Separation of sulfur compounds from petroleum using hydrodesulfurization method to reduce SO2 emission: Process optimization and validation

Abstract

Preparation of clean fuels from petroleum need decent removal of sulfur compounds from the petroleum which can be done using the well-known hydrodesulfurization (HDS) method. The removal of sulfur compounds from crude oil would be essential in order to control the emission of gas pollutants such as SO2. However, due to the complexity of the sulfur separation process, predictive modeling and computations are required to improve the separation efficiency. In this research, we have collected some experimental data for optimization of separation process. Each data point has four input features: temperature, pressure, initial sulfur content, and dose. On the other hand, the outputs included left sulfur amount in the feed, value of SO2 emission, and process cost ($). Adaboost technique integrated with three core models of DT, Lasso, and KNN is used for modeling. The models are tuned using LOA method on the available dataset, thereby the optimum models’ parameters were determined for the best fitting. For sulfur concentration and emission parameters, the ADA-DT technique is the best one among other methods, but for the HDS cost, the ADA-LASSO framework works the best. Using these models, the R2-score for outputs is, respectively, 0.940, 0.923, and 0.999. This work makes significant contributions by providing accurate predictive models that would enhance the understanding and optimization of sulfur separation process. These models pave the way for more efficient and environmentally-friendly production of clean fuels from petroleum, contributing to reduced gas pollutants emissions.

Keywords

• Separation
• Hydrodesulfurization
• Process optimization
• Machine learning
• Modeling