Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology

Advances in Nanohybrid Membranes for Dye Reduction: A Comprehensive Review

Separating valuable materials such as dyes from wastewater using membranes and returning them to the production line is a desirable environmental and economical procedure. However, sometimes, besides filtration, adsorption, and separation processes, pollutant destruction also can be suitable using photocatalytic membranes. The art of producing nanohybrid materials in contrast with nanocomposites encompasses nanomaterial synthesis as a new product with different properties from raw materials for nanohybrids versus the composition of nanomaterials for nanocomposites. According to the findings of this research, confirming proper synthesis of nanohybrid is one challenge that can be overcome by different analyses, other researchers' reports, and the theoretical assessment of physical or chemical reactions. The application of organic-inorganic nanomaterials and frameworks is another challenge that is discussed in the present work. According to the findings, Nanohybrid Membranes (NHMs) can achieve 100% decolorization, but cannot eliminate salts and dyes, although the removal efficiency is notable for some salts, especially divalent salts. Hydrophilicity, antifouling properties, flux, pressure, costs, usage frequency, and mechanical, chemical, and thermal stabilities of NHMs should be considered.

Muscovite clay for methylene blue removal: advanced optimization and Al-guided breakthroughs-an independent application from prior antibiotic removal investigation

This study evaluates the efficacy of muscovite mineral clay as an adsorbent for removing Methylene Blue (MB) from water-based solutions. The research examined the impact of initial MB concentration, adsorbent mass, and time on the MB removal process. Two modeling techniques, namely Box-Behnken design with response surface methodology (BBD-RSM) and Artificial Neural Network (ANN), were employed to accurately predict the MB removal efficiency. The RSM and ANN models yielded satisfactory results in estimating MB removal efficiency. To further enhance the optimization process, conventional and techno-economic methods were implemented. The conventional method aimed to maximize dye removal efficiency (R), while the techno-economic approach incorporated multiple objectives. The comparative analysis demonstrated that the techno-economic optimization method outperformed the conventional method. This study emphasizes the significance of considering multiple objectives and integrating techno-economic factors in optimizing clay adsorption processes. The successful application of the techno-economic optimization approach highlights its potential as a robust optimization method, particularly in the field of wastewater treatment. The findings provide valuable insights for optimizing adsorption and advancing environmental remediation practices.

Modeling of adsorption of Methylene Blue dye on Ho-CaWO4 nanoparticles using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques

The aim of this study is to evaluate the applicability of Ho-CaWO₄ nanoparticles prepared using the hydrothermal method for the removal of Methylene Blue (MB) from aqueous solution using adsorption process. The effects of contact time, Ho-CaWO₄ nanoparticles dose and initial MB concentration on the removal of MB were studied using the central composite design (CCD) method. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling techniques were applied to model the process and their performance and predictive capabilities of the response (removal efficiency) was also examined. The adsorption process was optimized using the RSM and the optimum conditions were determined. The process was also modelled using the adsorption isotherm and kinetic models. The ANN and RSM model showed adequate prediction of the response, with absolute average deviation (AAD) of 0.001 and 0.320 and root mean squared error (RMSE) of 0.119 and 0.993, respectively. The RSM model was found to be more acceptable since it has the lowest RMSE and AAD compared to the ANN model. Optimum MB removal of 71.17% was obtained at pH of 2.03, contact time of 15.16 min, Ho-CaWO₄ nanoparticles dose of 1.91 g/L, and MB concentration of 100.65 mg/L. Maximum adsorption capacity (q_m ) of 103.09 mg/g was obtained. The experimental data of MB adsorption on Ho-CaWO₄ nanoparticles followed the Freundlich isotherm and pseudo-second-order kinetic models than the other models. It could be concluded that the prepared Ho-CaWO₄ nanoparticles can be used efficiently for the removal of MB and also, the process can be optimized to maximize the removal of MB. •Synthesis and characterization of Ho-CaWO₄ nanoparticles.•Modelling and optimization of Methylene Blue removal onto Ho-CaWO₄ using Response Surface Methodology (RSM) and Artificial neural network (ANN).•Evaluation of the isotherm and kinetic parameters of the adsorption process.

Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models

In this research, Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were applied for optimization of Reactive Blue 19 removal using combined electrocoagulation/coagulation process through Multi-Objective Particle Swarm Optimization (MOPSO). By applying RSM, the effects of five independent parameters including applied current, reaction time, initial dye concentration, initial pH and dosage of Poly Aluminum Chloride were studied. According to the RSM results, all the independent parameters are equally important in dye removal efficiency. In addition, ANFIS was applied for dye removal efficiency and operating costs modeling. High R(2) values (≥85%) indicate that the predictions of RSM and ANFIS models are acceptable for both responses. ANFIS was also used in MOPSO for finding the best techno-economical Reactive Blue 19 elimination conditions according to RSM design. Through MOPSO and the selected ANFIS model, Minimum and maximum values of 58.27% and 99.67% dye removal efficiencies were obtained, respectively.