Machine Learning-Enabled Prediction and Optimization of Sulfur Recovery Units: A Step towards Industry 4.0 Integration

Sulfur recovery units are integral to the oil and gas industry, addressing the release of hazardous gases, particularly hydrogen sulfide. These units facilitate the conversion of H2S into elemental sulfur, aligning with stringent environmental regulations. This study employs a machine learning algorithm to predict sulfur recovery efficiency under uncertain conditions. An industrial sulfur recovery unit was simulated in Aspen HYSYS and validated using real-world industrial data. The simulation incorporated artificial uncertainties (±5%) in key process parameters, including acid gas flow rate, acid gas temperature, air flow rate, air temperature, and Claus reactor inlet temperature. The generated data were utilized to train a Gaussian process regression model, assessed through R2 and RMSE values during validation, achieving a high predictive accuracy with an R2 value of 0.993 and RMSE value of 0.0709. In the next step, the Gaussian process regression model served as a surrogate for fitness function evaluations within a particle swarm optimization framework. The application of a hybrid optimization methodology resulted in the optimal parameter values, leading to a notable 3% increase in sulfur recovery efficiency. This study establishes the foundation for applying industry 4.0 principles to augment predictive capabilities in sulfur recovery units, thereby laying the groundwork for the development of digital twins.