Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: artificial neural network modeling and genetic algorithm 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.

Optimization by Box-Behnken design for environmental contaminants removal using magnetic nanocomposite

In this study, a CoO-Fe2O3/SiO2/TiO2 (CIST) nanocomposite was synthesized and utilized as an adsorbent to remove methylene blue (MB), malachite green (MG), and copper (Cu) from aqueous environments. The synthesized nanocomposite was characterized using field emission scanning electron microscopy (FE-SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and X-ray diffraction (XRD). Input parameters included pH (3-10), contact time (10-30 min), adsorbent amount (0.01-0.03 g), and pollutant concentration (20-60 mg L-1). The effects of these parameters on the removal process efficiency were modeled and optimized using the response surface methodology (RSM) based on the Box-Behnken design (BBD). The RSM-BBD method demonstrated the capability to develop a second-degree polynomial model with high validity (R2 ˃ 0.99) for the removal process. The optimization results using the RSM-BBD method revealed a removal efficiency of 98.01%, 93.06%, and 88.26% for MB, MG, and Cu, respectively, under optimal conditions. These conditions were a pH of 6, contact time of 10 min, adsorbent amount of 0.025 g, and concentration of 20 mg L-1. The synthesized adsorbent was recovered through five consecutive adsorption-desorption cycles using hydrochloric acid. The results showed an approximately 12% reduction from the first to the seventh cycle. Also, MB, MG, and Cu removal from real water samples in optimal conditions was achieved in the range of 81.69-98.18%. This study demonstrates the potential use of CIST nanocomposite as an accessible and reusable option for removing MB, MG, and Cu pollutants from aquatic environments.

Studies on the Removal of Malachite Green from Its Aqueous Solution Using Water-Insoluble β-Cyclodextrin Polymers

The rising global pollution of natural waters by dyes has brought to light the need for adaptable and efficient removal techniques. To create water-insoluble β-cyclodextrin (β-CD) polymers like CA/-CD, TA/-CD, and MA/-CD, several organic acids including citric acid (CA), tartaric acid (TA), and malic acid (MA) were cross-linked with β-cyclodextrin in this study. The obtained polymers were characterized by different advanced analytical techniques such as FTIR, SEM, and UV-vis spectrophotometry. Malachite green dye was removed from aqueous solutions using the synthesized polymers by adsorption. The adsorption investigation was conducted under several conditions, including pH, adsorbent mass, dye concentration, temperature, contact time, adsorption isotherm, and kinetics. The adsorbent CA/β-CD shows the highest adsorption of MG dye in all of the conditions because it contains a high number of carboxyl groups. The negatively charged carboxyl ions of CA/β-CD attract the positively charged MG dye electrostatically and remove MG from aqueous media with an efficiency of 91%. As a result, the findings indicated that water-insoluble polymers based on β-cyclodextrin are well-suited as inexpensive adsorbents to remove colors from aqueous media.

Waste valorization as low-cost media engineering for auxin production from the newly isolated Streptomyces rubrogriseus AW22: Model development

Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability was isolated in Algeria for the first time and was characterized as Streptomyces rubrogriseus through chemotaxonomic analysis and 16 S rDNA sequence alignment. The suitable medium for a maximum IAA yield was engineered in vitro and in silico using machine learning-assisted modeling. The primary low-cost feedstocks comprised various concentrations of spent coffee grounds (SCGs) and carob bean grounds (CBGs) extracts. Further, we combined the Box-Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA). The critical process parameters screened via Plackett-Burman design (PBD) served as BBD and ANN-GA inputs, with IAA yield as the output variable. Analysis of the putative IAA using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. AW 22 achieved a maximum IAA yield of 188.290 ± 0.38 μg/mL using the process parameters generated by the ANN-GA model, consisting of L-Trp, 0.6%; SCG, 30%; T°, 25.8 °C; and pH 9, after eight days of incubation. An R² of 99.98%, adding to an MSE of 1.86 × 10^-5 at 129 epochs, postulated higher reliability of ANN-GA-approach in predicting responses, compared with BBD-RSM modeling exhibiting an R² of 76.28%. The validation experiments resulted in a 4.55-fold and 4.46-fold increase in IAA secretion, corresponding to ANN-GA and BBD-RSM models, respectively, confirming the validity of both models.

