Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

Liu, Banghai; Chen, Jin; Wan, Jiteng; Li, Pengfang; Yan, Huanxi

doi:10.2298/jsc170721101l

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…The R 2 values indicated that the variable range of independent variables could explain 82.53% and 96.20 % of the changes in the corresponding COPs yield by RSM and ANN models, respectively. Compared with RSM model, the superiority of ANN model has been previously confirmed by many reports [37] , [38] . Therefore, ANN modeling method was selected to optimize the subsequent polysaccharides process in this study.…”

Section: Resultsmentioning

confidence: 74%

Ultrasound assisted aqueous two-phase extraction of polysaccharides from Cornus officinalis fruit: Modeling, optimization, purification, and characterization

Tan

Cui

et al. 2022

Ultrasonics Sonochemistry

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Section: Resultsmentioning

confidence: 74%

Ultrasound assisted aqueous two-phase extraction of polysaccharides from Cornus officinalis fruit: Modeling, optimization, purification, and characterization

Tan

Cui

et al. 2022

Ultrasonics Sonochemistry

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“…8) of the standardized residual of the model is subject to normal distribution, which confirms that the ANN model has high accuracy to predict results. 38 Fig. 9, the maximum removal efficiency was obtained after 100 generations.…”

Section: Ann Modelling and Optimization Of Parametersmentioning

confidence: 88%

Electrochemical oxidation of sulfamethoxazole using Ti/SnO2-Sb/Co-PbO2 electrode through ANN-PSO

et al. 2019

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Even in a trace amounts, the presence of antibiotics in aqueous solution is getting more and more attention. Accordingly, appropriate technologies are needed to efficiently remove these compounds from aqueous environments. In this study, we have examined the electrochemical oxidation (EO) of sulfamethoxazole (SMX) on a Co modified PbO 2 electrode. The process of EO of SMX in aqueous solution followed the pseudo-first-order kinetics, and the removal efficiency of SMX reached the maximum value of 95.1 % within 60 min. The effects of major factors on SMX oxidation kinetics were studied in detail by single-factor experiments, namely current density (1-20 mA cm-2), solution pH value (2-10), initial concentration of SMX (10-500 mg L-1) and concentration of electrolytes (0.05-0.4 mol L-1). An artificial neural network (ANN) model was used to simulate this EO process. Based on the obtained model, particle swarm optimization (PSO) was used to optimize the operating parameters. The maximum removal efficiency of SMX was obtained at the optimized conditions (e.g., current density of 12.37 mA cm-2 , initial pH value of 4.78, initial SMX concentration of 74.45 mg L-1 , electrolyte concentration of 0.24 mol L-1 and electrolysis time of 51.49 min). The validation results indicated that this method can ideally be used to optimize the related parameters and predict the anticipated results with acceptable accuracy.

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“…Support Vector Regression SVR is a regression technique rooted in Vapnik’s concept of support vector. SVR is a kernel-based technique with a solid theoretical basis and is generally easier to implement than artificial neural networks, its major competitor (Rupp 2015 ).SVR is a supervised-learning machine characterized by using Kernel functions, the number of support vectors, penalty parameters C, coefficient of non-sensitivity ε, and the Gaussian nucleus bandwidth σ (Liu et al 2018 ). These parameters can be manually defined or by the mean of heuristic optimization algorithms.…”

Section: Regression Methodsmentioning

confidence: 99%

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Madani

Benkortbi

Laidi

2023

Braz. J. Chem. Eng.

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Continuous effort is dedicated to clinically and computationally discovering potential drugs for the novel coronavirus-2. Computer-Aided Drug Design CADD is the backbone of drug discovery, and shifting to computational approaches has become necessary. Quantitative Structure–Activity Relationship QSAR is a widely used approach in predicting the activity of potential molecules and is an early step in drug discovery. 3-chymotrypsin-like-proteinase 3CLpro is a highly conserved enzyme in the coronaviruses characterized by its role in the viral replication cycle. Despite the existence of various vaccines, the development of a new drug for SARS-CoV-2 is a necessity to provide cures to patients. In the pursuit of exploring new potential 3CLpro SARS-CoV-2 inhibitors and contributing to the existing literature, this work opted to build and compare three models of QSAR to correlate between the molecules’ structure and their activity: IC 50 through the application of Multiple Linear Regression(MLR), Support Vector Regression(SVR), and Particle Swarm Optimization-SVR algorithms (PSO-SVR). The database contains 71 novel derivatives of ML300which have proven nanomolar activity against the 3CLpro enzyme, and the GA algorithm obtained the representative descriptors. The built models were plotted and compared following various internal and external validation criteria, and applicability domains for each model were determined. The results demonstrated that the PSO-SVR model performed best in predictive ability and robustness, followed by SVR and MLR. These results also suggest that the branching degree 6 had a strong negative impact, while the moment of inertia X/Z ratio, the fraction of rotatable bonds, autocorrelation ATSm2, Keirshape2, and weighted path of length 2 positively impacted the activity. These outcomes prove that the PSO-SVR model is robust and concrete and paves the way for its prediction abilities for future screening of more significant inhibitors' datasets. Supplementary Information The online version contains supplementary material available at 10.1007/s43153-023-00332-z.

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Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

Cited by 7 publications

References 24 publications

Ultrasound assisted aqueous two-phase extraction of polysaccharides from Cornus officinalis fruit: Modeling, optimization, purification, and characterization

Ultrasound assisted aqueous two-phase extraction of polysaccharides from Cornus officinalis fruit: Modeling, optimization, purification, and characterization

Electrochemical oxidation of sulfamethoxazole using Ti/SnO2-Sb/Co-PbO2 electrode through ANN-PSO

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

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