Selective removal of Cr (VI) from hydrometallurgical effluent using modified cellulose nanocrystals (CNCs) with succinic anhydride and ethylenediaminetetraacetic acid: Isotherm,kinetics, and thermodynamic studies

Biological sources are renewable basic resources that may be used for several purposes, including the development of green materials for the removal of heavy metal ions. Cellulose nanocrystals (CNCs) extracted from waste papers via acid hydrolysis were modified and utilized as adsorbents to remove Cr (VI) ions from metallurgical effluent in this work. X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, thermogravimetric analysis, and zeta potentiometer were used to characterize the CNCs. The CNCs treated with succinic anhydride and ethylenediaminetetraacetic acid tetrasodium salt have thin particle sizes and are porous. The carboxylate functional group is primarily engaged in the coordination and selective removal of metal ions (–COO²⁻) and thermal degradation of 85%, observed at temperatures between 250–380°C. On the surface of the modified CNCs, the zeta potential data showed a decrease in negative value. The results revealed that the modified CNCs had a maximum adsorption capacity of 387.25 ± 0.88 mg L⁻¹ at pH 5, at CNCs doses of 25 and 400 mg L⁻¹ as starting concentrations. The adsorption equilibrium period was 300 min and the temperature was 313 K. The equilibrium results fit the Langmuir isotherm model with an R² of 0.993 and a qmax of 340 ± 0.97. The Chi-square (X²) and Marquardt's percent standard deviation tests confirmed that the adsorption process was pseudo-second-order with an R² of 0.998, and the Elovich model revealed that Cr (VI) complexed with the adsorbent's functional groups. The reaction was endothermic due to positive ΔH and spontaneous due to negative ΔG. The positive ΔS indicates that the adsorption process enhances the unpredictability of the solid/liquid interface, according to thermodynamic analysis. After acid treatment, the CNCs may be effectively reused for six cycles with an adsorption capacity of 220 ± 0.78 mg g⁻¹.     

The use of artificial neural network (ANN) in dry flue gas desulphurization modelling: Levenberg–Marquardt (LM) and Bayesian regularization (BR) algorithm comparison

This research project aims to investigate the efficacy of artificial neural networks (ANN) in mapping dry flue gas desulphurization (DFGD). Bayesian regularization (BR) and Levenberg–Marquardt (LM) training algorithms were used for DFGD modelling. The input layer feed data contained diatomite to Ca(OH)₂ ratio, hydration time, hydration temperature, sulphation temperature, and inlet gas concentration, while the output layer metadata were sorbent conversion and sulphation responses. The hyperbolic tangent (tansig), sigmoid (logsig), and linear (purelin) activation functions were compared to ascertain the best network learning model. The number of hidden layer cells also varied between 7 and 10, given the existence of multiple output feed data. BR and LM performance evaluation was based on coefficient of determination (R²), root mean square error (RMSE), and mean square error (MSE) mathematical analysis. BR was a superlative training tool compared to LM, with lower RMSE and MSE values. The goodness of fit data for both techniques was close to unity, clarifying that ANN using BR and LM tools can be used to predict DGFD outcome. The shrinking core model was used to analyze the desulphurization reaction and concluding the chemical reaction was the reaction controlling step.