Modeling of malachite green adsorption onto novel polyurethane/SrFe12O19/clinoptilolite nanocomposite using response surface methodology and biogeography-based optimization-assisted multilayer neural network

This research studied the modeling of malachite green (MG) adsorption onto novel polyurethane/SrFe₁₂O₁₉/clinoptilolite (PU/SrM/CLP) nanocomposite from aqueous solutions by the application of biogeography-based optimization (BBO) algorithm-assisted multilayer neural networks (MNN-BBO) as a new evolutionary algorithm in environmental science. The PU/SrM/CLP nanocomposite was successfully fabricated and characterized by some spectroscopic analyses. Four variables influencing the removal efficiency were modeled by MNN-BBO and response surface methodology (RSM). The MNN-BBO model gave higher percentage removal (99.6%) about 7.6% compared to the RSM technique. Under optimal conditions obtained by MNN-BBO, the four independent variables including pH, shaking rate, initial concentration, and adsorbent dosage were 6.5, 255 rpm, 50 mg.L^-1, and 0.08 g, respectively. Under these conditions, the results were fitted well to the Langmuir isotherm with a monolayer maximum amount of sorbate uptake (q_max) of 68.49 mg.g^-1 and the pseudo-first-order kinetic pattern with the rate constant (K₁) of 0.01 min^-1 with the R² values of 0.9248 and 0.9980, respectively. The results of thermodynamics demonstrated that the MG uptake was not spontaneous due to the positive value of the adsorption ΔG. In addition, the positive values of ΔS (0.079 kJ/mol K) and ΔH (30.816 kJ/mol) indicated the feasible operation and endothermic approach, respectively. Besides, the wastewater investigations showed that the nanocomposite could be used as a new promising sorbent for efficient removal of MG (R% > 72) and magnetically separable from the real samples.

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.

Ligustrum lucidum Leaf Extract-Assisted Green Synthesis of Silver Nanoparticles and Nano-Adsorbents Having Potential in Ultrasound-Assisted Adsorptive Removal of Methylene Blue Dye from Wastewater and Antimicrobial Activity

Present study was conducted to investigate the adsorption and ultrasound-assisted adsorption potential of silver nanoparticles (AgNPs) and silver nanoparticles loaded on chitosan (AgCS composite) as nano-adsorbents for methylene blue (MB) removal. AgNPs were synthesized using leaf extract of Ligustrum lucidum, which were incorporated on the chitosan’s surface for modification. UV−Vis Spectroscopy, FTIR, XRD, SEM, and EDX techniques were used to confirm the synthesis and characterization of nanomaterials. Batch adsorption and sono-adsorption experiments for the removal of MB were executed under optimal conditions; for fitting the experimental equilibrium data, Langmuir and Freundlich’s isotherm models were adopted. In addition, the antimicrobial potential of the AgNPs and AgCS were examined against selected bacterial and fungal strains. UV−Vis spectroscopy confirmed AgNPs synthesis from the leaf extract of L. lucidum used as a reducer, which was spherical as exposed in the SEM analysis. The FTIR spectrum illustrated phytochemicals in the leaf extract of L. lucidum functioning as stabilizing agents around AgNPs and AgCS. Whereas, corresponding crystalline peaks of nanomaterial, including a signal peak at 3 keV indicating the presence of silver, were confirmed by XRD and EDX. The Langmuir model was chosen as an efficient model for adsorption and sono-adsorption, which exposed that under optimum conditions (pH = 6, dye initial concentration = 5 mg L−1, adsorbents dosage = 0.005 g, time = 120 min, US power 80 W), MB removal efficiency of AgNPs was >70%, using ultrasound-assisted adsorption compared to the non-sonicated adsorption. Furthermore, AgNPs exhibited promising antibacterial potential against Staphylococcus aureus with the maximum zone of inhibition (14.67 ± 0.47 mm). It was concluded that the green synthesis approach for the large-scale production of metallic nanoparticles is quite effective and can be recommended for efficient and cost-effective way to eradicate dyes, particularly from textile wastewater.

