Intensified removal of Malachite green by AgOH-AC nanoparticles combined with ultrasound: Modeling and optimization

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Polyvinyl Alcohol Assisted Iron-Zinc Nanocomposite for Enhanced Optimized Rapid Removal of Malachite Green Dye

Eliminating hazardous contaminants is a necessity for maintaining a healthy environment on Earth. This work used a sustainable method to create Iron-Zinc nanocomposites with polyvinyl alcohol assistance. Mentha Piperita (mint leaf) extract was used as a reductant in the green synthesis of bimetallic nanocomposites. Doping with Poly Vinyl Alcohol (PVA) caused a reduction in crystallite size and greater lattice parameters. XRD, FTIR, EDS, and SEM techniques were used to establish their surface morphology and structural characterization. The high-performance nanocomposites were used to remove malachite green (MG) dye using the ultrasonic adsorption technique. Adsorption experiments were designed by central composite design and optimized by response surface methodology. According to this study, 77.87% of the dye was removed at the optimum optimized parameters (10.0 mg L^-1 was the concentration of MG dye at a time of 8.0 min, pH 9.0, and 0.02 g of adsorbent amount) with adsorption capacity up to 92.59 mg·g^-1. The dye adsorption followed Freundlich's isotherm model and the pseudo-second-order kinetic model. Thermodynamic analysis affirmed the spontaneous nature of adsorption due to negative ΔG^o values. As a result, the suggested approach offers a framework for creating an effective and affordable technique to remove the dye from a simulated wastewater system for environmental conservation.

Efficient removal of levofloxacin by a magnetic NiFe-LDH/N-MWCNTs nanocomposite: Characterization, response surface methodology, and mechanism

Antibiotic pollutants in water bodies, was studied to remove using an oxidized, nitrogen-doped, and Fe₃O₄ and NiFe-LDH decorated MWCNT (magnetic NiFe-LDH/N-MWCNTs) nanocomposite (NC). The novel, engineered NC was characterized by different techniques of SEM, XRD, TEM, EDX, and XPS and then examined under different main effective parameters of NC dose, levofloxacin (LVX) concentration, pH, time, and temprature. The experimentally obtained data then evaluated using the modeling approaches of RSM, GRNN, and ANFIS. The as prepared adsorbent showed an excellent adsorption performance (removal efficiency = 95.28% and adsorption capacity = 344.83-454.55 mg/g) under the respective values of the mentioned parameters of 0.152 g, 23.01 mg/L, 12.00 min, and 37.5 °C, respectively. The comparison of the models showed that although all of them accurately predicted the removal efficiency, ANFIS presented the best capability with R², RMSE, MSE, MAE, as well as AAD of 0.9998, 0.0082, -0.0004, 0.0069, 0.1322, respectively. The adsorption by the NC followed Freundlich isotherm (R² = 0.9993) and PSO kinetic (>0.998) models, confirming a heterogenous chemisorption process. The thermodynamic parameters showed an endothermic and spontaneous nature for LVX removal by magnetic NiFe-LDH/N-MWCNTs NC. A high-performance efficiency, appropriate reusability (five times without loss of efficiency), as well as easy separation due to magnetic properties, makes the NC to a promising option in removing LVX from water.

Synthesis, characterization, and application of graphene oxide/layered double hydroxide /poly acrylic acid nanocomposite (LDH-rGO-PAA NC) for tetracycline removal: A comprehensive chemometric study

Tetracycline (TC), as the second produced and used antibiotic worldwide, is difficult to be entirely metabolized not only in the body, but also in the treatment processes of water and/or wastewater. Therefore, special attention needs to be paid on defining or developing new options for removing such contaminant. Herein, a reduced graphene oxide (GO) was integrated with Ni-Al layered double hydroxide (LDH) as well poly acrylic acid (LDH-rGO-PAA) and examined to reduce TC -as a model antibiotic-in water media under different operational parameters of TC initial concentration, pH, NC dose, and time. The governed behaviour in the adsorption process was investigated using three model methods of response surface methodology (RSM), artificial neural networks (ANN), and general regression neural network (GRNN) after confirming the physico-chemical properties of LDH-rGO-PAA nanocomposite (NC) using different techniques. The LDH-rGO-PAA NC displayed a good performance as either removal efficiency (R = 94.87 ± 0.25%) or adsorption capacity (q_e = 887.5 mg/g) with the respective values of 110 mg/L, 6.3, 20 mg, and 18.50 min for the mentioned factors (TC initial concentration, pH, NC dose, and time, respectively), which was higher than that of reported for the similar adsorbents until now.

