Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2 in Suspension and Prediction by Artificial Neural Networks

In this study, the photocatalytic degradation of oxytetracycline (OTC) in aqueous solutions has been studied under different conditions such as initial pollutant concentrations, amount of catalyst, and pH of the solution. Experimental results showed that photocatalysis was clearly the predominant process in the pollutant degradation, since OTC adsorption on the catalyst and photolysis are negligible. The optimal TiO₂ concentration for OTC degradation was found to be 1.0 g/L. The apparent rate constant decreased, and the initial degradation rate increased with increasing initial OTC concentration with the other parameters kept unchanged. Subsequently, data obtained from photocatalytic degradation were used for training the artificial neural networks (ANN). The Levenberg–Marquardt algorithm, log sigmoid function in the hidden layer, and the linear activation function in the output layer were used. The optimized ANN structure was four neurons at the input layer, eighteen neurons at the hidden layer, and one neuron at the output layer. The application of 18 hidden neurons allowed to obtain the best values for R² and the mean squared error, 0.99751 and 7.504e–04, respectively, showing the relevance of the training, and hence the network can be used for final prediction of photocatalytic degradation of OTC with suspended TiO₂.     

Enhanced Adsorption of Hexavalent Chromium onto Magnetic Calcium Ferrite Nanoparticles: Kinetic,Isotherm, and Neural Network Modeling

Calcium ferrite nanoparticles with super-paramagnetic behavior were synthesized via simple chemical precipitation method for effective removal of hexavalent chromium from aqueous media. The properties of synthesized nanoparticles were studied by X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), Fourier transform infrared (FTIR) spectroscopy, Brunauer-Emmett-Teller (BET), and vibrating sample magnetometer (VSM) measurements. The ferrite nanoparticles have shown polycrystalline nature and high BET specific surface area (229.83 m²/g) with active functional groups on the surface. The adsorption process follows second-order kinetics with the involvement of intra-particle diffusion and adsorption capacity as much as 124.11 mg/g was determined from the Langmuir isotherm. The thermodynamic analysis revealed that the adsorption process was feasible, spontaneous, and exothermic in nature. A three-layer feed-forward back-propagation artificial neural network (ANN) model was employed to predict the removal (%) of Cr(VI) ions as output. Optimal ANN network (4:8:1) shows the minimum mean squared error (MSE) of 0.00161 and maximum coefficient of determination (R²) of 0.984. The adsorption process is mostly influenced by solution pH and followed by adsorbent dosage, initial Cr(VI) concentration, and contact time as illustrated by sensitivity analysis. With small size and high surface area, biocompatibility, ecofriendly nature, easy magnetic separation, and enhanced adsorption capacity towards Cr(VI), calcium ferrite nanoparticles will find its potential application in wastewater remediation.     

Ultrasonic treatment of water contaminated with various pollutants onto copper nanowires loaded on activated carbon using response surface methodology and artificial intelligent

In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three‐layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R² and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.     

Adsorption of Remazol Brilliant Blue R on activated carbon prepared from a pine cone

In this work the activated pine cone was investigated as adsorbent to remove Remazol Brilliant Blue R from aqueous solution. The effects of various experimental parameters such as initial dye concentration, contact time, temperature, adsorbent dose and pH were examined. The adsorption of Remazol Brilliant Blue R from aqueous solution was increased with the increase in initial dye concentration, contact time, temperature, adsorbent dose but decreased with the increase in the pH. The results in this study indicated that the activated pine cone was an attractive candidate for removing Remazol Brilliant Blue R.     