Removing of Anionic Dye from Aqueous Solutions by Adsorption Using of Multiwalled Carbon Nanotubes and Poly (Acrylonitrile-styrene) Impregnated with Activated Carbon

This paper presents an estimation of the adsorptive potential of multiwalled carbon nanotubes (MWCNTs) and modified poly (acrylonitrile-co-styrene) with activated carbon for the uptake of reactive red 35 (RR35) dye from aqueous solution by a batch system. MWCNT adsorbent was synthesized by encapsulation via in situ polymerization. The copolymer material of poly (acrylonitrile-styrene) P (AN-co-ST) was prepared in a ratio of 2:1 V/V by the precipitation polymerization process. The prepared composites’ properties were characterized by FTIR, SEM, Raman, mean particle size (PSA), and XRD analysis. The PSA of the copolymeric material was determined to be 450.5 and 994 nm for MWCNTs and P(AN-co-St)/AC, respectively. Moreover, the influences of different factors, for example pH (2–10), adsorbents dosage (0.005–0.04 g), contact time (5–120 min), initial dye concentration (10–50 mg L−1), and temperature (25–55 °C). The optimum values were determined to be 2 and 4 pH, 10 mg L−1 of RR35 dye, and 0.04 g of adsorbents at early contact time. Furthermore, the adsorption isotherm was studied using Langmuir, Freundlich, Tempkin, and Halsey models. Maximum capacity qmax for MWCNTS and P (AN-co-St)/AC was 256.41 and 30.30 mg g−1, respectively. The investigational kinetic study was appropriated well via a pseudo second-order model with a correlation coefficient around 0.99. Thermodynamic study displayed that the removal of RR35 is exothermic, a spontaneous and physisorption system. The adsorption efficiency reduced to around 54–55% of the RR35 after four cycles of reuse of the adsorbents at 120 min.

Influencing Multi-Walled Carbon Nanotubes for the Removal of Ismate Violet 2R Dye from Wastewater: Isotherm, Kinetics, and Thermodynamic Studies

In this study, a multi-walled carbon nanotube (MWCNT) was synthesized and used as an adsorbent for the removal of Ismate violet 2R dye from contaminated water. The morphology and structure of the synthesized adsorbent were examined via the Brunauer–Emmett–Teller (BET) surface area, X-ray powder diffraction (XRD) analysis, infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and Raman spectroscopy. The effects of an MWCNT on the removal of IV2R were examined via a batch method using different factors such as pH, agitation time, adsorbent dosage, temperature, and initial dye concentration. The results showed that, at the acidic pH 4, 0.08 g of an MWCNT with 10 mg L−1 at 120 min realized the favorable removal of IV2R dye using an MWCNT. Under these operation conditions, the maximum elimination efficiency for real wastewater reached 88.2%. This process benefits from the ability to remove a large amount of dye (approximately 85.9%) in as short as 10 min using 0.005 g of MWCNTs. Moreover, the investigational isotherm data were examined by different models. The equations of error functions were used in the isotherm model to show the most appropriate isotherm model. The highest adsorption capacity for the removal of the dye was 76.92 mg g−1 for the MWCNT. Moreover, the regression data indicated that the adsorption kinetics were appropriate with a pseudo-second order and an R2 of 0.999. The thermodynamic study showed that the removal of IV2R is an endothermic, spontaneous, and chemisorption process. The MWCNT compound appears to be a new, promising adsorbent in water treatment, with 91.71% regeneration after three cycles.

