Artificial neural network and molecular modeling for assessing the adsorption performance of a hybrid alginate-based magsorbent

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model's R2 value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.

Adsorption of Brilliant Green Dye onto a Mercerized Biosorbent: Kinetic, Thermodynamic, and Molecular Docking Studies

This study reports the valorization of pistachio shell agricultural waste, aiming to develop an eco-friendly and cost-effective biosorbent for cationic brilliant green (BG) dye adsorption from aqueous media. Pistachio shells were mercerized in an alkaline environment, resulting in the treated adsorbent (PS_NaOH). The morphological and structural features of the adsorbent were analyzed using scanning electron microscopy, Fourier transform infrared spectroscopy, and polarized light microscopy. The pseudo-first-order (PFO) kinetic model best described the adsorption kinetics of the BG cationic dye onto PS_NaOH biosorbents. In turn, the equilibrium data were best fitted to the Sips isotherm model. The maximum adsorption capacity decreased with temperature (from 52.42 mg/g at 300 K to 46.42 mg/g at 330 K). The isotherm parameters indicated improved affinity between the biosorbent surface and BG molecules at lower temperatures (300 K). The thermodynamic parameters estimated on the basis of the two approaches indicated a spontaneous (ΔG < 0) and exothermic (ΔH < 0) adsorption process. The design of experiments (DoE) and the response surface methodology (RSM) were employed to establish optimal conditions (sorbent dose (SD) = 4.0 g/L and initial concentration (C₀) = 10.1 mg/L), yielding removal efficiency of 98.78%. Molecular docking simulations were performed to disclose the intermolecular interactions between the BG dye and lignocellulose-based adsorbent.

Application of Surface-Modified Nanoclay in a Hybrid Adsorption-Ultrafiltration Process for Enhanced Nitrite Ions Removal: Chemometric Approach vs. Machine Learning

Herein, we report the results of a study on combining adsorption and ultrafiltration in a single-stage process to remove nitrite ions from contaminated water. As adsorbent, a surface-modified nanoclay was employed (i.e., Nanomer^® I.28E, containing 25-30 wt. % trimethyl stearyl ammonium). Ultrafiltration experiments were conducted using porous polymeric membranes (Ultracel^® 10 kDa). The hybrid process of adsorption-ultrafiltration was modeled and optimized using three computational tools: (1) response surface methodology (RSM), (2) artificial neural network (ANN), and (3) support vector machine (SVM). The optimal conditions provided by machine learning (SVM) were found to be the best, revealing a rejection efficiency of 86.3% and an initial flux of permeate of 185 LMH for a moderate dose of the nanoclay (0.674% w/v). Likewise, a new and more retentive membrane (based on PVDF-HFP copolymer and halloysite (HS) inorganic nanotubes) was produced by the phase-inversion method, characterized by SEM, EDX, AFM, and FTIR techniques, and then tested under optimal conditions. This new composite membrane (PVDF-HFP/HS) with a thickness of 112 μm and a porosity of 75.32% unveiled an enhanced rejection efficiency (95.0%) and a lower initial flux of permeate (28 LMH). Moreover, molecular docking simulations disclosed the intermolecular interactions between nitrite ions and the functional moiety of the organonanoclay.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation

The present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases the process’ self- sustainability. Adsorption experiments with varying fecal ash loadings, initial urine concentrations, and adsorption temperatures were conducted, and the acquired data were applied to determine the adsorption kinetics. These three process parameters and their interactions served as the input vectors for the artificial neural network model, with the percentage urea adsorption onto fecal ash serving as the output. The Levenberg–Marquardt (TRAINLM) and Bayesian regularization (TRAINBR) techniques with mean square error (MSE) were trained and tested for predicting percentage adsorption. TRAINBR was demonstrated in our study to be an ideal match for improving urea adsorption, with an accuracy of R = 0.9982 and a convergence time of seven seconds. The ideal conditions for maximum urea adsorption were determined to be a high starting concentration of 13.5 g.L−1; a low temperature of 30 °C, and a loading of 1.0 g of adsorbent. For urea, the improved settings resulted in maximum adsorption of 92.8%.