Adsorption characteristics of Titan yellow and Congo red on CoFe2O4 magnetic nanoparticles

This paper assesses the adsorption characteristics of Titan yellow and Congo red on CoFe₂O₄ magnetic nanoparticles. The adsorption behavior of Titan yellow and Congo red from aqueous solution onto CoFe₂O₄ magnetic nanoparticles has been determined by investigating the effects of pH, concentration of the dye, amount of adsorbent, contact time, ionic strength and temperature. Experimental results indicated that CoFe₂O₄ nanoparticles can remove more than 98 % of each dye under optimum operational conditions of a dosage of 15.0 mg CoFe₂O₄, pH 3.0, initial dye concentration of 22–140 mg L^?1, and contact times of 2.0 and 15.0 min for Congo red and Titan yellow, respectively. Langmuir and Freundlich isotherm models have been used to evaluate the ongoing adsorption kinetic equations. Regeneration of the saturated adsorbent was possible by NaCl/acetone solution as eluent. The maximum adsorption capacities were 200.0 and 212.8 mg dye per gram adsorbent for Congo red and Titan yellow, respectively. With the help of adsorption isotherm, thermodynamic parameters such as free energy, enthalpy and entropy have been calculated. On the basis of pseudo-first-order and pseudo-second-order kinetic equations, different kinetic parameters have been obtained.     

Simple Chemical Route Synthesis of Fe2O3 Nanoparticles and its Application for Adsorptive Removal of Congo Red from Aqueous Media: Artificial Neural Network Modeling

Nanocrystalline Fe₂O₃ powder was synthesized by a simple chemical route involving FeCl₃ and NaOH. The Fe₂O₃ powder thus prepared was characterized using x-ray diffraction study, scanning electron microscopy, and Fourier transform infrared spectroscopy. The adsorption properties of crystalline Fe₂O₃ powder have been investigated with an aim to explore a possible low cost and efficient way to remove Congo red (CR) from waste water. Fe₂O₃ powder was found as an excellent adsorbent for CR from aqueous medium. Adsorption capacity as much as 203.66 mg g^?1 is reported at room temperature. Effect of different experimental parameters such as reaction pH, initial CR dye concentration, adsorbent dose, and reaction temperature were studied on adsorption capacity of Fe₂O₃ powder and modeled by artificial neural network (ANN). Optimal ANN structure (4–5–1) shows minimum mean squared error (MSE) of 0.00235 and determination coefficient (R²) of 0.991 with Levenberg–Marquardt algorithm. Isotherm analysis of experimental data exhibited better fit to the Langmuir isotherm. The adsorption process was found to follow second-order kinetics as depicted by the analysis of experimental results. Thermodynamic study shows that the adsorption process is endothermic, spontaneous, and thermodynamically favorable in the temperature range of 27°C to 60°C.     

Low-temperature synthesis of nanoparticle-assembled, transparent, and low-crystallized hydroxyapatite blocks

The mesoporous carbon CMK-3 adsorbent was prepared, characterized, and used for the removal of anionic methyl orange dye from aqueous solution. Adsorption experiments were carried out as batch studies at different contact time, pH, initial dye concentration, and salt concentration. The dye adsorption equilibrium was rapidly attained after 60 min of contact time. Removal of dye in acidic solutions was better than in basic solutions. The adsorption of dye increased with increasing initial dye concentration and salt concentration. The equilibrium data were analyzed by the Langmuir and Freundlich models, which revealed that Langmuir model was more suitable to describe the methyl orange adsorption than Freundlich model. Experimental data were analyzed using pseudo-first-order and pseudo-second-order kinetic models. It was found that kinetics followed a pseudo-second-order equation. Thermodynamic study showed that the adsorption was a spontaneous and exothermic process.     

Adsorption of Hazardous Azorhodanine Dye from an Aqueous Solution Using Rice Straw Fly Ash

The potential of using rice straw fly ash (RSFA) as low-cost adsorbents for the removal of hazardous azorhodanine (AR) dye from aqueous solution was investigated. The effects of different variables in the batch method as a function of solution pH, contact time, concentration of adsorbate, adsorbent dosage, and temperature were investigated, and optimal experimental conditions were ascertained: 0.05 g for initial dye concentration of 20–100 mg/L at pH 2. The experimental equilibrium data were tested by the isotherm models, namely the Langmuir and Freundlich adsorption and the isotherm constants were determined. The kinetic models, pseudo-first-order and pseudo-second-order, were employed to analyze the kinetic data. The activation energy of adsorption was also evaluated and found to be +10.89 kJ.mol^?1, indicating that the adsorption is physisorption. Various thermodynamic parameters, such as Gibbs free energy, entropy, and enthalpy of the ongoing adsorption process, have been calculated and found to be spontaneous and exothermic, respectively.     

