The artificial neural network and Box-Behnken design for Cu2+ removal by the pottery sludge from water samples: Equilibrium, kinetic and thermodynamic studies

Optimization of Cd (II) removal from aqueous solution by natural hydroxyapatite/bentonite composite using response surface methodology

Toxic cadmium (Cd) was removed from water using eggshell-based hydroxyapatite (HAp) grafted bentonite (HAp/bentonite) composite through a straightforward chemical synthesis route. The as-prepared adsorbents were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller analysis (BET). Optimization of the initial adsorbate concentration, adsorbent dosage, pH, and contact time—all of which affect the adsorption process—was performed using the central composite design (CCD) of the response surface methodology (RSM). 99.3 percent adsorptive removal efficiency was observed at an initial concentration of 61.58 mg/L of Cd (II), with an adsorbent dosage of 1.58 g, a solution pH of 5.88, and a contact time of 49.63 min. The analysis of variance (ANOVA) was performed, and the multiple correlation coefficient (R2) was found to be 0.9915 which confirms the significance of the predicted model. The Langmuir isotherm model best represented the adsorption isotherm data, which also predicted a maximum sorption capacity of 125.47 mg/g. The kinetic data were best described by the pseudo-second order model.

Pyrolysis of citrus wastes for the simultaneous production of adsorbents for Cu(II), H2, and d-limonene

A route based on pyrolysis and physical activation with H₂O and CO₂ was proposed to reuse citrus waste traditionally discarded. The citrus wastes were orange peel (OP), mandarine peel (MP), rangpur lime peel (RLP), and sweet lime peel (SLP). The main aim was to use the solid products of this new route as adsorbents for Cu(II) ions. Copper ions are among the most important water pollutants due to their non-degradability, toxicity, and bioaccumulation, facilitating their inclusion and long persistence in the food chain. Besides the solid products, the liquid and gaseous fractions were evaluated for possible applications. Results showed that the citrus waste composition favored the thermochemical treatment. In addition, the following yields were obtained from the pyrolysis process: approximately 30 % wt. of biochar, 40 % wt. of non-condensable gases, and 30 % wt. of bio-oil. The biochars did not present a high specific surface area. Nevertheless, activated carbons with CO₂ and H₂O presented specific surface areas of 212.4 m²/g and 399.4 m²/g, respectively, and reached Cu(II) adsorption capacities of 28.2 mg g^-1 and 27.8 mg g^-1. The adsorption kinetic study revealed that the equilibrium was attained at 60 min and the pseudo-second-order model presented a better fit to the experimental data. The main generated gases were CO₂, which could be employed as an activating agent for activated carbon production. d-limonene, used for food and medicinal purposes, was the main constituent of the bio-oil.

Pyrolysis of rubber seed pericarp biomass treated with sulfuric acid for the adsorption of crystal violet and methylene green dyes: an optimized process

In this study, the biomass of rubber seed pericarp was first treated with sulfuric acid and then its activated carbon was formed by the pyrolysis process. As produced acid-treated activated carbon of chosen biomass was then used for the adsorption of crystal violet (CV) and methylene green (MG) from the colored aqueous solution. The adsorbent was exposed to several characterization methods to know its structural and morphological behaviors before and after CV and MG adsorption. The adsorbent was found to be mesoporous having a surface area of 59.517 m²/g. The effect of pH, time, and concentration was assessed while various isotherm and kinetics models were employed to know the adsorption insight. The optimum conditions were at pH 8, within 30 min, 50 mg/L concentration, and 0.06 gm dose. The adsorption data (the maximum adsorption capacity for CV and MG were found to be 302.7 and 567.6 mg/g, respectively) was validated by fitting in a response surface statistical methodology and the positive interactions between the studied factors were found. The adsorption was mainly belonging to the electrostatic attraction of the dye molecules. The study proves that the used adsorbent is economical and an excellent source of treating wastewater.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO₃ (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R² = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

Walnut shell powder as a low-cost adsorbent for methylene blue dye: isotherm, kinetics, thermodynamic, desorption and response surface methodology examinations

The low cost, eco-friendly and potential biomass, i.e. walnut (Juglans regia) shell powder was deployed for the removal of toxic methylene blue dye from contaminated water solution. The important characterization of the waste material was conducted by using several techniques, i.e. Scanning electron microscope, Fourier-transform infrared spectroscopy, Energy-dispersive X-ray spectroscopy, X-ray powder diffraction, Brunauer-Emmett-Teller surface area analysis, and Thermogravimetric analysis. The marked impact of various operating conditions, i.e. dose, concentration, time, pH and temperature on the adsorption process was investigated. Increasing pH resulted in an increase of percent dye adsorption, and the adsorption mechanism was occurred by electrostatic attraction between negative adsorbent surface and positive dye molecules. The equilibrium data suited with Langmuir isotherm model while the adsorption practice followed the pseudo-second-order kinetic model. Higher temperature reduced the adsorption of dye molecules. The adsorption process was spontaneous, exothermic and chemical. The critical statistical analysis of the experimental results was directed by forming the design of the experiment, which was further, optimized by ANOVA, 3D and perturbation plots. The error and predicted values of both the studied responses as derived from the statistical model showed the agreeable results. 0.1 N HCl was found to be effective in complete desorption. The results are very practical and prove the effectiveness of walnut shell powder in the usage of decolorization for methylene blue.

