An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye

Preparation of Bamboo-Based Activated Carbon via Steam Activation for Efficient Methylene Blue Dye Adsorption: Modeling and Mechanism Studies

Dye pollution has long been an ecological and human health issue. Activated carbon is considered to be the most promising material for dye adsorption. In this study, bamboo was used as a biomass precursor to produce activated carbon with a high specific surface area by the steam activation method. The physical activation reaction between water vapor and bamboo promotes the carbonization product to have a rich porous structure. The prepared activated carbon was investigated from the perspectives of surface morphology, elemental change, surface porosity, and surface functional groups using a variety of techniques. According to the Brunauer-Emmett-Teller analysis, the activated carbon has a high surface area (1273 m2/g) and a mesoporous structure (average pore size 3.1 nm). On this basis, the effect of activated carbon on the removal of methylene blue (MB) dye from aqueous environments was evaluated and optimized by response surface methodology (RSM). Key adsorption parameters include initial MB concentration (150-200 mg/L), adsorption time (5-120 min), adsorbent dosage (30-50 mg), adsorption temperature (5-50 °C), and solution pH (3-11). Box-Behnken design (BBD) was used for modeling and analysis. Kinetic and isotherm model studies show that pseudo-second-order model kinetics and Langmuir isotherm can better describe the process of MB dye adsorption. This study will provide new ideas for the preparation of bamboo-activated carbon and provide a model prediction basis for dye adsorption research.

Development and optimization of geopolymer adsorbent for water treatment: Application of mixture design approach

The current paper refers to the study of a new approach to optimizing the adsorptive properties of geopolymers by varying the aluminosilicate precursors from kaolin (K), metakaolin (MK), and coal fly ash (CFA) as internal synthesis factors. The simplex-augmented-centroid mixture design was applied to identify the optimal formulation from the three aluminosilicate precursors to develop a geopolymer (GP) with a distinctive structure that positively affects its dye adsorption efficiency. The variously formulated GP samples were tested for the removal of both methylene blue (MB-dye) and crystal violet dye (CV-dye) from an aqueous solution. The mathematical-statistical analysis of the experimental readings suggested that the generated special cubic models were significant, and thus the chosen approach was adequate for determining the optimum blending proportion. The optimization tools indicated that the optimal mixture from the three aluminosilicate precursors for developing a GP with high adsorption efficiency was 58% MK, 42% K, and 0% CFA. The optimized geopolymer (GP_O) was synthesized and then analyzed using a variety of physicochemical techniques, which revealed the presence of an amorphous N-A-S-H gel-rich porous structure as an influencing property on the geopolymer's organic dye adsorption efficiency. The dependence of the adsorption mechanism of both MB-dye and CV-dye by GP_O on the adsorbent dosage, contact time, initial dye concentration, temperature, and solution pH was evaluated. The isothermic and kinetic experimental readings for MB and CV-dyes adsorption by GP_O were well fitted to the pseudo-second-order and Freundlich models, with an exothermic, favorable, and spontaneous adsorption reaction thermodynamically. The experimental studies in the lab scale on GP_O produce comparable results. From these results, it has been concluded that the accuracy and feasibility of the mixture design simulation succeeded in optimizing and developing a geopolymeric sorbent material with great potential as an excellent economical agent for removing cationic dyes from aqueous media. This point represents an added value compared to traditional non-optimized geopolymer absorbents. Besides, this geopolymer material represents a significant application possibility for water treatment and remediation of hazardous dye pollutants.

Facile synthesis of multifunctional C@Fe3O4-MoO3-rGO ternary composite and its versatile roles as sonoadsorbent to ameliorate triphenylmethane textile dye and as potential electrode for supercapacitor applications

The toxic wastewater effluents from textile dyes have been a significant environmental threat worldwide in recent decades. Against this backdrop, this study investigates the performance of C@Fe₃O₄-MoO₃-rGO as a sonoadsorbent to ameliorate crystal violet (CV) dye from the aqua matrix and further explores its potential as an electrode in supercapacitor applications. The phase purity, crystal structure, surface morphology, thermal stability and magnetic behaviour characteristics of the composite were studied using various characterisation techniques such as powder X-ray diffraction (XRD), Raman Spectroscopy, Fourier transform infrared spectroscopy (FTIR), Field emission scanning electron microscopy (FESEM), High-resolution transmission electron microscopy (HRTEM), Thermogravimetric analysis (TGA) and Vibrating-sample magnetometry (VSM). From the Langmuir isotherm model, the synthesised sonoadsorbent exhibited a maximum adsorption capacity of 1664.26 mg/g for crystal violet, which is remarkably high. Further, to its inherited magnetic characteristics, the composite can be easily separated from the solution by using an external magnet. Furthermore, the working electrode was synthesised with 80% active material, 10% carbon black, and 10% polyvinylidene difluoride to investigate its suitability in supercapacitor applications. The C@Fe₃O₄-MoO₃-rGO composite exhibited an excellent capacitance value of 180.36 F/g with commendable cycling stability, making it suitable as a potential cathode material for the next generation supercapacitors.

Artificial neural network (ANN) and response surface methodology (RSM) algorithm-based improvement, kinetics and isotherm studies of electrocoagulation of oily wastewater

The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m^-2, 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.