壳聚糖插层蒙脱土复合材料吸附酸性染料的动力学和热力学研究

通过离子交换的方法将天然大分子壳聚糖引入蒙脱土的层间,利用XRD和TG对得到的复合材料进行表征。将该复合材料用于酸性黑10B的吸附,考查了pH值、染料起始浓度、吸附时间以及温度对吸附性能的影响。结果表明,吸附动力学实验数据符合准二级动力学模型。应用Langmuir和Freundlich两种模型描述吸附等温线,结果显示Langmuir模型的拟合效果更好。计算得到吸附过程的热力学参数△Go、△Ho和△So的值分别为-27.16kJ/mol(20℃)、-14.18kJ/mol和44.0J/mol.K,说明复合材料对酸性染料的吸附是一个自发进行的放热过程。     

Removal of Reactive Yellow 84 from aqueous solutions by adsorption onto hydroxyapatite

The adsorption of a reactive dye, Reactive Yellow 84, from aqueous solution onto synthesized hydroxyapatite was investigated. The experiments were carried out to investigate the factors that influence the dye uptake by the adsorbent, such as the contact time under agitation, absorbent dosage, initial dye concentration, temperature and pH of dye solution. The experimental results show that the amount of dye adsorbed increases with an increase in the amount of hydroxyapatite. The maximum adsorption occurred at the pH value of 5. The equilibrium uptake was increased with an increase in the initial dye concentration in solution. The experimental isotherm data were analyzed using Langmuir isotherm equation. The maximum monolayer adsorption capacity was 50.25 mg/g. The adsorption has a low temperature dependency and was endothermic in nature with an enthalpy of adsorption of 2.17 kJ mol⁻¹.     

The removal of uranium(VI) from aqueous solutions onto activated carbon: kinetic and thermodynamic investigations

The adsorption of uranium(VI) from aqueous solutions onto activated carbon has been studied using a batch adsorber. The parameters that affect the uranium(VI) adsorption, such as contact time, solution pH, initial uranium(VI) concentration, and temperature, have been investigated and optimized conditions determined (contact time 240 min; pH 3.0+/-0.1; initial uranium concentration 100 mg/L; temperature 293.15 K). The experimental data were analyzed using sorption kinetic models (pseudo-first- and pseudo-second-order equations) to determine the equation that fits best our experimental results. Equilibrium isotherm studies were used to evaluate the maximum sorption capacity of activated carbon and experimental results showed this to be 28.30 mg/g. The Freundlich, Langmuir, and Dubinin-Radushkevich (D-R) models have been applied and the data correlate well with Freundlich model and that the sorption is physical in nature (the activation energy Ea=7.91 kJ/mol). Thermodynamic parameters (DeltaHads0=-50.53 kJ/mol, DeltaSads0=-98.76 J/mol K, DeltaGads(293.15 K)0=-21.61 kJ/mol) showed the exothermic heat of adsorption and the feasibility of the process.     

Artificial Neural Network (ANN) Method for Modeling of Sunset Yellow Dye Adsorption Using Nickel Sulfide Nanoparticle Loaded on Activated Carbon: Kinetic and Isotherm Study

In this paper, the NiS nanoparticles are prepared and characterized by x-ray powder diffraction and scanning electron microscopy. The NiS nanoparticles showed the excellent adsorption properties toward sunset yellow (UA) dye. The effect of solution pH, adsorbent dosage (0.005–0.020 g), contact time (0.5–30 minutes), and initial UA concentration (5–40 mg L^?1) on the extent of adsorption was investigated and modeled by artificial neural network. The experimental equilibrium data was analyzed by Langmuir, Freundlich, Tempkin, and D–R isothermal models. It was seen that the data was well presented by Langmuir model with a maximum adsorption capacity of 333.3 mg g^?1 at 26°C. Kinetic studies at various adsorbent dosages and initial UA concentrations show that high removal percentage (>90%) was achieved within 15 minutes. The adsorption of UA follows the pseudo-second-order rate model. The experimental data were applied to train the multilayer feed-forward neural network with three inputs and one output with Levenberg–Marquart algorithm and different numbers of neurons in the hidden layer. The minimum mean square error of 0.0003 and determination coefficient of (R²) 0.99 were found.     

