RESUMEN
In this study, polypyrrole/carboxymethyl cellulose nanocomposite particles (PPy/CMC NPs) were synthesized and applied for removal of reactive red 56 (RR56)and reactive blue 160 (RB160) as highly toxic dyes. The amount of CMC was found significantly effective on the surface adsorption efficiency. Different optimization methods including the genetic programming, response surface methodology, and artificial neural network (ANN) were used to optimize the effect of different parameters including pH, adsorption time, initial dye concentration and adsorbent dose. The maximum adsorption of RR56 and RB160 were found under the following optimum conditions: pH of 4 and 5, adsorption time of 55 min and 52 min for RR56 and RB160, respectively, initial dye concentration of 100 mg/L and adsorbent dose of 0.09 g for both dyes. were obtained for RR56 and RB160, respectively. Also, the results indicated that ANN method could predict the experimental adsorption data with higher accuracy than other methods. The analysis of ANN results indicated that the adsorbent dose is the main factor in RR56 removal, followed by time, pH and initial concentration, respectively. However, initial concentration mostly determines the RB160 removal process. The isotherm data for both dyes followed the Langmuir isotherm model with a maximum adsorption capacity of 104.9 mg/g and 120.7 mg/g for RR56 and RB160, respectively. In addition, thermodynamic studies indicated the endothermic adsorption process for both studied dyes. Moreover, DFT calculations were carried out to obtain more insight into the interactions between the dyes and adsorbent. The results showed that the hydrogen bondings and Van der Waals interactions are dominant forces between the two studied dyes and PPy/CMC composite. Furthermore, the interaction energies calculated by DFT confirmed the experimental adsorption data, where PPy/CMC resulted in higher removal of both dyes compared to PPy. The developed nanocomposite showed considerable reusability up to 3 cylces of the batch adsorption process.
Asunto(s)
Carboximetilcelulosa de Sodio/química , Colorantes/química , Nanocompuestos/química , Adsorción , Compuestos Azo , Bencenosulfonatos , Teoría Funcional de la Densidad , Cinética , Nanopartículas , Polímeros , Pirroles , Termodinámica , Contaminantes Químicos del AguaRESUMEN
Presence of pigments and dyes in water bodies are growing tremendously and pose as toxic materials and have severe health effects on human and aquatic creatures. Treatments methods for removal of these toxic dyes along with other pollutants are growing in different dimensions, among which adsorption was found a cheaper and efficient method. In this study, the performance of polyaniline-based nano-adsorbent for removal of methyl orange (MO) dye from wastewater in a batch adsorption process is studied. Along with this to minimize the number of experiments and obtain optimal conditions, a multivariate predictive model based on response surface methodology (RSM) is developed. This is compared with data-driven modeling using the artificial neural network (ANN) which is integrated with differential evolution optimization (DEO) for prediction of the adsorption of MO. The interactive effects on MO removal efficiency with respect to independent process variables were investigated. The fit of the predictive model was found to good enough with R2â¯=â¯0.8635. The optimal ANN architecture with 5-12-1 topology resulted in higher R2 and lower RMSE of 0.9475 and 0.1294 respectively. Pearson's Chi-square measure which provides a good measurement scale for weighing the goodness of fit is found to be 0.005 and 0.038 for RSM and ANN-DEO respectively, and other statistical metrics evaluated in this study further confirms that the ANN-DEO is very superior over RSM for model predictions.
Asunto(s)
Compuestos Azo/química , Redes Neurales de la Computación , Adsorción , Compuestos de Anilina , Compuestos Azo/aislamiento & purificación , Purificación del AguaRESUMEN
In the present study, a polyaniline/carboxymethyl cellulose/TiO2 nanocomposite (PAn/CMC/TiO2) was synthesized by a polymerization method, and was used for adsorption of Congo Red from aqueous solution. The effects of operational parameters of the adsorption process including pH, initial dye concentration, temperature, adsorbent dosage, and adsorption time on adsorption efficiency were investigated, and response surface methodology was used for their optimization. Optimal adsorption conditions were determined at pH of 2.6, initial concentration of 82mgL, temperature of 56⯰C, adsorption time of 24â¯min, and adsorbent dose of 0.14â¯g. In addition, the system was also simulated using artificial neural network (ANN) and genetic programming (GP). It was found that the behavior of the system could be well predicted by ANN using 5, 1 and 8 neurons for input, middle and output layers, respectively. Kinetic and isothermal analyses showed that the maximum adsorption capacities were obtained at 94.28, 97.53 and 119.9 mgg by Langmuir model at temperatures of 25, 40 and 50⯰C, respectively and that adsorption kinetics followed the pseudo-second-order model. The nano-adsorbent was also found to be reusable without a significant change in adsorption capacity for at least five adsorption-desorption cycles. Finally, the mechanism of dye adsorption on the nano-adsorbent was investigated and proposed.
RESUMEN
The present work focused on the performance of Polyaniline/SiO2 nanocomposite for removing Amido Black 10B dye from aqueous solution. The effect of different variables, such as adsorption time, the mass of adsorbent, solution pH and initial dye concentration was studied and also was optimized by an Artificial Neural Network (ANN) method. Lagergren, pseudo-second order, Intra-particle Diffusion, Elovich and Boyd models were tested to track the kinetics of the adsorption process. The experimental data were fitted to different two-parameter, and three-parameter isotherm models, namely, Langmuir, Freundlich, Temkin, D-R, Hill, Sips and Redlich-Peterson models, and their validity was examined. The results showed that the dye adsorption process was well described by Redlich-Peterson isotherm model. Thermodynamic studies revealed that the adsorption of Amido Black 10B onto Polyaniline/SiO2 nanocomposite was endothermic. The comparison of the adsorption efficiencies obtained by the ANN model and the experimental data evidenced that the ANN model could estimate the behavior of the Amido Black 10B dye adsorption process under various conditions.