Evolving multilayer perceptron, and factorial design for modelling and optimization of dye decomposition by bio-synthetized nano CdS-diatomite composite

Design of experiment and hybrid genetic algorithm optimized multilayer perceptron (GA-MLP) artificial neural network have been employed to model and predict dye decomposition capacity of the biologically synthesized nano CdS diatomite composite. Impact of independent variables such as, light (UV: on-off), solution pH (5-8), composite weight (CW: 0.5-1 mg), initial dye concentration (DC: 10-20 mg/l) and contact time (0-120 min), mainly in two levels, were examined to evaluate dye removal efficiency of the composite. According to the developed response surface based on the factorial design, all independent variables shown positive interactive effect on dye removal (UV > CW > pH > DC), as well as the pH-CW mutual interaction, while both UV-DC and CW-DC had antagonistic effect. The pH-CW interaction was more influential than pH and DC. Incorporation of the intermediate measurements of dye removal between the start and final contact times in GA-MLP approach, had found to improve the accuracy and predictability of the GA-MLP model. Based on the closeness of the R² (0.98), root mean square error (1.03), variance accounted for (98.23%), mean absolute error (0.61) and model predictive error (9.46%) to their desirable levels, proposed GA-MLP model outperformed the factorial design model. Finally, optimal parameter choice for maximum dye removal using factorial design and GA-MLP were found as: UV (on), pH (9), CW (1 g) and DC (10 mg/l) and UV (on), pH (8.85), CW (0.92 g), DC (12.3 mg/l) and T (117 0.6 min), respectively.

Removal of methylene blue via bioinspired catecholamine/starch superadsorbent and the efficiency prediction by response surface methodology and artificial neural network-particle swarm optimization

This paper demonstrates coupling of the artificial neural network (ANN) technique with the particle swarm optimization (PSO) method and compares the performance of ANN-PSO with response surface methodology (RSM) in prediction of the adsorption of methylene blue (MB) by a novel bio-superadsorbent. To this, a starch-based superadsorbent was synthesized using acrylic acid and acryl amid polymers and then catecholamine functional groups were combined onto the surface with oxidative polymerization of dopamine. The adsorption of MB was considered as a function of pH, dye concentration, and contact time. The best topology of the ANN was found to be 3-7-1, and prediction model of the adsorption capacity was demonstrated as a matrix of explicit equations. ANN-PSO is more accurate than RSM. The results revealed that the root-mean-square error, correlation coefficient, and normalized standard deviation for the ANN-PSO are 22.46, 0.99, and 16.83, respectively, while for RSM are 82.89, 0.98, and 65.41, respectively.

Behavior and Mechanism of Tannic Acid Adsorption on the Calcite Surface: Isothermal, Kinetic, and Thermodynamic Studies

Tannic acid is a calcite flotation agent widely used in mineral processing. To better understand the physicochemical reactivity of tannic acid toward calcite, the present work focused on studying the mechanisms involved during the adsorption process. Hence, in order to determine the optimal physicochemical parameters, tannic acid adsorption onto calcite was investigated at various experimental conditions such as contact time, initial tannic acid concentration, solution pH, particle size, and temperature. The obtained results showed that the adsorption capacity of tannic acid increased significantly with initial tannic acid concentration. Furthermore, tannic acid adsorption onto calcite was highly dependent on solution pH, and the optimal adsorption amount was found to be at pH 8. Therefore, the behavior controlling the studied adsorption process could be attributed to ion exchange. Moreover, the adsorption mechanism has been determined by isothermal, kinetic, and thermodynamic studies. Thus, the Sips isotherm model was the one that best predicted equilibrium data. Adsorption kinetics followed a pseudo-second-order model, indicating that the adsorption process was controlled by the chemical reaction. The estimated thermodynamic parameters revealed that the adsorption reaction was exothermic in nature and the system entropy decreased nonsignificantly during this process. Based on these results, the study of the physicochemical interaction between tannins and carbonates has potential application in mineral processing as well as in other fields.

Cross-Linked Magnetic Chitosan/Activated Biochar for Removal of Emerging Micropollutants from Water: Optimization by the Artificial Neural Network

One of the most important types of emerging micropollutants is the pharmaceutical micropollutant. Pharmaceutical micropollutants are usually identified in several environmental compartments, so the removal of pharmaceutical micropollutants is a global concern. This study aimed to remove diclofenac (DCF), ibuprofen (IBP), and naproxen (NPX) from the aqueous solution via cross-linked magnetic chitosan/activated biochar (CMCAB). Two independent factors—pH (4–8) and a concentration of emerging micropollutants (0.5–3 mg/L)—were monitored in this study. Adsorbent dosage (g/L) and adsorption time (h) were fixed at 1.6 and 1.5, respectively, based on the results of preliminary experiments. At a pH of 6.0 and an initial micropollutant (MP) concentration of 2.5 mg/L, 2.41 mg/L (96.4%) of DCF, 2.47 mg/L (98.8%) of IBP, and 2.38 mg/L (95.2%) of NPX were removed. Optimization was done by an artificial neural network (ANN), which proved to be reasonable at optimizing emerging micropollutant elimination by CMCAB as indicated by the high R2 values and reasonable mean square errors (MSE). Adsorption isotherm studies indicated that both Langmuir and Freundlich isotherms were able to explain micropollutant adsorption by CMCAB. Finally, desorption tests proved that cross-linked magnetic chitosan/activated biochar might be employed for at least eight adsorption-desorption cycles.