The use of artificial neural network (ANN) for modeling adsorption of sunset yellow onto neodymium modified ordered mesoporous carbon

Discharging coloring products in water bodies has degraded water quality irreversibly over the past several decades. Order mesoporous carbon (OMC) was modified by embedding neodymium(III) chloride on the surface of OMC to enhance the adsorptive removal towards these contaminants. This paper represents an artificial neural network (ANN) based approach for modeling the adsorption process of sunset yellow onto neodymium modified OMC (OMC-Nd) in batch adsorption experiments. Neodymium modified OMC was characterized using N₂ adsorption-desorption isotherm, TEM micrographs, FT-IR and XPS spectra analysis techniques. 2.5 wt% Nd loaded OMC was selected as the final adsorbent for further experiments because OMC-2.5Nd showed highest removal efficiency of 93%. The ANN model was trained and validated with the adsorption experiments data where initial concentration, reaction time, and adsorbent dosage were selected as the variables for the batch study, whereas the removal efficiency was considered as the output. The ANN model was first developed using a three-layer back propagation network with the optimum structure of 3-6-1. The model employed tangent sigmoid transfer function as input in the hidden layer whereas a linear transfer function was used in the output layer. The comparison between modeled data and experimental data provided high degree of correlation (R² = 0.9832) which indicated the applicability of ANN model for describing the adsorption process with reasonable accuracy.

Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R²), Adjusted-R², Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R² of 0.9983 and 0.9991 respectively, compared with MLR models with R² of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33.

Use of neural networks to estimate the sorption and desorption coefficients of herbicides: A case study of diuron, hexazinone, and sulfometuron-methyl in Brazil

The use of herbicides in Brazil has been carried out based on the manufacturer's recommendation, often disregarding the high variability of soil attributes. The use of statistical methods to predict the herbicide retention processes in the soil can contribute to the improvement of weed control efficiency associated with the lower risk of environmental contamination. This research evaluated the use of Artificial Neural Networks (ANNs) to predict soil sorption and desorption, as well as the environmental contamination potential of diuron, hexazinone and sulfometuron-methyl herbicides in Brazilian soils. The sorption and desorption coefficients of the three herbicides were determined in laboratory tests for 15 soils from different Brazilian states. To predict the sorption and desorption of diuron, hexazinone and sulfometuron-methyl were used a multilayer perceptron ANNs (MLP). The inputs were the characteristics of the herbicides and the physical and chemical attributes of the soils, and the outputs of were the sorption and desorption coefficients (Kfs and Kfd). The risk of leaching of diuron, hexazinone, and sulfometuron-methyl herbicides were evaluated considering the sorption values observed and those estimated by the models. The Artificial Neural Network (ANN) models were efficient for the prediction of sorption and desorption of diuron, hexazinone, and sulfometuron-methyl herbicides. The physicochemical properties of the herbicides were more important for the modeling of multilayer perceptron ANNs than the soil attributes. The herbicides diuron, hexazinone, and sulfometuron-methyl have a high potential risk for contamination of groundwater in different Brazilian states.

Fast decolorization of cationic dyes by nano-scale zero valent iron immobilized in sycamore tree seed pod fibers: kinetics and modelling study

In the present work, Sycamore (Platanus occidentalis) tree seed pod fibers (STSPF) and nano-scale zero valent iron particles (nZVI) immobilized in Sycamore tree seed pod fibers (nZVIʘSTSPF) were produced. This biosorbent has been utilized as a viable effective biosorbent in the removing of methylene blue hydrate (MB), malachite green oxalate(MG), methyl violet 2B(MV) dyes from synthetic wastewater. The biosorbents were characterized by scanning electron microscopy (SEM) and Fourier transform infrared (FT-IR) spectroscopy. Various parameters such as contact time, solution concentration, pH and amount of biosorbent were investigated in order to evaluate the potential of the nanomaterials immobilized on natural wastes as sorbing biomaterials for the cationic dyes. Study on sorption kinetic and the sorption isotherm was carried out and best fitting models for the rate kinetics and isotherms were suggested. Langmuir isotherm was observed to be compatible with the isotherm models. The STSPF in the raw form showed the best dye sorption capacity of 43.67 mg/g for MG, 25.32 mg/g for MV, and 126.60 mg/g for MB. The magnetic nZVIʘSTSPF showed the best dye sorption capacity 92.59 mg/g for MG, 92.59 mg/g for MV, and 140.80 mg/g for MB. The iron nanoparticles immobilized biosorbent exhibited a higher removal capacity for all dyes compared to the raw biosorbent.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms.

