Absorption-Based Optimization Technologies for Acid Gas Removal Units: A Review of Recent Trends and Challenges

Hydrogen sulfide (H2S) and carbon dioxide (CO2) can cause various damages that degrade the quality of natural gas before it reaches end users and contribute to environmental pollution. Therefore, it is crucial to eliminate these contaminants to ensure effective usage and prolong equipment lifetime. An acid gas removal unit (AGRU) is a well-known type of equipment for removing H2S and CO2 from natural gas. Forty-six recent research papers have focused on the landscape of the AGRU process and its optimization strategies through experimental and simulation methods, which rely heavily on mathematical approaches. These techniques are often costly and time-consuming. Conversely, discussions on data-driven approaches as optimization techniques for AGRUs are limited. Therefore, this review highlights the potential advancements of data-driven strategies toward AGRU performance. Numerous predictive models of AGRU-related parameters, including H2S and CO2, operating parameters, and material discovery, are discussed in detail. Furthermore, predictive models of fault detection and its prevention are also examined. The literature confirms that data-driven approaches exhibit effective capabilities to enhance the operational performance of AGRUs. This could help industry operators and stakeholders maintain reliable operation and optimize the AGRU’s performance.

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