Modeling of Textile Dye Removal from Wastewater Using Innovative Oxidation Technologies (Fe(II)/Chlorine and H₂O₂/Periodate Processes): Artificial Neural Network-Particle Swarm Optimization Hybrid Model

Abstract

An efficient optimization technique based on a metaheuristic and an artificial neural network (ANN) algorithm has been devised. Particle swarm optimization (PSO) and ANN were used to estimate the removal of two textile dyes from wastewater (reactive green 12, RG12, and toluidine blue, TB) using two unique oxidation processes: Fe(II)/chlorine and H 2 O 2 /periodate. A previous study has revealed that operating conditions substantially influence removal efficiency. Data points were gathered for the experimental studies that developed our ANN-PSO model. The PSO was used to determine the optimum ANN parameter values. Based on the two processes tested (Fe(II)/chlorine and H 2 O 2 /periodate), the proposed hybrid model (ANN-PSO) has been demonstrated to be the most successful in terms of establishing the optimal ANN parameters and brilliantly forecasting data for RG12 and TP elimination yield with the coefficient of determination (R2) topped 0.99 for three distinct ratio data sets.

Evaluation of the Potentiality of the H2O2/IO4– Process for the Removal of Acid Orange 10 from the Liquid Phase: Experimental Investigation Coupled with Numerical Analysis Using the Box–Behnken Design

“…The “p-values” are used to identify which aspects of the model are essential (Table S4, p -values lower than 0.05 shows that the model factor is statistically significant); moreover, for each variable, the number of degrees of freedom (Df) is reported. As a consequence, X 1 , X 3 , X 4 , X 1 X 4 , X 3 X 4 , X 1 2 , X 3 2 , X 4 2 , X 1 2 X 3 , X 2 2 X 4 , and X 1 2 X 3 2 are key model terms. − …”

“…2 are key model terms. [70][71][72] represent the degradation of AO10 as a function of (a) the initial solution pH (X 1 ) and the initial H 2 O 2 concentration (X 2 ) and (b) X 1 and initial periodate concentration (X 3 ), as determined according to eq 8. The results show that the optimum values to obtain a 100% AO10 removal were X 1 = 1.05 for initial pH, H 2 O 2 initial concentration X 2 = 65.32 mM, initial periodate concentration X 3 = 1.82 mM, and dye concentration of X 4 = 93.9 mg L −1 .…”

Computational Chemistry as Applied in Environmental Research: Opportunities and Challenges

“…The model forecasts the degradations with high coefficients of determination (R 2 > 0.99). 324 Risk management relies on assessing environmental concentrations and distributions to provide data for pollution removal to meet regulations. 325 ML impacts remediation strategies (adsorption, photodegradation, electrodegradation, among others) 7,25,45,48 and control strategies that focus on improving or maintaining effluent quality, decreasing the operational cost of wastewater treatment processes (WWTP).…”

“…The optimization of pH, temperature, and initial concentrations for the reagents and contaminants in Fe­(II)/chlorine and H 2 O 2 /periodate oxidation processes for the degradation of textile dyes was also achieved by using ML modeling. The model forecasts the degradations with high coefficients of determination (R 2 > 0.99) …”

Methylene Blue Removal Using Modified PGMA Powder as a Low-Cost Sorbent in Batch Mode: Kinetic and Equilibrium Studies

“…Various physical/chemical methods, such as flocculation/coagulation (McYotto et al, 2021), adsorption (Kathing and Saini, 2022;Parthasarathy et al, 2022;Teli and Nadathur, 2018), chemical oxidation (Farzaneh et al, 2020;Fetimi et al, 2022;Mian and Liu, 2018;Nippatla and Philip, 2019;Pinna et al, 2021), ion exchange (Joseph et al, 2020), and biological treatment methods (Kornaros and Lyberatos, 2006), are used for color removal caused by dyestuffs in textile industry wastewater. For example, in flocculation/coagulation, there is a significant amount of sludge formation with high water content (Ren et al, 2022).…”