Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Innovative optimization for enhancing Pb2+ biosorption from aqueous solutions using Bacillus subtilis

Introduction

Toxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness.

Methods

The lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process.

Results

Five physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir's isotherm and pseudo-second-order model with R2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively.

Discussion

The outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.

Emerging iron-based mesoporous materials for adsorptive removal of pollutants: Mechanism, optimization, challenges, and future perspective

Iron-based materials (IBMs) have shown promise as adsorbents due to their unique physicochemical properties. This review provides an overview of the different types of IBMs, their synthesis methods, and their properties. Results found in the adsorption of emerging contaminants to a wide range of IBMs are discussed. The IBMs used were evaluated in terms of their maximum uptake capacity, with special consideration given to environmental conditions such as contact time, solution pH, initial pollutant concentration, etc. The adsorption mechanisms of pollutants are discussed taking into account the results of kinetic, isotherm, thermodynamic studies, surface complexation modelling (SCM), and available spectroscopic data. A current overview of molecular modeling and simulation studies related to density functional theory (DFT), surface response methodology (RSM), and artificial neural network (ANN) is presented. In addition, the reusability and suitability of IBMs in real wastewater treatment is shown. The review concludes with the strengths and weaknesses of current research and suggests ideas for future research that will improve our ability to remove contaminants from real wastewater streams.

Sequestration of Ni (II), Pb (II), and Zn (II) utilizing biogenic synthesized Fe3O4/CLPC NCs and modified Fe3O4/CLPC@CS NCs: Process optimization, simulation modeling, and feasibility study

The present study reports low-cost novel biogenic magnetite Citrus limetta peels carbon (Fe3O4/CLPC) nanocomposites and modified Fe3O4/CLPC@CS nanocomposites cross-linked with glutaraldehyde and subsequently employed in batch mode sequestration of heavy metals ions. Diverse techniques fully characterized them, and the influence of operating variables on adsorption reactions from aqueous solutions was investigated. The Brunauer, Emmett, and Teller (BET) surface areas of synthesized Fe3O4/CLPC and Fe3O4/CLPC@CS NCs were 53.91 and 32.16 m2/g, while the mesoporous diameters were 7.69 and 7.57 nm, respectively. The Langmuir isotherm and Pseudo second order kinetic were well-fitting and capable of explaining the adsorption reaction. The Langmuir-based monolayer adsorption (qmax) for Fe3O4/CLPC@CS NCs was 82.65, 95.24, and 64.10 mg/g, higher than Fe3O4/CLPC NCs, which were 70.92, 84.75, and 59.17 mg/g for Ni (II), Pb (II), and Zn (II), respectively. Each metal's pseudo second order correlation coefficient (R2 ≥ 0.99) reveals that nanocomposites surface binding functional groups controlled the adsorption rate via chemisorption. Further, thermodynamic results confirm that each studied metal ions' adsorption was spontaneous, endothermic, and characterized by an increase in randomness. In addition to magnetic separability, three ad-desorption cycles yielded exceptional adsorption efficacy and > 93% regenerability. The present study also reveals the effective utilization of Fe3O4/CLPC and Fe3O4/CLPC@CS NCs as cost-effective magnetic separable green adsorbents for heavy metals sequestration from electroplating wastewater.

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.

Development of a New Eco-Friendly Copolymer Based on Chitosan for Enhanced Removal of Pb and Cd from Water

Worldwide, concerns about heavy metal contamination from manmade and natural sources have increased in recent decades. Metals released into the environment threaten human health, mostly due to their integration into the food chain and persistence. Nature offers a large range of materials with different functionalities, providing also a source of inspiration for scientists working in the field of material synthesis. In the current study, a new type of copolymer is introduced, which was synthesized for the first time by combining chitosan and poly(benzofurane-co-arylacetic acid), for use in the adsorption of toxic heavy metals. Such naturally derived materials can be easily and inexpensively synthesized and separated by simple filtration, thus becoming an attractive alternative solution for wastewater treatment. The new copolymer was investigated by solid-state nuclear magnetic resonance, thermogravimetric analysis, scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray photon electron microscopy. Flame atomic absorption spectrometry was utilized to measure heavy metal concentrations in the investigated samples. Equilibrium isotherms, kinetic 3D models, and artificial neural networks were applied to the experimental data to characterize the adsorption process. Additional adsorption experiments were performed using metal-contaminated water samples collected in two seasons (summer and winter) from two former mining areas in Romania (Roșia Montană and Novăț-Borșa). The results demonstrated high (51–97%) adsorption efficiency for Pb and excellent (95–100%) for Cd, afttr testing on stock solutions and contaminated water samples. The recyclability study of the copolymer indicated that the removal efficiency decreased to 89% for Pb and 58% for Cd after seven adsorption–desorption cycles.

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

Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment is essential in order to predict its overall performance and to provide an idea of how to control its operation. This model considered the adsorption process parameters (initial concentration, adsorbent dosage, pH, and residence time) as the input variables and arsenic removal as the only output. The ANN model predicted the adsorption efficiency with high accuracy for both training and testing datasets, when compared with the available response surface methodology (RSM) model. Based on the best model synaptic weights, user-friendly ANN software was created to predict and analyze arsenic removal as a function of adsorption process parameters. We developed various graphical user interfaces (GUI) for easy use of the developed model. Thus, a researcher can efficiently operate the software without an understanding of programming or artificial neural networks. Sensitivity analysis and quantitative estimation were carried out to study the function of adsorption process parameter variables on As(V) removal efficiency, using the GUI of the model. The model prediction shows that the adsorbent dosages, initial concentration, and pH are the most influential parameters. The efficiency was increased as the adsorbent dosages increased, decreasing with initial concentration and pH. The result show that the pH 2.0-5.0 is optimal for adsorbent efficiency (%).

Application of neural network in metal adsorption using biomaterials (BMs): a review

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.