Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon

Enhanced adsorption of Bismark Brown R dye by chitosan conjugated magnetic pectin loaded filter mud: A comprehensive study on modeling and mechanisms

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol-1 K and 403.47 kJ mol-1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg-1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater.

Artificial intelligence application in adsorption of uremic toxins: Towards the eco-friendly design of highly efficient with potential applications as hemodialysis membranes

Six Functionalized Activated Carbon Cloths (FACCs) were designed to obtain fundamental information for training a Bayesian Regularized Artificial Neural Network (BRANN) capable of predicting adsorption capacity of the FACCs to synthesize tailor-made materials with potential application as dialysis membranes. Characterization studies showed that FACCs have a high surface area (1354-2073 m2 g-1) associated with increased microporosity (W0, average: 0.57 cm3 g-1). Materials are carbonaceous, with a carbon content between 69 and 92%. Chemical treatments modify the pHpzc of materials between 4.1 and 7.8 due to incorporating functional groups on the surface (C=O, -COOH, -OH, -NH, -NH2). Uremic toxins tests showed a high elimination rate of p-cresol (73 mg g-1) and creatinine (90 mg g-1) which is not affected by the matrix (aqueous solution and simulated serum). However, in the case of uric acid, adsorption capacity decreased from 143 mg g-1 to 71 mg g-1, respectively. When comparing the kinetic constants of the adsorption studies in simulated serum versus the studies in aqueous solution, it can be seen that this does not undergo significant changes (0.02 min-1), evidencing the versatility of the material to work in different matrices. The previous studies, in combination with characterization of the materials, allowed to establish the adsorption mechanism. Thus, it permitted to train the BRANN to obtain mathematical models capable to predict the kinetic adsorption of the toxins studied. It is concluded that the predominant adsorption mechanism is due to π-π interactions between the adsorbate unsaturations with the material's pseudo-graphitic planes. Results show that FACCs are promising materials for hemodialysis membranes. Finally, taking into consideration the adsorption capacities and rates, as well as the semiquantitative analysis of the environmental impact associated with the preparation of the adsorbents, the best adsorbent (CC, Eco-Scale = 91.5) was selected. The studies presented show that the material is eco-friendly and highly efficient in the elimination of uremic toxins.

CdS nanocrystallites sensitized ZnO nanosheets for visible light induced sonophotocatalytic/photocatalytic degradation of tetracycline: From experimental results to a generalized model based on machine learning methods

CdS/ZnO nanosheets heterostructures ((x)CdS/ZNs) with different mole ratios of Cd/Zn ((x) = 0.2, 0.4, and 0.6) were synthesized by the impregnation-calcination method. PXRD patterns showed that the (100) diffraction of ZNs was the most significant in the (x)CdS/ZNs heterostructures, and it confirmed that CdS nanoparticles (in cubic phase) occupied the (101) and (002) crystal facets of ZNs with hexagonal wurtzite crystal phase. UV-Vis DRS results indicated that CdS nanoparticles decreased the band gap energy of ZNs (2.80-2.11 eV) and extended the photoactivity of ZNs to the visible light region. The vibrations of ZNs were not observed clearly in the Raman spectra of (x)CdS/ZNs due to the extensive coverage of CdS nanoparticles shielding the deeper-laying ZNs from Raman response. The photocurrent of (0.4) CdS/ZNs photoelectrode reached 33 μA, about 82 times higher than that for ZNs (0.4 μA, 0.1 V vs Ag/AgCl). The formation of an n-n junction at the (0.4) CdS/ZNs reduced the recombination of electron-hole pairs and increased the degradation performance of the as-prepared (0.4) CdS/ZNs heterostructure. The highest percentage removal of tetracycline (TC) in the sonophotocatalytic/photocatalytic processes was obtained by (0.4) CdS/ZNs under visible light. The quenching tests showed that O₂^•-, h⁺, and OH^• were the main active species in the degradation process. The degradation percentage decreased negligibly in the sonophotocatalytic (84%-79%) compared to the photocatalytic (90%-72%) process after four re-using runs due to the presence of ultrasonic waves. For the estimation of degradation behavior, two machine learning methods were applied. The comparison between the ANN and GBRT models evidenced that both models had high prediction accuracy and could be used for predicting and fitting the experimental data of the %removal of TC. The excellent sonophotocatalytic/photocatalytic performance and stability of the fabricated (x)CdS/ZNs catalysts made them promising candidates for wastewater purification.

Adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) modelling of Cu, Cd, and Pb adsorption onto tropical soils

Soils interact in many ways with metal ions thereby modifying their mobility, phase distribution, plant availability, speciation, and so on. The most prominent of such interactions is sorption. In this study, we investigated the sorption of Pb, Cd, and Cu in five natural soils of Nigerian origin. A relatively sparsely used method of modelling soil-metal ion adsorption, i.e. adaptive neuro-fuzzy inference system (ANFIS), was applied comparatively with multiple linear regression (MLR) models. The isotherms were well described by Freundlich and Langmuir equations (R² ≥ 0.95) and the kinetics by nonlinear two-stage kinetic model, TSKM (R² ≥ 0.81). Based on the values delivered by the Langmuir equation, the maximum adsorption capacities (Q_m*) were found to be in the ranges 10,000-20,000, 12,500-50,000, and 4929-35,037 µmol kg^-1 for Cd, Cu, and Pb, respectively. The study revealed significant correlations between Q_m* and routinely determined soil parameters such as soil organic carbon (C_org), cation exchange capacity (CEC), amorphous Fe and Mn oxides, and percentage clay content. These soil parameters, combined with operational variables (i.e. solution/soil pH, initial metal concentration (C_o), and temperature), were used as input vectors in ANFIS and MLR models to predict the adsorption capacities (Q_e) of the soil-metal ion systems. A total of 255 different ANFIS and 255 different MLR architectures/models were developed and compared based on three performance metrics: MAE (mean absolute error), RMSE (root mean square errors), and R² (coefficient of determination). The best ANFIS returned MAE_test 0.134, RMSE_test 0.164, and R²_test 0.76, while the best MLR returned MAE_test 0.158, RMSE_test 0.199, and R²_test 0.66, indicating the predictive advantage of ANFIS over MLR. Thus, ANFIS can fairly accurately predict the adsorption capacity and/or distribution coefficient of a soil-metal ion system a priori. Nevertheless, more investigation is required to further confirm the robustness/generalisation of the proposed ANFIS.

Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix

The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay.  The process variables considered were surfactant concentration, , activation time, ,  activation temperature, , and chitosan dosage, .  The ANFILS models showed better modeling abilities of the adsorption data on the synthesized composite adsorbent than those of ANN for reason of lower % mean absolute deviation, lower % error value, higher coefficient of determination, , amongst others and lower error functions’ values than those obtained using ANN for both responses.  When applied RSM, the hybrid of RSM–genetic algorithm (RSM–GA) and ANFILS 3–D surface pot optimization technique to determine the optimal conditions for both responses, ANFILS was adjudged the best.  The ANFILS predicted optimal conditions were = 116.00 mg/L, = 2.06 h, = 81.2oC and = 5.20 g.  Excellent agreements were achieved between the predicted responses of 99.055% removal of naphthalene and 248.6375 mg/g adsorption capacity and their corresponding experimental values of 99.020% and 248.86 mg/g with % errors of -0.0353 and 0.0894 respectively.  Hence, in this study, ANFILS has been successfully used to model and optimize the conditions for the treatment of industrial wastewater containing polycyclic aromatic compounds, especially naphthalene and is hereby recommended for such and similar studies.

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

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

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.

Synergistic degradation of 4-chlorophenol by persulfate and oxalic acid mixture with heterogeneous Fenton like system for wastewater treatment: Adaptive neuro-fuzzy inference systems modeling

