A rapid and efficient sono-chemistry process for removal of pollutant: Statistical modeling study

Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning

A prediction model based on XGBoost is proposed for ultrasonic degradation of micropollutants' kinetic constants. After parameter optimization through iteration, the model achieves Evaluation metrics with R2 and SMAPE reaching 0.99 and 2.06%, respectively. The impact of design parameters on predicting kinetic constants for ultrasound degradation of trace pollutants was assessed using Shapley additive explanations (SHAP). Results indicate that power density and frequency significantly impact the predictive performance. The database was sorted based on power density and frequency values. Subsequently, 800 raw data were split into small databases of 200 each. After confirming that reducing the database size doesn't affect prediction accuracy, ultrasound degradation experiments were conducted for five pollutants, yielding experimental data. A small database with experimental conditions within the numerical range was selected. Data meeting both feature conditions were filtered, resulting in an optimized 60-data group. After incorporating experimental data, a model was trained for prediction. Degradation kinetic constants for experiments (kE) were compared with predicted constants (for 800 data-based model: kP-800 and for 60 data-based model: kP-60). Results showed ibuprofen, bisphenol A, carbamazepine, and 17β-Estradiol performed better on the 60-data group (kP-60/kE: 1.00, 0.99, 1.00, 1.00), while caffeine suited the model trained on the 800-data group (kP-800/kE: 1.02).

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.

Development of a magnetic nanocomposite sorbent (NiCoMn/Fe3O4@C) for efficient extraction of methylene blue and Auramine O

A rapid and efficient method for the simultaneous monitoring and recovery of Auramine O (AO) and Methylene Blue (MB) dyes from water samples is presented. This method, named ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMN-μSPE), utilizes NiCoMn/Fe3O4@C composite sorbents. Response surface methodology (RSM) combined with artificial neural networks (ANN) and generalized regression artificial neural network (GRNN) under central composite design (CCD) was employed to optimize various parameters for efficient extraction, followed by further refinement using desirability function analysis (DFA) and genetic algorithms (GA). Under optimized conditions, the method achieved exceptional recovery rates (99.5 ± 1.2% for AO and 99.8 ± 1.1% for MB) with acetone as the eluent. Additionally, a high preconcentration factor of 45.50 and 47.30 for AO and MB, respectively, was obtained. Low detection limits of 0.45 ng mL⁻1 (AO) and 1.80 ng mL⁻1 (MB) were achieved with wide linear response ranges (5-1000 and 5-2000 ng mL⁻1 for AO and MB, respectively). The method exhibited good stability with RSDs below 3% for five recycling runs, and minimal interference from various ions was observed. This UA-DMN-μSPE-UV/Vis method offers significant advantages in terms of efficiency, preconcentration, and detection limits, making it a valuable tool for the analysis of AO and MB in water samples.

Efficient removal of ciprofloxacin and ofloxacin from aqueous solutions using a novel nano-scale adsorbent: Modeling, optimization, and characterization

In the fascinating realm of water purification, our study unveils the remarkable potential of a cutting-edge nano-scale adsorbent-combining graphene oxide (GO), chitosan (CS), and polydopamine (PDA)-in efficiently remove ciprofloxacin (CPF) and ofloxacin (OFL) from aqueous solutions. Our exploration delves deep into the adsorbent's character, utilizing a range of analytical techniques including SEM, RAMAN, FTIR, TGA, BET, XRD, and Zeta potential analyses provided insights into the adsorbent's properties. Modeling the adsorption process with Response Surface Methodology (RSM), Artificial Neural Network (ANN) and General Regression Neural Network (GRNN) indicated excellent predictions by GRNN, with RMSE = 0.0200 and 0.0166, MAE = 0.0082 and 0.0092, as well as AAD = 0.0002 and 0.0006, highlighting its modeling power. Optimization using genetic algorithm (GA) revealed maximum CPF removal efficiency of approximately 95.20% under pH = 6.3, sonication time = 9.0 min, adsorbent dosage = 2.10 g L⁻1, temperature = 45 °C and initial CPF concentration = 90.0 mg L⁻1. Similarly, OFL removal reached about 95.50% under pH = 6.30, sonication time = 8.0 min, adsorbent dosage = 2.0 g L⁻1, temperature = 45 °C and OFL concentration = 115.0 mg L⁻1. RSM optimization closely aligned with GA results. Pseudo-second-order (PSO) kinetic model and Langmuir isotherm model best fitted the experimental data for both antibiotics. Thermodynamic analysis indicated a favorable and spontaneous adsorption process for CPF and OFL. The study concludes that the proposed adsorbents show effectiveness in removing CPF and OFL at lower doses and shorter sonication times compared to various reported adsorbents.