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200W) as an efficient method and characterized by FE-SEM, FTIR and N₂ adsorption-desorption isotherm. The effect of different parameters such as contact time (0-120min), initial metal ion concentration (25-200mg/L), temperature (298, 318 and 333K), amount of adsorbent (0.002-0.007g) and the solution's initial pH (1-7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R²>0.99). The maximum monolayer adsorption capacity (q_m) of NiO/ACNC was 1428.57mg/L. Thermodynamic parameters (ΔG°, ΔH° and ΔS°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R²>0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.

Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

Bleaching of sugar cane juice using a food-grade adsorber resin and explained by a kinetic model describing the variation in time of the content of adsorbate

This work studies the adsorption of colored compounds in cane juice using a food-grade macroporous adsorber resin without functional groups. The adsorption equilibrium was studied through the adsorption isotherms at 30, 40, and 50 ℃. The absorbance at 420 nm was used to measure the concentration of colored compounds, which enables correlation of the residual concentration with the adsorbed concentration. Furthermore, the efficiency of the adsorption process was studied, from which it was observed that there was an improvement in efficiency with increasing resin content, while the increase in temperature was less important in the process. The kinetic study was performed using the Ibarz model and intraparticle diffusion model, which correctly account for the kinetics of the adsorption process. The adsorption kinetic constant was always greater than the desorption kinetic constant, indicating that the adsorption step predominates over the desorption step.

Effective adsorption of malachite green using magnetic barium phosphate composite from aqueous solution

Magnetic Ba₃(PO₄)₂/Fe₃O₄-nanoparticle (called BPFN) was prepared, characterized, and developed as a low-cost adsorbent for malachite green (MG) from aqueous solution. Factors such as adsorption temperature, pH of solution, dosage of adsorbent, adsorption kinetics and isotherms were investigated. The maximum adsorption capacity obtained in this work was 1639mgg^-1 at 45°C and pH6. The adsorption process fitted the pseudo-first-order kinetic model and Langmuir isotherm model. Evidences from zeta potential, Fourier transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS) data revealed that the adsorption process was driven by electrostatic attraction, the interaction between Lewis base N(CH₃)₂ in MG and Lewis acid Ba sites of BPFN. In addition, the BPFN could be easily regenerated by a magnet and the adsorption capacity maintained at 70% after five cycles. The present study suggests that the BPFN had high potential of removing MG from wastewater.

Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review

Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO). Most of these papers are discussed. The further research needs in this field are suggested. These ANNs models are obtaining popularity as approaches, which can be successfully employed for the adsorption of dyes with acceptable accuracy.

Adsorption removal of malachite green dye from aqueous solution

In this review, the state of the art on the removal of malachite green dye from aqueous solution using adsorption technique is presented. The objective is to critically analyze different adsorbents available for malachite green dye removal. Hence, the available recent literature in the area is categorized according to the cost, feasibility, and availability of adsorbents. An extensive survey of the adsorbents, derived from various sources such as low cost biological materials, waste material from industry, agricultural waste, polymers, clays, nanomaterials, and magnetic materials, has been carried out. The review studies on different adsorption factors, such as pH, concentration, adsorbent dose, and temperature. The fitting of the adsorption data to various models, isotherms, and kinetic regimes is also reported.

Application of linear and non-linear methods for modeling removal efficiency of textile dyes from aqueous solutions using magnetic Fe3O4 impregnated onto walnut shell

The performance of the Nano-magnetite Fe₃O₄ impregnated onto walnut shell (Fe₃O₄-WNS), which possessed the adsorption features of walnut shell and the magnetic property of Fe₃O₄, was investigated for the elimination of the methyl violet and Rhodamine 6G from contaminated aqueous solutions. The effects of different experimental variables on the removal efficiency of the cited dyes were examined. Then these variables were used as the inputs to generate linear and non-linear models such as the multiple linear regression, random forest, and artificial neural network to predict the removal efficiency of these dye species at different experimental conditions. The validation studies of these models were performed using the test set, which was not present in the modeling procedure. It was found that ANN had a higher ability to predict the adsorption process under different experimental conditions, and could be applied for the development of an automated dye wastewater removal plant. Also the maximum adsorption capacity (q_max) indicated that the q_max value for Fe₃O₄-WNS for removal of cationic dyes was comparable or better than that for some reported adsorbents. Also it should be cited that exhausted Fe₃O₄-WNS was regenerated using dishwashing liquid, and reused for removal of the cited dye species from aqueous solutions.

