RESUMO
The discharge of textile wastewater into aquatic streams is considered a major challenge due to its effect on the water ecosystem. Direct blue 78 (DB78) dye has a complex structure. Therefore, it is difficult to separate it from industrial wastewater. In this study, carbon obtained from the pyrolysis of mixed palm seeds under different temperatures (400 °C and 1000 °C) was activated by a thermochemical method by using microwave radiation and an HCl solution in order to improve its adsorption characteristics. The generated activated carbon was used to synthesize a novel activated carbon/chitosan microbead (ACMB) for dye removal from textile wastewater. The obtained activated carbon (AC) was characterized by a physicochemical analysis that included, namely, particle size, zeta potential, SEM, EDX, and FTIR analyses. A series of batch experiments were conducted in terms of the ACMB dose, contact time, pH, and activated carbon/chitosan ratios in synthetic microbeads for enhancing the adsorption capacity. A remarkable improvement in the surface roughness was observed using SEM analysis. The particle surface was transformed from a slick surface with a minor-pore structure to a rough surface with major-pore structure. The zeta potential analysis indicated a higher improvement in the carbon surface charge, from -35 mv (before activation) to +20 mv (after activation). The adsorption tests showed that the dye-removal efficiency increased with the increasing adsorbent concentration. The maximum removal efficiencies were 97.8% and 98.4% using 3 and 4 g/L of AC400°C MB-0.3:1 and AC1000°C MB-0.3:1, respectively, with initial dye concentrations of 40 mg/L under acidic conditions (pH = 4-5), and an optimal mixing time of 50 min. The equilibrium studies for AC400°C MB-0.3:1 and AC1000°C MB-0.3:1 showed that the equilibrium data best fitted to the Langmuir isothermal model with R2 = 0.99. These results reveal that activated carbon/chitosan microbeads are an effective adsorbent for the removal of direct blue 78 dye and provide a new platform for dye removal.
RESUMO
The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.
