Quantitative structure-property relationship studies on electrochemical degradation of substituted phenols using a support vector machine

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.

Heterogeneous fenton degradation of azo dyes catalyzed by modified polyacrylonitrile fiber fe complexes: QSPR (quantitative structure peorperty relationship) study

The amidoximated polyacrylonitrile (PAN) fiber Fe complexes were prepared and used as the heterogeneous Fenton catalysts for the degradation of 28 anionic water soluble azo dyes in water under visible irradiation. The multiple linear regression (MLR) method was employed to develop the quantitative structure property relationship (QSPR) model equations for the decoloration and mineralization of azo dyes. Moreover, the predictive ability of the QSPR model equations was assessed using Leave-one-out (LOO) and cross-validation (CV) methods. Additionally, the effect of Fe content of catalyst and the sodium chloride in water on QSPR model equations were also investigated. The results indicated that the heterogeneous photo-Fenton degradation of the azo dyes with different structures was conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for the dye decoloration and mineralization were successfully developed using MLR technique. MW/S (molecular weight divided by the number of sulphonate groups) and NN=N (the number of azo linkage) are considered as the most important determining factor for the dye degradation and mineralization, and there is a significant negative correlation between MW/S or NN=N and degradation percentage or total organic carbon (TOC) removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloride did not alter the nature of the QSPR model equations.

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

The half-wave potential (E1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R2=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E1/2, and can be useful in predicting the E1/2 of other compounds.

Prediction of acute mammalian toxicity from QSARs and interspecies correlations

With the ever-growing number of xenobiotics that can potentially contaminate the environment, the determination of their mammalian toxicity is of prime importance. In this context, LD50 tests on rats and mice have been used for a long time to express the relative hazard associated with the acute toxicity of inorganic and organic chemicals. However, these laboratory tests encounter important hurdles. They are costly, time consuming and actively opposed by animal rights activists. Moreover, new legislation policies, such as REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), aim at reducing the use of toxicity tests on vertebrates. Consequently, there is a need to find alternative methods for estimating the acute mammalian toxicity of chemicals. The quantitative structure-activity relationships (QSARs) and interspecies correlations appear particularly suited to reaching this goal. In this context, this paper reviews more than 150 models aiming at predicting rat and mouse LD50 values from molecular descriptors or (and) ecotoxicity data. The interest of these computational tools is discussed.