Exploring the Applications of Artificial Intelligence in Sulfur Recovery Units: A Review

Abstract

The Sulfur Recovery Units, particularly the Claus process, are critical in the natural gas and petroleum industries for recovering sulfur from hydrogen sulfide (H2S) and other sulfur compounds. Even if traditional methods have been employed for decades, the integration of Artificial Intelligence (AI) is applied to sulfur recovery recently in the aim of improving efficiency, reducing costs, and enhancing environmental compliance. This literature review examines the key applications and contributions of AI in the sulfur recovery process. The results show that from conceptual constructs for adaptive controls, artificial intelligence became an effective driver of process intelligence in sulfur recovery units. This new era of AI is characterized by deep learning, transfer learning, and hybrid modeling that allow for accurate prediction, improved emission control, and robust optimization under uncertain process conditions. The studies examined show consistent progress from neural estimators to autonomous, interpretable SRU systems that can adapt in real time.