Adaptive Neuro-Fuzzy Inference system analysis on adsorption studies of Reactive Red 198 from aqueous solution by SBA-15/CTAB composite

In this study, the Mesoporous material SBA-15 were synthesized and then, the surface was modified by the surfactant Cetyltrimethylammoniumbromide (CTAB). Finally, the obtained adsorbent was used in order to remove Reactive Red 198 (RR 198) from aqueous solution. Transmission electron microscope (TEM), Fourier transform infra-red spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and BET were utilized for the purpose of examining the structural characteristics of obtained adsorbent. Parameters affecting the removal of RR 198 such as pH, the amount of adsorbent, and contact time were investigated at various temperatures and were also optimized. The obtained optimized condition is as follows: pH=2, time=60min and adsorbent dose=1g/l. Moreover, a predictive model based on ANFIS for predicting the adsorption amount according to the input variables is presented. The presented model can be used for predicting the adsorption rate based on the input variables include temperature, pH, time, dosage, concentration. The error between actual and approximated output confirm the high accuracy of the proposed model in the prediction process. This fact results in cost reduction because prediction can be done without resorting to costly experimental efforts. SBA-15, CTAB, Reactive Red 198, adsorption study, Adaptive Neuro-Fuzzy Inference systems (ANFIS).

Screening and optimization of highly effective ultrasound-assisted simultaneous adsorption of cationic dyes onto Mn-doped Fe3O4-nanoparticle-loaded activated carbon

The ultrasound-assisted simultaneous adsorption of brilliant green (BG) and malachite green (MG) onto Mn-doped Fe₃O₄ nanoparticle-loaded activated carbon (Mn-Fe₃O₄-NP-AC) as a novel adsorbent was investigated and analyzed using first derivative spectrophotometry. The adsorbent was characterized using FT-IR, FE-SEM, EDX and XRD. Plackett-Burman design was applied to reduce the total number of experiments and to optimize the ultrasound-assisted simultaneous adsorption procedure, where pH, adsorbent mass and sonication time (among six tested variables) were identified as the most significant factors. The effects of significant variables were further evaluated by a central composite design under response surface methodology. The significance of independent variables and their interactions was investigated by means of the analysis of variance (ANOVA) within 95% confidence level together with Pareto chart. Using this statistical tool, the optimized ultrasound-assisted simultaneous removal of basic dyes was obtained at 7.0, 0.02g, 3min for pH, adsorbent mass, and ultrasonication time, respectively. The maximum values of BG and MG uptake under these experimental conditions were found to be 99.50 and 99.00%, respectively. The adsorption process was found to be followed by the Langmuir isotherm and pseudo-second order model using equilibrium and kinetic studies, respectively. According to Langmuir isotherm model, the maximum adsorption capacities of the adsorbent were obtained to be 101.215 and 87.566mgg^-1 for MG and BG, respectively. The value of apparent energy of adsorption obtained from non-linear Dubinin-Radushkevich model (4.348 and 4.337kJmol^-1 for MG and BG, respectively) suggested the physical adsorption of the dyes. The studies on the well regenerability of the adsorbent in addition to its high adsorption capacity make it promising for such adsorption applications.

