Computational modeling of petroleum purification for removal of sulfur compounds: Process analysis for reduction of environmental impacts and material costs

In the course of this investigation, three machine learning models (Gaussian Process Regression, Decision Tree Regression, and Kernel Ridge Regression) were examined for determining the correlation between the input variables (x and y) which are spatial coordinates of model’s geometry, and the content of species in adsorption for sulfur capture. For the process modeling, mass transfer was analyzed, and the concentration distribution of sulfur compound was obtained via numerical solution of mass transfer equations, and then used for machine learning models. The machine learning models were trained using a dataset of 19,000 observations, and their performance was assessed through metrics including R2 score, MAE, and RMSE. Analysis of the results reveals that Decision Tree Regression surpassed the other two models in performance, with an R2 score of 0.9989, MAE of 6.64405E-01, and RMSE of 1.1277E+00. Gaussian Process Regression had an R2 score of 0.97106, MAE of 3.65541E+00, and RMSE of 5.6821E+00, while Kernel Ridge Regression had an R2 score of 0.86347, MAE of 8.26121E+00, and RMSE of 1.1330E+01. The Clonal Selection Algorithm was used for hyper-parameter optimization for all models. These findings demonstrate the potential of machine learning techniques for accurately and reliably predicting the concentration of chemical species and highlight the importance of considering the choice of model and hyper-parameter optimization for optimal performance.