The 4-chlorophenol (4-CP) is known to be a highly toxic compound having harmful effects on human health and the environment. Due to adverse effect of 4-CP, a new combination of persulfate (PS) and oxalic acid (OA) with heterogeneous Fenton like (HFL) system was developed and applied for 4-CP degradation as an emerging contaminant from synthetic wastewater. The individual (OA, PS, and HFL) and combined (HFL/OA, HFL/PS, and HFL/OA/PS) systems were investigated under various conditions to synergistic effects verification and determination of degradation mechanism of 4-CP. Compared to individual and combined systems, significant synergetic of 4-CP degradation efficiency was observed by HFL/OA/PS system. The highest 4-CP degradation efficiency by HFL/OA/PS system under optimal conditions (solution pH: 6, H₂O₂ dose: 275 mg/L, goethite dose: 125 mg/L, OA dose: 50 mg/L and PS dose: 100 mg/L) with an initial 4-CP concentration of 30 mg/L was 99.6 ± 4.9% after 35 min reaction time. 4-CP degradation by HFL/OA/PS system was followed with the first-order kinetic. The application of radical scavengers including ethanol (EtOH) and tert-butyl alcohol (TBA) revealed that the SO₄^•- radical was determined as primary produced radical species. The Cl- ions release was measured during degradation reaction at various 4-CP concentrations and indicating the complete 4-CP degradation. The developing of the adaptive neuro-fuzzy inference system (ANFIS) for 4-CP degradation efficiency prediction was revealed. These results show that prediction of 4-CP degradation efficiency using HFL/OA/PS system is possible by the ANFIS model with a high accuracy (R²: 0.98).

Performance comparison of wavelet neural network and adaptive neuro-fuzzy inference system with small data sets

In this work, performance of wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were compared with small data sets by different criteria such as second order corrected Akaike information criterion (AICc), Bayesian information criterion (BIC), root mean squared error (RMSE), mean absolute relative error (MARE), coefficient of determination (R²), external Q² function ( [Formula: see text] ) and concordance correlation coefficient (CCC). Another criterion was the over-fitting. Ten data sets were selected from literature and their data were divided into training, test, and validation sets. Network parameters were optimized for WNN and ANFIS models and the best architectures with the lowest errors were selected for each data set. A precise survey of the number of permitted adjustable parameters (NPAP) and the total number of adjustable parameters (TNAP) in WNN and ANFIS models was shown that 60% of the ANFIS models and 30% of the WNN models had over-fitting. As a rule of thumb, to avoid over-fitting it is suggested that the ratio of the number of observations in training set to the number of input neurons must be greater than 10 and 20 for WNN and ANFIS, respectively. The smaller ratio required in WNN indicates its flexibility vs. ANFIS that relates to differences in structure and connections in the both networks.

Analysis of indium (III) adsorption from leachates of LCD screens using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANIFS)

Ten different adsorbent materials were tested to adsorb indium (III) from leachates of LCD screens, aiming to concentrate this valuable material. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANIFS) were applied to analyze the indium (III) adsorption. The input variables for the network models were: specific surface area, point of zero charge, adsorbent dosage and contact time. Adsorption capacity (q) was used as output variable. The adsorption capacity values ranged from 8.203 to 1000 mg g^-1. The ANN modeling presented the best fit when the Levenberg-Marquardt algorithm was used. The ANFIS modeling presented the optimum performance when the hybrid method was used. Among the tested adsorbents, chitosan presented the best performance; attaining adsorption capacity of 1000 mg g^-1 within 20 min. This is an excellent value since the maximum indium concentration in LCD screens is 0.613 mg g^-1. This high capacity was attributed to the coordination ligation between chitosan and indium (III).

Adsorptive removal of Pb (II) by means of hydroxyapatite/chitosan nanocomposite hybrid nanoadsorbent: ANFIS modeling and experimental study

In the current study, the prediction efficiency of lead adsorption by highly functional nanocomposite adsorbent of hydroxyapatite (HAp)/chitosan using ANFIS system was investigated. In this regard, the nanocomposite was applied in order to investigate the lead adsorption capacity. The operational conditions were pH (2-6), contact time between lead ions and adsorbent (15-360 min), shaker velocity (80-400 rpm), temperature (25-55 °C), amount of adsorbent (0.01-1.5 g), lead initial concentration (0-5000 ppm) and HAp concentration (10-75%). The effect of each parameter was investigated, and then the ANFIS was employed to model the adsorption process using the obtained experimental results. The ANFIS modeled the results with total average error and total average of absolute error less than 0.0646% and 4.2428%, respectively, for training data. Moreover, the coefficient of determination for training data and testing data were found to be 0.9999 and 0.9823, respectively. In addition, granular chitosan and HAp nanoparticles adsorption capabilities were compared with nanocomposite of HAp (20%wt)/chitosan adsorbent. It was found that nanocomposite adsorbent had a higher adsorption capability than other adsorbents.

