Development of artificial neural networks software for arsenic adsorption from an aqueous environment

Metformin-modified polyethersulfone magnetic microbeads for effective arsenic removal from apatite soil leachate water

Arsenic is the hazardous species and still is the global challenge in water treatment. Apatite soil is highly rich in arsenic species, and its mining presents various environmental issues. In this study, novel magnetic microbeads as adsorbent were developed for the elimination of hazardous arsenic ions from apatite soil's aqueous leachate before discharging into environment. The microbeads were fabricated with metformin polyether sulfone after being doped with zero-valent iron (Met-PES/ZVI). The microbeads were characterized using various techniques, including FTIR, XRD, SEM-EDX, VSM, and zeta potential analysis. The developed adsorbent demonstrated a significant elimination in arsenic in aqueous leachate, achieving 82.39% removal after 30 min of contact time, which further increased to 90% after 180 min of shaking. The kinetic analysis revealed that the pseudo-second-order model best represented the adsorption process. The intra-particle diffusion model indicated that the adsorption occurred in two steps. The Langmuir model (R2 = 0.991), with a maximum adsorption capacity of 188.679 mg g-1, was discovered to be the best fit for the experimental data as compared Freundlich model (R2 = 0.981). According to the thermodynamic outcome (ΔG < -20 kJ/mol), the adsorption process was spontaneous and involved physisorption. These findings demonstrate the potential of magnetic Met-PES/ZVI microbeads as an efficient adsorbent for the removal of arsenic from apatite soil aqueous leachate.

Thiamethoxam adsorption by ZnCl2 modified cow manure biochar: Mechanism and quantitative prediction

The overuse of thiamethoxam (THM) has threatened the survival of living organisms and it is necessary to find an environmentally friendly material to remove THM frequently detected in water. Biochar prepared from cow manure modified with ZnCl2 (Zn-CBC) was used to remove THM. Compared to the unmodified cow manure biochar (CBC), the removal ratio of THM by Zn-CBC was enhanced 35 times. In the mechanistic analysis, SEM and BET showed that Zn-CBC had a good pore structure and its specific surface area (166.502 m2 g-1) increased to 17 times that of CBC, indicating that Zn-CBC had good pore adsorption properties. The adsorption kinetic and isotherm implied that the main mechanism was chemisorption including π-π interaction and H-bonding. Furthermore, the stable graphitized structure of Zn-CBC allowed for efficient adsorption and reusability. In addition, this study constructed an intelligent prediction model using batch experiment data, and the high R2 (0.978) and low RMSE (0.057) implied that the model could accurately and quantitatively predict the adsorption efficiency. This paper provides a novel perspective to simultaneously remove the neonicotinoid insecticides and realize the resource utilization of cow manure.

A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil

"Save Soil Save Earth" is not just a catchphrase; it is a necessity to protect soil ecosystem from the unwanted and unregulated level of xenobiotic contamination. Numerous challenges such as type, lifespan, nature of pollutants and high cost of treatment has been associated with the treatment or remediation of contaminated soil, whether it be either on-site or off-site. Due to the food chain, the health of non-target soil species as well as human health were impacted by soil contaminants, both organic and inorganic. In this review, the use of microbial omics approaches and artificial intelligence or machine learning has been comprehensively explored with recent advancements in order to identify the sources, characterize, quantify, and mitigate soil pollutants from the environment for increased sustainability. This will generate novel insights into methods for soil remediation that will reduce the time and expense of soil treatment.

New insights into arsenate removal during siderite oxidation by dissolved oxygen

Nowadays, arsenic (As) pollution in aquatic environments severely threatens the health of human beings. Although it has been known that siderite is capable of As adsorption and dissolved oxygen (DO) enhances the adsorption, effects of DO concentrations on As(V) adsorption onto siderite remain elusive. In this study, As(V) removal was investigated by synthesized siderite from aqueous solutions with different DO concentrations. Arsenic(V) adsorption kinetics were conformed to the pseudo-second-order model. As(V) adsorption onto siderite was enhanced in the presence of dissolved oxygen, but the excess DO concentration did not increase As(V) adsorption since Fe(III) oxides were coated onto the pristine siderite surface, preventing the mineral from further oxidation. With the increase in DO concentration, the rate of Fe(II) oxidation decreased, which was the kinetic-limited step during As(V) removal by siderite with the presence of DO. The theoretically generated Fe(III) was stoichiometrically proportional to the consumed oxygen. Microscopic characteristics by means of XRD, SEM, TEM, FTIR and XPS indicated that the adsorption was dominated by the chemical process via the As(V) complexation with siderite and co-precipitation with produced Fe(III) oxides. This study reveals the mechanisms of As(V) adsorption during siderite oxidation under different DO concentrations and emphasizes the importance of siderite oxidation in As(V) fate in aqueous systems.

Applications of soft computing techniques for prediction of pollutant removal by environmentally friendly adsorbents (case study: the nitrate adsorption on modified hydrochar)

Artificial intelligence has emerged as a powerful tool for solving real-world problems in various fields. This study investigates the simulation and prediction of nitrate adsorption from an aqueous solution using modified hydrochar prepared from sugarcane bagasse using an artificial neural network (ANN), support vector machine (SVR), and gene expression programming (GEP). Different parameters, such as the solution pH, adsorbent dosage, contact time, and initial nitrate concentration, were introduced to the models as input variables, and adsorption capacity was the predicted variable. The comparison of artificial intelligence models demonstrated that an ANN with a lower root mean square error (0.001) and higher R² (0.99) value can predict nitrate adsorption onto modified hydrochar of sugarcane bagasse better than other models. In addition, the contact time and initial nitrate concentration revealed a higher correlation between input variables with the adsorption capacity.

Artificial neural network-genetic algorithm-based optimization of aerobic composting process parameters of Ganoderma lucidum residue

The rapid development of traditional Chinese medicine enterprises has put forward higher requirements for the resource utilization of traditional Chinese medicine residues (TCMR). Aerobic composting of TCMR to prepare bio-organic fertilizer is an effective resource utilization method. In this study, a back-propagation artificial neural network (BPNN) model using composting factors as inputs (C/N, initial moisture content, type of inoculant, composting days) and the humic acid content as the output was constructed based on the orthogonal test data. BPNN-GA (a genetic algorithm) was used for extreme value optimization, and the optimal composting process parameter combination was obtained and verified. The results show that the combination of orthogonal testing and BPNN can effectively establish the relationship between the composting process parameters and humic acid content. The R² value was 0. 9064. The optimized parameter combination is as follows: C/N,37.42; moisture content,69.76%; bacteria,no; and composting time,50 d.

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%.