羧基化石墨烯对4种离子型染料的吸附脱色

合成的羧基化石墨烯（G-COOH）用FT-IR进行表征,并对G-COOH用于水溶液中甲基紫、中性红、灿烂黄和茜素红4种离子型染料的吸附性能进行了研究。 考察了吸附剂用量、吸附时间、初始浓度以及溶液pH值等条件对吸附效果的影响。 同时,研究了甲基紫染料的脱附性能,结果表明,用NaOH/EtOH混合溶液洗脱甲基紫,洗脱率可达88.2%,洗脱后的G-COOH可再利用。 从热力学角度探讨得出,G-COOH对阳离子染料甲基紫和中性红的吸附行为能够较好的符合Langmuir等温吸附模型,而对阴离子染料灿烂黄和茜素红的吸附行为则能够较好的符合Freundlich等温吸附模型,计算的吸附参数表明,G-COOH对4种染料的吸附过程容易进行。 动力学研究表明,G-COOH对4种离子型染料的吸附行为均能较好的符合准二级吸附模型。 该实验研究表明,在处理染料废水时,G-COOH为相当优异的吸附剂。     

On the adsorption and diffusion of Methylene Blue in glass fibers

The adsorption and diffusion phenomena of Methylene Blue dye on glass fiber have been explored. Both isotherm and kinetics have been studied. Effect of process parameters such as pH, adsorbent loading, and initial dye concentration was investigated. Temperature had a little effect on adsorption. A diffusion model was developed to estimate the diffusivity of the dye in glass fiber. The diffusivity was found to be varying exponentially with the dye concentration.     

Optimization of a stability-indicating HPLC method for the simultaneous determination of rifampicin, isoniazid, and pyrazinamide in a fixed-dose combination using artificial neural networks

The aim of this study is to develop and optimize a simple and reliable high-performance liquid chromatography (HPLC) method for the simultaneous determination of rifampicin (RIF), isoniazid (INH), and pyrazinamide (PZA) in a fixed-dose combination. The method is developed and optimized using an artificial neural network (ANN) for data modeling. Retention times under different experimental conditions (solvent, buffer type, and pH) and using four different column types (referred to as the input and testing data) are used to train, validate, and test the ANN model. The developed model is then used to maximize HPLC performance by optimizing separation. The sensitivity of the separation (retention time) to the changes in column type, concentration, and type of solvent and buffer in the mobile phase are investigated. Acetonitrile (ACN) as a solvent and tetrabutylammonium hydroxide (tBAH), used to adjust pH, have the greatest influence on the chromatographic separation of PZA and INH and are used for the final optimization. The best separation and reasonably short retention times are produced on the micro-bondapak C18, 4.6 x 250-mm column, 10 microm/125 A using ACN-tBAH (42.5:57.5, v/v) (0.0002M) as the mobile phase, and optimized at a final pH of 3.10.     

Study of Removal of Cationic Dye from Waste Aqua Using Chromatographic Grade Silica as Adsorbent

The adsorption of cationic dye, Basic Red 13 from aqueous solution onto silica has been studied. The time growth adsorption as a function of the amount of adsorbent, pH, initial dye concentration as well as the temperature was measured of the system. Adsorption was found to follow a first-order process. Various isotherm and thermodynamic parameters for the adsorption of dye have been calculated. Isotherms were used to identify formation of either monolayer or multilayer formation whereas thermodynamic and other parameter indicated that the dye adsorption process was physisoption as well as spontaneous and exothermic in nature.     

