Theoretical investigations on the purification of petroleum using catalytic hydrodesulfurization process: AI Optimization of SO2 emission and process cost

Abstract

Optimization of SO2 emission and the process cost in catalytic hydrodesulfurization (HDS) is of great importance for the petroleum industry. Given that the process of HDS is complicated, machine learning-based models are suitable for the purpose of process optimization in which the cost and separation efficiency can be optimized efficiently. In this investigation, we are working with a data collection on the HDS process to model via machine learning models. Pressure, temperature, initial sulfur content, and catalyst dose constitute the inputs for the models. Outputs include sulfur concentration (ppm), emission of gas (%), and HDS process cost ($). To model the process, for the first time, four tree-based ensemble methods are developed including Gradient Boosting, Extreme gradient boosting, Random Forest, and Extra Trees to optimize the HDS process. The models tuned on the available dataset and then the best ones selected for each output For sulfur concentration the extra tree model is the most accurate and for other outputs extreme gradient boosting has the best performance. For the models, the R2 scores for outputs are 0.983, 0.982, and 0.995, respectively.