Double ionic liquid reinforced g-CN nanocomposite for an enhanced adsorption of methylparaben: Mechanism, modeling, and optimization

The efficient removal of organic pollutants, especially pharmaceuticals, from aquatic environments has attracted great attentions. Application of green, multipurpose, and inexpensive compounds is being extensively favorite as adsorbent instead of the traditional chemicals or materials. In this study, sulfonated graphitic carbon nitride was modified with two ionic liquids of polyethyleneimine and choline chloride to create a novel nanocomposite (Sg-CN@IL2 NC) and to use for removal of methylparaben (MeP) from aqueous media. After confirmation of the successful synthesized using different methods, the effective parameters for MeP removal, such as initial MeP concentration, adsorbent dose, sonication time, and temperature, as well as their interactions, were experimentally examined and modeled using response surface methodology (RSM), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The models were then optimized using desirability function analysis (DF) and genetic algorithm (GA). The results showed that MeP adsorption: a) can be explained more accurate and reliable using GRNN (AARD% = 11.67, MAE = 15.31, RAE % = 45.42, RRSE % = 55.18, MSE = 435.86, RMSE = 20.70, and R2 = 0.995) than the others; b) reached equilibrium within 7.0 min with a maximum uptake of 267.2 mg/g at a temperature of 45 °C and a neutral pH; c) followed from Freundlich (R2 = 0.999) isotherm and PSO kinetic (R2 = 0.95) models; d) is endothermic and spontaneous; e) is mainly due to π-π stacking, electrostatic and hydrogen bonding interactions. Moreover, Sg-CN@IL2 NC showed an appropriate reusability for up to five cycles. These findings demonstrate the potential of as-prepared NC as an excellent adsorbent for removal of MeP from aqueous media.

β-cyclodextrin grafted multi-walled carbon nanotubes/chitosan (MWCNT/Cs/CD) nanocomposite for treatment of methylene blue-containing aqueous solutions

β-cyclodextrin (CD) was grafted with multi-walled carbon nanotubes/chitosan (MWCNTs/Cs) to obtain MWCNTs/Cs/CD nanocomposite (NC) for methylene blue (MB) adsorption from aqueous media. TEM, XRD, TGA, Raman spectra, and BET & BJH analyses were utilized to characterize and confirm the successful synthesis of as-prepared NC. MB capture was investigated by considering the parameters of pH (1.9-9.0), temperature (∼16-63 °C), sonication time (∼5-15 min), MB concentration (∼1.2-48 mg/L), and NC dose (0.03-0.26 mg). The obtained responses were then modelled using CCD, generalized regression neural network (GRNN), and least squares support vector machine (LS-SVM), of which the latter found to provide most reliable and accurate results (RMSE = 0.0235, MAE = 0.020, AAD = 0.0047, and R2 = 0.999). Moreover, the genetic algorithm-based optimization results showed that under the respective values of 7.05, 45.5 °C, 10 min, 23 mg/L, 0.12 g, MWCNTs/Cs/CD NC would be able to remove 96.75% of MB with an adsorption capacity of 603 mg/g, through different mechanisms mainly electrostatic interactions. Following from Dubinin-Radushkevich (D-R) isotherm (qs = 460.66 ± 8.9 and R2 > 0.99) and intraparticle diffusion kinetic (R2 = 0.75-0.90) models indicated a chemical adsorption mechanism. Besides, thermodynamic parameters (ΔH◦ = -66.9 kJ/mol, ΔG◦ = between -3.77 kJ/mol and -8.52 kJ/mol, and ΔS◦ = 237.1818 J/mol K) confirmed an endothermic and spontaneous nature for the adsorption. These findings along with appropriate recyclability (five times), turn the as prepared NC to a promising material in removing MB from aqueous solutions.

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.

Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies

An aluminium-based metal–organic framework ((MOF), MIL-53(Al)), was hydrothermally synthesized, characterized and applied for the remediation of the herbicides dicamba (3,6-dichloro-2-methoxy benzoic acid) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in aqueous medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to design, optimize and predict the non-linear relationships between the independent and dependent variables. The shared interaction of the effects of key response parameters on the adsorption capacity were assessed using the central composite design-RSM and ANN optimization models. The optimum adsorption capacities for dicamba and MCPA are 228.5 and 231.9 mg g⁻¹, respectively. The RSM ANOVA results showed significant p-values, with coefficients of determination (R²) = 0.988 and 0.987 and R² adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave R² = 0.999 and 0.999, R² adjusted = 0.997 and 0.995 and root mean square errors (RMSEs) of 0.001 and 0.004 for dicamba and MCPA, respectively. In each set of experimental conditions used for the study, the ANN gave better prediction than the RSM, with high accuracy and minimal error. The rapid removal (∼25 min), reusability (5 times) and good agreement between the experimental findings and simulation results suggest the great potential of MIL-53(Al) for the remediation of dicamba and MCPA from water matrices.

Rapid equilibration within a short time, high adsorption capacity, optimization, multivariate interaction of adsorption parameters and artificial neural network prediction model.

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.