Eco-friendly approach to mineralise 2-nitroaniline using subcritical water oxidation method: use of ANN and RSM in the optimisation and modeling of the process
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In this study, the mineralisation of 2-nitroaniline was investigated using the eco-friendly subcritical water oxidation method and the effective oxidising agent, H2O2. Central composite design was utilized to examine the effect of temperature, oxidant concentration, and treatment time on the mineralisation of 2-nitroaniline, to optimise the experimental process and to propose a theoretical equation of the chemical oxygen demand removal percentage. ANOVA test was performed to evaluate the reliability of the process. F and p values were obtained as 23.03 and < 0.0001, respectively. R2 and adjusted R2 were obtained as 0.9540 and 0.9126, respectively. Artificial neural network modeling was used to determine the predicted values. The efficiency of central composite design and artificial neural network was statistically compared as well as by closeness of their predicted values to the experimental values. The maximum chemical oxygen demand removal percentages of 2-nitroaniline at 473 K of temperature, 30 min of treatment time, and 30 mM of H2O2 concentration were found to be 80.15 and 78.03% according to the predicted results of central composite design and artificial neural network. Removal of 2-nitroaniline was also followed using UV–Vis, FT-IR, and NMR spectroscopy. 2-Nitroaniline was removed by 99.88% at 473 K of temperature, 90 min of treatment time, and 120 mM of H2O2. Mineralisation and removal of 2-nitroaniline were also supported by FT-IR and NMR analyses.
Keywords2-Nitroaniline Mineralisation Subcritical water Artificial neural network Response surface methodology
This academic work was linguistically supported by the Mersin Technology Transfer Office Academic Writing Center of Mersin University.