Abstract:
Crude oil remains a vital energy source; nevertheless, its production faces increasing
challenges due to climate change concerns and stricter environmental regulations. Ensuring
its sustainable utilization requires prioritizing practices that minimize environmental
impact. Optimizing the performance of existing infrastructure, such as Sulfur Recovery
Units (SRUs), plays a crucial role in this endeavor. SRUs play a vital role in mitigating the
environmental footprint of oil processing by capturing and converting hazardous gases like
hydrogen sulfide (H2S) into elemental sulfur. However, achieving high Sulfur Recovery
Efficiency (SRE) conflicts with economic considerations, as optimal performance requires
precise control of process parameters. This research investigates the application of machine
learning algorithms to predict SRE and optimize process parameters in SRUs. An industrial
SRU model was simulated in Aspen HYSYS and validated with industrial data. This
simulated model was then used to generate diverse datasets by incorporating artificial
uncertainties of ±5% in key process parameters like air/gas flow rates, temperatures, and
Claus reactor inlet temperature. These datasets were used to train three machine learning
models: Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and
Regression Trees (RT). The evaluation criteria for these models were R-squared (R²) and
Root Mean Squared Error (RMSE) values of the validation step. All three models achieved
high prediction accuracy, with R² values exceeding 0.97. Furthermore, the most accurate
models, GPR and ANN, were employed as surrogate models within the fitness function
evaluation of two optimization frameworks: a Genetic Algorithm (GA) and a Particle
Swarm Optimization (PSO) approach. Both algorithms demonstrated effectiveness, but
PSO exhibited marginally superior performance in optimizing the process parameters. This
proposed methodology, utilizing surrogate-based optimization, establishes a platform for
practical implementation at the plant level. It paves the way for the realization of Industry
4.0 principles within the refinery sector by enabling the optimization of existing
infrastructure for improved sustainability and environmental performance.