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.

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.

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).

Application of least squares support vector regression and linear multiple regression for modeling removal of methyl orange onto tin oxide nanoparticles loaded on activated carbon and activated carbon prepared from Pistacia atlantica wood

Two novel and eco friendly adsorbents namely tin oxide nanoparticles loaded on activated carbon (SnO2-NP-AC) and activated carbon prepared from wood tree Pistacia atlantica (AC-PAW) were used for the rapid removal and fast adsorption of methyl orange (MO) from the aqueous phase. The dependency of MO removal with various adsorption influential parameters was well modeled and optimized using multiple linear regressions (MLR) and least squares support vector regression (LSSVR). The optimal parameters for the LSSVR model were found based on γ value of 0.76 and σ(2) of 0.15. For testing the data set, the mean square error (MSE) values of 0.0010 and the coefficient of determination (R(2)) values of 0.976 were obtained for LSSVR model, and the MSE value of 0.0037 and the R(2) value of 0.897 were obtained for the MLR model. The adsorption equilibrium and kinetic data was found to be well fitted and in good agreement with Langmuir isotherm model and second-order equation and intra-particle diffusion models respectively. The small amount of the proposed SnO2-NP-AC and AC-PAW (0.015 g and 0.08 g) is applicable for successful rapid removal of methyl orange (>95%). The maximum adsorption capacity for SnO2-NP-AC and AC-PAW was 250 mg g(-1) and 125 mg g(-1) respectively.

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

In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1g), contact time (1-40min) and initial MG concentration (5, 10, 20, 70 and 100mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R(2)) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8mg/g at 25°C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model.

Comparison of ultrasonic with stirrer performance for removal of sunset yellow (SY) by activated carbon prepared from wood of orange tree: artificial neural network modeling

The present work focused on the removal of sunset yellow (SY) dye from aqueous solution by ultrasound-assisted adsorption and stirrer by activated carbon prepared from wood of an orange tree. Also, the artificial neural network (ANN) model was used for predicting removal (%) of SY dye based on experimental data. In this study a green approach was described for the synthesis of activated carbon prepared from wood of an orange tree and usability of it for the removal of sunset yellow. This material was characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The impact of variables, including initial dye concentration (mg/L), pH, adsorbent dosage (g), sonication time (min) and temperature (°C) on SY removal were studied. Fitting the experimental equilibrium data of different isotherm models such as Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models display the suitability and applicability of the Langmuir model. Analysis of experimental adsorption data by different kinetic models including pseudo-first and second order, Elovich and intraparticle diffusion models indicate the applicability of the second-order equation model. The adsorbent (0.5g) is applicable for successful removal of SY (>98%) in short time (10min) under ultrasound condition.

Removal of methylene blue from aqueous solution by wood millet carbon optimization using response surface methodology

The use of cheep, non-toxic, safe and easily available adsorbent are efficient and recommended material and alternative to the current expensive substance for pollutant removal from wastewater. The activated carbon prepared from wood waste of local tree (millet) extensively was applied for quantitative removal of methylene blue (MB), while simply. It was used to re-used after heating and washing with alkaline solution of ethanol. This new adsorbent was characterized by using BET surface area measurement, FT-IR, pH determination at zero point of charge (pHZPC) and Boehm titration method. Response surface methodology (RSM) by at least the number of experiments main and interaction of experimental conditions such as pH of solution, contact time, initial dye concentration and adsorbent dosage was optimized and set as pH 7, contact time 18 min, initial dye concentration 20 ppm and 0.2 g of adsorbent. It was found that variable such as pH and amount of adsorbent as solely or combination effects seriously affect the removal percentage. The fitting experimental data with conventional models reveal the applicability of isotherm models Langmuir model for their well presentation and description and Kinetic real rate of adsorption at most conditions efficiently can be represented pseudo-second order, and intra-particle diffusion. It novel material is good candidate for removal of huge amount of MB (20 ppm) in short time (18 min) by consumption of small amount (0.2 g).