Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system

Abstract

The aim of this study is to predict the efficiency of oxidative desulfurization method (in a gas–liquid oxidation system) for gas condensate using artificial intelligence (AI) systems such as Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Algorithm (GA)-Fuzzy, and GA-ANFIS. The method utilizes mixtures of H2SO4, HNO3, and NO2 as oxidant agents in various amounts. The optimal parameters of the proposed models were determined using GA, and statistical parameters such as mean absolute error, average relative deviation, and correlation coefficient were used to compare the models. The correlation coefficients for Fuzzy, ANFIS, GA-Fuzzy, and GA-ANFIS models were found to be 0.5899, 0.7831, 0.9693, and 0.9687, respectively. The results indicated that ANFIS-GA and Fuzzy-GA models can effectively predict the desulfurization efficiency of the novel technique. Furthermore, the use of GA improved the performance of the Fuzzy and ANFIS models and enhanced their prediction accuracy. Overall, this study demonstrates the potential of AI systems in predicting the efficiency of novel chemical methods for industrial applications.