Machine learning-based optimization for catalytic sulfur removal: Computational modeling and analysis of fuel purification for reduction of environmental impacts

Hydrodesulfurization (HDS) process is an important process for separation of sulfur compounds from petroleum-based products due to operational and environmental problems that the sulfur compounds can cause. In this study, this process was evaluated to optimize its performance in removing sulfur compounds from petroleum to reduce its adverse effects. Multiple machine learning models were implemented for optimization of HDS process considering several inputs/outputs. Each data point has four input parameters: pressure, temperature, initial sulfur content of petroleum, and dosage of catalyst in the reactor. Sulfur concentration (ppm), SO2 emission percentage (%), and HDS cost ($) are also outputs to be optimized by the machine learning models. Multi-layered perceptron (MLP), Multi-task Lasso (MTL), and Gaussian process regression (GPR) are core models in this study developed for the first time for HDS process. These models were optimized utilizing Artificial Bee Colony (ABC) and applied on cleansed and normalized dataset. According to assessments done on final models, sulfur concentration, emission %, and HDS cost are predicted by R2-scores of 0.983, 0.999, and 0.990 respectively using models proposed in this study. Also, absence of overfitting can be guaranteed using these models according to analysis done in results section.