Modelling and optimization of psychoactive pharmaceutical caffeine removal by electrochemical oxidation process: A comparative study between response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS)

Electrochemical mechanism of synchronous ammonia and nitrate removal based on multi-objective optimization by coupling random forest with genetic algorithm

In this work, an electrochemical system was constructed for the simultaneous elimination of ammonia and nitrate using the prepared Ti foam/SnO2-Sb anode and a Cu foam cathode. The hybrid RF-GA method is proposed as a tool for the analysis and optimization of the simultaneous removal of ammonia and nitrate. The influence of independent variables including NaCl concentration, time, and current densities was studied. Results showed that the random forest (RF) model could successfully predict the behavior of electrochemical systems (R2 = 0.9751, RMSE = 0.4567 for the ammonia prediction model; R2 = 0.9772, RMSE = 0.0436 for the nitrate prediction model). The variable importance measures (VIM) analysis reveals that time has the maximum influence on the degradation rate of ammonia and nitrate. The RF model is used as an objective function for the genetic algorithm (GA) to determine the optimum conditions in combination with the calculated specific energy consumption. Based on the optimization results, the removal rates of ammonia and nitrate reach 94.4 % and 74.7 %, respectively, with a minimum specific energy consumption of 0.181 kwh·g-1. The electrochemical reaction mechanism of the composite pollutants in the Ti foam/SnO2-Sb and Cu foam electrode system is further elucidated. The results indicate that nitrate is reduced to nitrite, ammonia, or nitrogen gas at the cathode, accompanied by the mutual transformation of Cu(0), Cu(I), and Cu(II) on the Cu electrode. Ammonia is oxidized to nitrogen gas or nitrate at the anode. Ultimately, the nitrogen-containing composite pollutant is decomposed and discharged as nitrogen gas by cyclic redox reactions.

Comprehensive understanding of electrochemical treatment systems combined with biological processes for wastewater remediation

The presence of toxic pollutants in wastewater discharge can affect the environment negatively due to presence of the organic and inorganic contaminants. The application of the electrochemical process in wastewater treatment is promising, specifically in treating these harmful pollutants from the aquatic environment. This review focused on recent applications of the electrochemical process for the remediation of such harmful pollutants from aquatic environments. Furthermore, the process conditions that affect the electrochemical process performance are evaluated, and the appropriate treatment processes are suggested according to the presence of organic and inorganic contaminants. Electrocoagulation, electrooxidation, and electro-Fenton applications in wastewater have shown effective performance with high removal rates. The disadvantages of these processes are the formation of toxic intermediate metabolites, high energy consumption, and sludge generation. To overcome such disadvantages combined ecotechnologies can be applied in large-scale wastewater pollutants removal. The combination of electrochemical and biological treatment has gained importance, increased removal performance remarkably, and decreased operational costs. The critical discussion with depth information in this review could be beneficial for wastewater treatment plant operators throughout the world.

Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review

In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40201-022-00835-w.