Adsorption of Triarylmethane Dye on Ca-Montmorillonite: Equilibrium,Kinetics, and Thermodynamics

The equilibrium, kinetics, and thermodynamics of adsorption of triarylmethane dye, crystalline violet, from aqueous solutions on Ca-montmorillonite have been studied. The regularities of the influence of the physicochemical parameters (suspension pH, clay and dye concentrations, temperature, and contact time) on the specific adsorption value of the dye have been found. It has been shown that Ca-montmorillonite is capable of removing the dye from aqueous solutions in a wide range of concentrations with an efficiency of up to 99.8%. The adsorption of crystalline violet obeys the Langmuir model (the correlation coefficient is 0.999), which corresponds to monolayer adsorption on a homogeneous surface. The kinetics of dye adsorption is described by a pseudo-second-order equation, which is characteristic of chemisorption. The thermodynamic parameters of adsorption, ΔH = 40.42 kJ/mol, ΔS = 139.6 J/mol, and ΔG =–4.68 kJ/mol (323 K), have been determined and lead to the conclusion that the dye adsorption is a spontaneous endothermic process.     

Modeling of fixed-bed adsorption of Cs+ and Sr2+ onto clay–iron oxide composite using artificial neural network and constant–pattern wave approach

A low-cost, non-toxic and effective adsorbent constituted by a montmorillonite coated by iron oxides (montmorillonite–iron oxide composite) was prepared to assess its effectiveness in the removal of Cs⁺ and Sr²⁺ from aqueous solution. Dynamic adsorption experiments were carried out at room temperature under the effect of various operating parameters such as bed depth Z (5–15 cm), initial cation concentration C ₀ (2–50 mg L^?1) and volumetric flow rate Q (0.5–8 mL min^?1). Column performance has been modeled with constant-pattern wave approach combined to the Freundlich isotherm model and artificial neural network (ANN) models. The time, initial cation concentration, bed depth and volumetric flow rate were chosen as the input variables whereas, the outlet concentration C _t was considered as the output variable. The developed network was found to be useful in predicting the breakthrough curves. Experimental data for the used system were well fitted with ANN than the combined constant–pattern wave approach.     

Removal of Reactofix Navy Blue 2 GFN from aqueous solutions using adsorption techniques

The wheat husk, an agricultural by-product, has been activated and used as an adsorbent for the adsorption of Reactofix Navy Blue 2 GFN from aqueous solution. In this work, adsorption of Reactofix Navy Blue 2 GFN on wheat husk and charcoal has been studied by using batch studies. The equilibrium adsorption level was determined to be a function of the solution pH, adsorbent dosage, dye concentration and contact time. The equilibrium adsorption capacities of wheat husk and charcoal for dye removal were obtained using Freundlich and Langmuir isotherms. Thermodynamic parameters such as the free energies, enthalpies and entropies of adsorption were also evaluated. Adsorption process is considered suitable for removing color, COD from waste water.     

Application of artificial neural network for comparison and modeling of the ultrasonic and stirrer assisted removal of anionic dye using activated carbon supported with nanostructure material

In this study, a green approach has been described for the synthesis of copper sulfide nanoparticles loaded on activated carbon (CuS‐NP‐AC) and usability of it for the removal of sunset yellow (SY) dye by ultrasound‐assisted and stirrer has been compared. In addition, the artificial neural network (ANN) model has been employed for a forecasting removal percentage of SY dye using the results obtained. 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 was 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 of 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 (0.005 g) is applicable for successful removal of SY dye (> 98%) in short time (9 min) under ultrasound condition. A three layer ANN models with 8 and 6 neurons at hidden layer was selected as optimal models using stirrer and ultrasonic, respectively. These models displayed a good agreement between forecasted data and experimental data with the determination coefficient (R²) of 0.9948 and 0.9907 and mean squared error (MSE) of 0.0001 and 0.0002 for training set using stirrer and ultrasonic, respectively.