Optimization and modeling of preconcentration and determination of dyes based on ultrasound assisted-dispersive liquid-liquid microextraction coupled with derivative spectrophotometry

Present study is based on describing an ultrasound-assisted dispersive liquid-liquid microextraction coupled with derivative spectrophotometry (UAS-DLLME-UV-vis) as useful technique for selective determination of crystal violet (CV) and azure b (Az-B). The significant factors like pH, extractor volume, disperser value and extraction time contribution and their numerical coefficient in quadratic model were calculated according to central composite design (CCD). According to desirability function (DF) as good criterion the best experimental conditions was adjusted and selected at pH of 7.0, 170μL of chloroform, 800μL of ethanol that strongly mixed with the aqueous phase via 4min sonication. Additionally, under study system was modeled by trained artificial neural networks (ANNs) as fitness function with acceptable error of MSE 2.97×10^-06 and 1.15×10^-05 with R²: 0.9999 and 0.9997 for CV and Az-B, respectively. The optimum conditions by using genetic algorithm (GA) method was pH of 6.3, 160μL of chloroform, 740μL of ethanol and 4.5min sonication. Under above specified and optimize conditions, the predicted extraction percentage were 99.80 and 102.20% for CV and Az-B, respectively. The present UAS-DLLME-UV-vis procedure has minimum interference from other substances assign to the matrix, which candidate this method as good alternative to quantify under study dyes content with recoveries in the range of 86-100% for dyes. The detection limits were 2.043ngmL^-1 and 1.72ngmL^-1, and limits of quantitation were 6.81ngmL^-1 and 5.727ngmL^-1 for CV and Az-B, respectively. The proposed methodology was successfully applied for quantification of under study analytes at different media.

Electrocoagulation-Adsorption to Remove Anionic and Cationic Dyes from Aqueous Solution by PV-Energy

The cationic dye malachite green (MG) and the anionic dye Remazol yellow (RY) were removed from aqueous solutions using electrocoagulation-adsorption processes. Batch and continuous electrocoagulation procedures were performed and compared. Carbonaceous materials obtained from industrial sewage sludge and commercial activated carbons were used to adsorb dyes from aqueous solutions in column systems with a 96–98% removal efficiency. The continuous electrocoagulation-adsorption system was more efficient for removing dyes than electrocoagulation alone. The thermodynamic parameters suggested the feasibility of the process and indicated that the adsorption was spontaneous and endothermic (ΔS=0.037 and −0.009 for MG and RY, resp.). The ΔG value further indicated that the adsorption process was spontaneous (−6.31 and −10.48; T=303 K). The kinetic electrocoagulation results and fixed-bed adsorption results were adequately described using a first-order model and a Bohart-Adams model, respectively. The adsorption capacities of the batch and column studies differed for each dye, and both adsorbent materials showed a high affinity for the cationic dye. Thus, the results presented in this work indicate that a continuous electrocoagulation-adsorption system can effectively remove this type of pollutant from water. The morphology and elements present in the sludge and adsorbents before and after dye adsorption were characterized using SEM-EDS and FT-IR.

Integrating powdered activated carbon into wastewater tertiary filter for micro-pollutant removal

Integrating powdered activated carbon (PAC) into wastewater tertiary treatment is a promising technology to reduce organic micro-pollutant (OMP) discharge into the receiving waters. To take advantage of the existing tertiary filter, PAC was pre-embedded inside the filter bed acting as a fixed-bed adsorber. The pre-embedding (i.e. immobilization) of PAC was realized by direct dosing a PAC solution on the filter top, which was then promoted to penetrate into the filter media by a down-flow of tap water. In order to examine the effectiveness of this PAC pre-embedded filter towards OMP removal, batch adsorption tests, representing PAC contact reactor (with the same PAC mass-to-treated water volume ratio as in the PAC pre-embedded filter) were performed as references. Moreover, as a conventional dosing option, PAC was dosed continuously with the filter influent (i.e. the wastewater secondary effluent with the investigated OMPs). Comparative results confirmed a higher OMP removal efficiency associated with the PAC pre-embedded filter, as compared to the batch system with a practical PAC residence time. Furthermore, over a filtration period of 10 h (approximating a realistic filtration cycle for tertiary filters), the continuous dosing approach resulted in less OMP removal. Therefore, it was concluded that the pre-embedding approach can be preferentially considered when integrating PAC into the wastewater tertiary treatment for OMP elimination.