al. Treatment of a dye solution using photoelectro-Fenton process on the cathode containing carbon nanotubes under recirculation mode: Investigation of operational parameters and artificial neural network modeling[J]. Environmental Progress & Sustainable Energy, 2013, 32(3): 557-563. |
42 | MALEKI Nasim, KASHANIAN Soheila, MALEKI Erfan, et al. A novel enzyme based biosensor for catechol detection in water samples using artificial neural network[J]. Biochemical Engineering Journal, 2017, 128: 1-11. |
43 | VALENTE G F S, MENDONÇA R C S, PEREIRA J A M, et al. Artificial neural network prediction of chemical oxygen demand in dairy industry effluent treated by electrocoagulation[J]. Separation and Purification Technology, 2014, 132: 627-633. |
44 | WANG Jiade, ZHANG Tian, MEI Yu, et al. Treatment of reverse-osmosis concentrate of printing and dyeing wastewater by electro-oxidation process with controlled oxidation-reduction potential (ORP)[J]. Chemosphere, 2018, 201: 621-626. |
45 | KUDR Jiri, NGUYEN Hoai Viet, GUMULEC Jaromir, et al. Simultaneous automatic electrochemical detection of zinc, cadmium, copper and lead ions in environmental samples using a thin-film mercury electrode and an artificial neural network[J]. Sensors (Basel, Switzerland), 2014, 15(1): 592-610. |
46 | YU Rueyfang, LIN Chuanghung, CHEN Howen, et al. Possible control approaches of the Electro-Fenton process for textile wastewater treatment using on-line monitoring of DO and ORP[J]. Chemical Engineering Journal, 2012, 218: 341-349. |
47 | YU H, ZHANG Z, ZHANG L, et al. Improved Norfloxacin degradation by urea precipitation Ti/SnO2–Sb anode under photo-electro catalysis and kinetics investigation by BP-neural-network-physical modeling[J]. Journal of Cleaner Production, 2021, 280(1): 124412. |
48 | ADEOGUN Abideen Idowu, BHAGAWATI P B, SHIVAYOGIMATH C B. Pollutants removals and energy consumption in electrochemical cell for pulping processes wastewater treatment: Artificial neural network, response surface methodology and kinetic studies[J]. Journal of Environmental Management, 2021, 281: 111897. |
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