响应曲面法和人工神经网络模型对电催化氧化磺胺甲恶唑条件的优化
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摘要
为获得电催化氧化磺胺甲恶唑的最佳实验条件,通过中心组合设计(CCD)设计实验,建立响应曲面(RSM)和人工神经网络(ANN)两种模型,并评价了两种模型的准确性和预测能力。在此基础上分别通过响应曲面法和遗传算法对所得模型进行优化。结果表明:RSM和ANN的均方误差(MSE)、平均绝对误差(MAE)和决定系数(R~2)分别为2.62、 1.13、 0.976和0.59、 0.62、 0.994,说明ANN模型比RSM模型具有更高拟合度、精度和预测能力。响应曲面法和遗传算法的优化结果与实验所得结果的相对误差分别为1.48%和0.74%,说明遗传算法具有更强的优化能力。本研究结果可为优化电催化氧化抗生素废水的条件提供参考。
In order to obtain the best experimental conditions for electrocatalytic oxidation of sulfamethoxazole, two models of response surface(RSM) and artificial neural network(ANN) were established through CCD design experiments.In addition, the accuracy and predictability of the two models were evaluated.On this basis, the resulting model was optimized by response surface methodology and genetic algorithm. The results show that the MSE, MAE, and R~2 of RSM and ANN are 2.62, 1.13, 0.976, and 0.59, 0.62, and 0.994, respectively, indicating that the ANN model has higherdegreeof fitting, precision, and predictability than the RSM model. The relative errors of the experimental results obtained by the response surface method and genetic algorithm are 1.48% and 0.74%, respectively, indicating that the genetic algorithm has a stronger optimization ability. The results of this study can provide reference for optimizing electrocatalytic oxidation of antibiotic wastewater.
In order to obtain the best experimental conditions for electrocatalytic oxidation of sulfamethoxazole, two models of response surface(RSM) and artificial neural network(ANN) were established through CCD design experiments.In addition, the accuracy and predictability of the two models were evaluated.On this basis, the resulting model was optimized by response surface methodology and genetic algorithm. The results show that the MSE, MAE, and R~2 of RSM and ANN are 2.62, 1.13, 0.976, and 0.59, 0.62, and 0.994, respectively, indicating that the ANN model has higherdegreeof fitting, precision, and predictability than the RSM model. The relative errors of the experimental results obtained by the response surface method and genetic algorithm are 1.48% and 0.74%, respectively, indicating that the genetic algorithm has a stronger optimization ability. The results of this study can provide reference for optimizing electrocatalytic oxidation of antibiotic wastewater.
引文
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[2] Ching-Hua Huang,Renew J E,Smeby K L,et al.Assessment of potential antibiotic contaminants in water and preliminary occurrence analysis[J].Journal of Contemporary Water Research & Education,2001,120(1):30-40.
[3] Bu Q,Wang B,Huang J,et al.Estimating the use of antibiotics for humans across China[J].Chemosphere,2016,144:1384.
[4] Jiang M X,Wang L H,Rong J.Biotic and abiotic degradation of four cephalosporin antibiotics in a lake surface water and sediment[J].Chemosphere,2010,80(11):1399-1405.
[5] 张昱,唐妹,田哲,等.制药废水中抗生素的去除技术研究进展[J].环境工程学报,2018,12(1):1-14.Zhang Y,Tang M,Tian Z,et al.Research progress of removal technology of antibiotics from antibiotic production wastewater[J].Chinese Journal of Environmental Engineering,2018,12(1):1-14.
[6] 孙子为,归谈纯,高乃云,等.高级氧化技术降解水体中抗生素的研究进展[J].四川环境,2014,33(5):146-153.Sun Z W,Gui T C,Gao N Y,et al.Research progress on degradation of antibiotics Via advanced oxidation processes[J].Sichuan Environment,2014,33(5):146-153.
[7] Wang Y,Shen C,Zhang M,et al.The electrochemical degradation of ciprofloxacin using a SnO2 -Sb/Ti anode:Influencing factors,reaction pathways and energy demand[J].Chemical Engineering Journal,2016,296:79-89.
[8] Wu J,Zhang H,Oturan N,et al.Application of response surface methodology to the removal of the antibiotic tetracycline by electrochemical process using carbon-felt cathode and DSA (Ti/RuO2-IrO2) anode[J].Chemosphere,2012,87(6):614-620.
[9] 李虹雨,王可,邢璇,等.响应曲面法优化新型电化学系统深度处理垃圾渗滤液生化出水[J].环境化学,2018,37(4):713-719.Li H Y,Wang K,Xing X,et al.Landfill leachate treatment by novel electrochemical system:Optimization of experimental conditions by Response Surface Methodology[J].Environmental Chemistry,2018,37(4):713-719.
[10] Vahidian H R,Ali Reza Soleymani,Parsa J B.Development of a four-layered ANN for simulation of an electrochemical water treatment process[J].Desalination & Water Treatment,2015,56(2):388-398.
[11] Fan M,Hu J,Cao R,et al.Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO[J].Sci Rep,2017,7(1):18040.
[12] Jiang B,Zhang F,Sun Y,et al.Modeling and optimization for curing of polymer flooding using an artificial neural network and a genetic algorithm[J].Journal of the Taiwan Institute of Chemical Engineers,2014,45(5):2217-2224.
[13] Dai Q,Zhou J,Weng M,et al.Electrochemical oxidation metronidazole with Co modified PbO2,electrode:Degradation and mechanism[J].Separation & Purification Technology,2016,166:109-116.
[14] Aghaeinejad-Meybodi A,Ebadi A,Shafiei S,et al.Modeling and optimization of antidepressant drug Fluoxetine removal in aqueous media by ozone/H2O2,process:Comparison of central composite design and artificial neural network approaches[J].Journal of the Taiwan Institute of Chemical Engineers,2015,48:40-48.
[15] Estahbanati M R K,Feilizadeh M,Iliuta M C.Photocatalytic valorization of glycerol to hydrogen:Optimization of operating parameters by artificial neural network[J].Applied Catalysis B Environmental,2017,209:483-492.
[16] 高爱舫,李爱国,张莹,等.基于响应曲面法的US/UV/Fenton联合处理抗生素制药废水优化[J].安全与环境学报,2014,14(3):180-183.Gao A F,Li A G,Zhang Y,et al.Application of response surface methodology for optimization of US/UV/Fenton combination process for antibiotic pharmaceutical wastewater[J].Journal of Safety and Environment,2014,14(3):180-183.
[17] Li J,Liu Q,Ji Q Q,et al.Degradation of p-nitrophenol (PNP) in aqueous solution by Fe 0-PM-PS system through response surface methodology (RSM)[J].Applied Catalysis B Environmental,2017,200:633-646.
[18] 王栗,岳琳,王开红,等.响应曲面法优化电化学氧化技术处理染料废水[J].环境工程学报,2014,8(3):990-996.Wang L,Yue L,Wang K H,et al.Optimization of electrochemical oxidation of dye wastewater using response surface methodology[J].Chinese Journal of Environmental Engineering,2014,8(3):990-996.
[19] Zhang Y,Pan B.Modeling batch and column phosphate removal by hydrated ferric oxide-based nanocomposite using response surface methodology and artificial neural network[J].Chemical Engineering Journal,2014,249:111-120.
[20] Liu B,Jin C,Wan J,et al.Modeling and optimizing an electrochemical oxidation process using artificial neural network,genetic algorithm,and particle swarm optimization[J].Journal of the Serbian Chemical Society,2018,83(3):379-390.
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