Regulation pore size distribution for facilitating malachite green removal on carbon foam

Malachite green (MG) is widely used as a textile dye and an aquacultural biocide, and become a serious pollution of drink water, but effectually isolating and removing it from wastewater are still a challenge. Here we report a new strategy to prepare a carbon foam with tunable pore size distribution by a one-pot lava foam process. We find that uniform micropore size is beneficial to the formation of C-OH coordination on the pore surface, increasing MG adsorption rates via H⁺ ionization. As a result, carbon foam with uniform pore size distribution demonstrates an optimum MG removal efficiency of 1812 mg g^-1 and a higher partition coefficient of 3.02 mg g^-1 μM^-1, which is twice that of carbon foams with irregular pore size distribution. The adsorption of MG onto these adsorbents was found to be an endothermic monolayer chemical adsorption process, and the Gibbs free energy of adsorption process was decreased obviously by regulating micropore size distribution. The experiment results are in good agreement with pseudo-second-order kinetic and Langmuir isotherm models. Revealed the pore size distribution was the critical factor of MG removal by carbon foam. It should be and inspiration for the design and development of highly efficiency adsorbents for dyes removal.

Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence

Mesoporous Mn-doped Fe nanoparticle-modified reduced graphene oxide (Mn-doped Fe/rGO) was prepared through a one-step co-precipitation method, which was then used to eliminate ethyl violet (EV) in wastewater. The prepared Mn-doped Fe/rGO was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, high-resolution transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, N2-sorption, small angle X-ray diffraction and superconducting quantum interference device. The Brunauer–Emmett–Teller specific surface area of Mn-doped Fe/rGO composites was 104.088 m2/g. The EV elimination by Mn-doped Fe/rGO was modeled and optimized by artificial intelligence (AI) models (i.e., radial basis function network, random forest, artificial neural network genetic algorithm (ANN-GA) and particle swarm optimization). Among these AI models, ANN-GA is considered as the best model for predicting the removal efficiency of EV by Mn-doped Fe/rGO. The evaluation of variables shows that dosage gives the maximum importance to Mn-doped Fe/rGO removal of EV. The experimental data were fitted to kinetics and adsorption isotherm models. The results indicated that the process of EV removal by Mn-doped Fe/rGO obeyed the pseudo-second-order kinetics model and Langmuir isotherm, and the maximum adsorption capacity was 1000.00 mg/g. This study provides a possibility for synthesis of Mn-doped Fe/rGO by co-precipitation as an excellent material for EV removal from the aqueous phase.

Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: Modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN)

Because antibiotic-containing wastewaters are able to contaminate all environmental matrices (e.g. water bodies, soil, etc.), a special attention should be paid on developing appropriate materials for their remediation. Herein, the novel nanocomposite (NC) of Fe₃O₄-g-CN@PEI-β-CD was synthesized and employed effectively for the adsorptive removal of tetracycline (TC), the second most produced and employed antibiotic around the world. The successful fabrication of the nanocomposite with a high specific surface area (57.12 m²/g) was confirmed using XRD, SEM, TEM, FTIR, TGA, EDX, and BET analyses. The Fe₃O₄-g-CN@PEI-β-CD NC exhibited fast adsorption rates towards TC and maximum adsorption capacity on the basis of the Langmuir model reached 833.33 mg g^-1, much higher than that reported by different carbon- and/or nano-based materials. The adsorption process was modeled using the approaches of central composite design (CCD), boosted regression tree (BRT), and general regression neural network (GRNN) under various operational conditions of initial TC concentration, pH, adsorbent dose, tempreature, and time. The comparison of the models indicated good predictions of all, however, the BRT model was more accurate compared to the others, with R² = 0.9992, RMSE = 0.0026, MAE = 0.0014, and AAD = 0.0028, proving that it is a powerful approach for modeling TC adsorption by Fe₃O₄-g-CN@PEI-β-CD nanocomposite. The results showed that the order of the variables' effectiveness is as follow: pH > dose > TC concentration. The high adsorption capacity along with high efficiency (98 % in the optimized conditions by GA) ensures the potential of the as-prepared nanocomposite for in situ remediation of antibiotic-containing wastewaters.

