Hydrogen sulfide (${\mathrm{H}}_2$S) and carbon dioxide (${\mathrm{CO}}_2$) 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 ${\mathrm{H}}_2$S and ${\mathrm{CO}}_2$ 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 ${\mathrm{H}}_2$S and ${\mathrm{CO}}_2$, 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.

The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H₂S) and carbon dioxide (CO₂), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in AGRU is monodiethanolamine (MDEA), often mixed with piperazine (PZ) as an additive to accelerate acid gas capture. The absorption performance, however, is significantly influenced by the solvent mixture composition. Despite this, solvent composition is often determined through trial and error in experiments or simulations, with limited studies focusing on predictive methods for optimizing solvent mixtures. Therefore, this paper aims to develop a predictive technique for determining optimal solvent compositions under varying sour gas conditions. An ensemble algorithm, Extreme Gradient Boosting (XGBoost), is selected to develop two predictive models. The first model predicts H₂S and CO₂ concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R² values above 0.99 in most scenarios, and the lowest RMSE and MAE values of less than 1, indicating robust and consistent predictions. The predicted acid gas concentrations and solvent compositions were further analyzed to study the effects of solvent composition on acid gas absorption across different scenarios. The proposed models offer valuable insights for optimizing solvent compositions to enhance AGRU performance in industrial applications.

Natural gas is widely used in various applications including power generation and chemical industry. However, the natural gas from upstream contains several hazardous acid gases such as Hydrogen Sulfide (H2S) and Carbon Dioxide (CO2) which pose risks to both human health and the environment. Therefore, acid gas removal process is pivotal for purifying the natural gas before being delivered to the end-users. This paper aims to investigate the colleration between different solvent components used in absorption-based acid gas removal units. The analysis employs three correlation approaches: Pearson, Spearman, and Kendall. The data was simulated using a Hysys flowsheet by varying the type of solvent blends and compositions. The results indicate that the solvent blends MDEA+PZ and MDEA+DEA exhibit the highest correlation with CO2 removal, achieving a correlation value of -0.91. For H2S removal, the MDEA+SFL blend shows the highest correlation, with a value of -0.74. These findings suggest that specific solvent blending can significantly enhance the reduction of H2S and CO2 concentrations in sweet gas, underlining the importance of solvent selection in the acid gas removal process.

The utilization of cascade refrigeration systems in the frozen food industry has proven advantageous, as they offer improved reliability compared to two-stage refrigeration systems. This study focuses on inspecting the application of nanoparticles in cascade refrigeration system. This research is done to assess the impact of nanoparticles on refrigeration capacity, compressor power, and coefficient of performance (COP). Al 2 O 3 nanoparticles with mass concentration of 0.1% and 0.2%. The system employed by R-134a and R-410a in the high and low circuits respectively. The system analysis was conducted with varying concentrations of nanoparticles Al 2 O 3 in the R134a circuit. The presence of nanoparticles in the system led to increased refrigeration capacity, significant decrease on compressor powers and the system COP improved by 40.7%.