Modeling and optimization study on sulfamethoxazole degradation by electrochemically activated persulfate process. (1st October 2018)

• Record Type: Journal Article
• Authors: Zhang, Lingling; Ding, Wei; Qiu, Jiantao; Jin, Hui; Ma, Hongkun; Li, Zifu; Cang, Daqiang
• Is Part Of: Journal of cleaner production. Volume 197(2018)Part 1
• Page Start: 297
• Page End: 305
• Publication Date: 2018-10-01
• Subjects: Kinetics -- Response surface method -- Artificial neural networks -- Sulfamethoxazole removal -- Sulfate radical -- Electrochemical oxidation; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.05.267
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: In this work, empirical kinetics, response surface method (RSM) and artificial neural network (ANN) were employed to model and optimize electrochemically activated persulfate process. Electrochemically activated persulfate using a sacrificial iron anode was used as a cost effective method to degrade sulfamethoxazole (SMX) in aqueous solution. The individual and the interactive effects of operation parameters such as pH, applied current, persulfate concentration and electrolysis time were investigated. Eight unseen experiments beyond the experimental design were performed to test the accuracy of the generalization ability of the models. Based on the unseen experiments, ANN demonstrated the superiority in predicting the results when compared to empirical kinetics and RSM modeling. Response surface analysis was used to illustrate the interactions between parameters pH/applied current, pH/persulfate concentration and pH/electrolysis time. Sensitivity analysis indicated that the relative importance of the influencing parameters were of the following order: electrolysis time > pH > applied current > persulfate concentration. The highest degradation efficiency was achieved with the optimal conditions of pH of 3.43, the applied current of 18.4 mA, the persulfate concentration of 3.54 mM, and the electrolysis time of 60 min. The electrical energy consumption was also calculated at this optimized condition (0.04 kWh ⋅ m −1 order −1 ). The work provides a novel predictive and optimized model for SMX removal under different conditions by electrochemically activated persulfate. Highlights: Electrochemically activated persulfate using iron anode was employed to degrade SMX. Empirical kinetics, RSM and ANN were used to model and optimize SMX removal. Relative importance of variables was in the order: time > pH > current > [PDS]. The optimum conditions for degradation were determined. The relationships among parameters affecting SMX degradation were proposed.