Long term impact of surfactants & polymers on the colloidal stability, aggregation and dissolution of silver nanoparticles

Cetyl trimethyl ammonium bromide (CTAB), Tween 20, polyvinyl pyrrolidone (PVP) and polyethylene glycol (PEG) are among the commonly used surfactants and polymers to stabilize silver nanoparticles (AgNPs). However, their interactions with AgNPs are different. The impact of these surfactants and polymers on the colloidal stability of freshly synthesized uncoated AgNPs was evaluated through a series of long-term experiments and analyzed in terms of their physical and chemical behavior. The cationic surfactant, CTAB was able to produce a mono modal particle size distribution in a prolonged period without affecting the dissolution. In the presence of Tween 20, a non-ionic surfactant, dissolution was promoted in the long run and the particles were preserved with minimal aggregation. In the presence of the polymers, PVP and PEG, the particle structure was not affected even though dissolution was observed. This study presents important insights on the interactions of AgNPs with surfactants and polymers, which could significantly affect the transformations and fate of AgNPs in the aquatic environment.

Modeling and optimization of V2O5/TiO2 nanocatalysts for NH3-Selective catalytic reduction (SCR) of NOx by RSM and ANN techniques

In the present study, two statistical methods including the response surface method (RSM) and artificial neural network (ANN), were employed for modeling and optimization of selective catalytic reduction of NOx with NH₃ (NH₃-SCR) over V₂O₅/TiO₂ nanocatalysts. The relationship between catalyst preparation variables, such as metal loading, impregnation temperature, and calcination temperature on NO conversion were investigated. The R² value of 0.9898 was obtained for quadratic a RSM model, which proves the high agreement of the model with the experimental data. The results of Pareto analysis revealed that three factors including calcination temperature, V loading, and impregnation temperature have a considerable impact on the response. Deduced from the established RSM model, the order of influence on the NO conversion was as follows: calcination followed by V loading and impregnation temperature. The optimum condition of catalyst preparation for maximum NO conversion over V₂O₅/TiO₂ nanocatalysts was predicted to be at 0.0051 mol of V loading, an impregnation temperature of 50 °C and a calcination temperature of 491 °C. Moreover, an ANN model was created by a feed-forward back propagation network (with the topology 4, 12 and 1) to model the relation between the selected catalyst preparation variables and NH₃-SCR process temperature. The R² values for training, validation as well as test sets, were 0.99, 0.9810 and 0.9733. These high values proved the accuracy of the AAN model in modeling and estimating the NO conversion over V₂O₅/TiO₂ nanocatalysts. According to the ANN model, the relative significance of each variable on NO conversion is calcination temperature, process temperature loading, and impregnation temperature from high to low importance, respectively, corroborating the obtained results from RSM.

Ultrasound-assisted sorption of Pb(ii) on multi-walled carbon nanotube in presence of natural organic matter: an insight into main and interaction effects using modelling approaches of RSM and BRT

In real-scale applications, where NPs are injected into the aqueous environment for remediation, they may interact with natural organic matter (NOM). This interaction can alter nanoparticles' (NPs) physicochemical properties, sorption behavior, and even ecological effects. This study aimed to investigate sorption of Pb(ii) onto multi-walled carbon nanotube (MWCNT) in presence of NOM. The predominant behavior of the process was examined comparatively using response surface methodology (RSM) and boosted regression tree (BRT)-based models. The influence of four main effective parameters, namely Pb(ii) and humic acid (HA) concentrations (mg L⁻¹), pH, and time (min) on Pb removal (%) was evaluated by contributing factor importance rankings (BRT) and analysis of variance (RSM). The applicability of the BRT and RSM models for description of the predominant behavior in the design space was checked and compared using statistics of absolute average deviation (AAD), mean absolute error (MAE), root mean square error (RMSE), and multiple correlation coefficient (R²). The results showed that although both approaches exhibited good performance, the BRT model was more precise, indicating that it could be a powerful method for the modeling of NOM-presence studies. Importance rankings of BRT displayed that the effectiveness order of the studied parameters is pH > time > Pb(ii) concentration > HA concentration. Although HA concentration showed the least effect in comparison with three other studied parameters theoretically, the experimental results revealed that Pb(ii) removal is enhanced in presence of HA (73% vs. 81.77%), which was confirmed by SEM/EDX analyses. Hence, maximum removal (R% = 81.77) was attained at an initial Pb(ii) concentration of 9.91 mg L⁻¹, HA concentration of 0.3 mg L⁻¹, pH of 4.9, and time of 55.2 min.

The proposed mechanism for effect of HA on Pb(ii) removal using MWCNTs.

Sonochemistry: a good, fast and clean method to promote the removal of Cu(ii) and Cr(vi) by MWCNT/CoFe2O4@PEI nanocomposites: optimization study

In this study, branched polyethylenimine (PEI) loaded on magnetic multiwalled carbon nanotubes (MWCNT/CoFe₂O₄) was synthesized and characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis and Fourier transform infrared spectroscopy (FTIR).