Machine learning-aided kinetic optimization of the Claus process for cleaner fuel gas and sustainable sulfur recovery

The Claus process is critical for recovering sulfur from hydrogen sulfide (H2S) in natural gas processing, yet challenges in achieving complete CS2 and H2S conversion contribute to significant environmental impacts, including SO2 emissions. This study introduces a hybrid framework combining kinetic modeling with a Deep Reinforcement Learning (DRL)-enhanced Artificial Neural Network (ANN) to optimize sulfur recovery and minimize emissions, validated with operational data from an industrial gas refinery. Integrating Tong’s formulation for COS and CS2 hydrolysis and Dalla Lana’s Claus reaction model, the approach reduces CS2 concentration to below the detection limit (<0.1 ppm, per ASTM D5504), reduces H2S residual to 0.8%, and cuts SO2 emissions by 68.2%, with a predictive accuracy of R2 = 0.978. Response Surface Methodology (RSM) optimizes parameters (temperature: 200–280 °C, inflow rate: 875–950 kmol/h, pressure: 1.6–2.0 bar_abs, catalyst surface area: 30–50 m2/g), yielding 90.5% sulfur recovery. Environmental and economic benefits include 13,613 tons/year SO2 and 1,800 tons/year CO2 reductions, saving $7.33 million annually. This scalable framework enables cleaner fuel gas production and sustainable gas processing with potential for real-time control, as demonstrated by low-latency DRL-ANN (execution time <1 s per episode).