Faqih Mochammad, Omar Madiah Binti, Wishnuwardana Rafi Jusar, Ismail Nurul Izni Binti, Mohd Zaid Muhammad Hasif Bin, Bingi Kishore
Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.
Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.
Molecules. 2024 Sep 27;29(19):4591. doi: 10.3390/molecules29194591.
The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (HS) and carbon dioxide (CO), 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 HS and CO concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R 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.
气体脱硫过程对于在将天然气输送给终端用户之前去除有害酸性气体至关重要，这些有害酸性气体包括硫化氢（HS）和二氧化碳（CO）。因此，基于化学吸收的酸性气体去除装置（AGRUs）因其高效率和可靠性而被广泛应用。AGRUs中最常用的溶剂是单乙醇胺（MDEA），通常与哌嗪（PZ）混合作为添加剂以加速酸性气体的捕获。然而，吸收性能会受到溶剂混合物组成的显著影响。尽管如此，溶剂组成通常是通过实验或模拟中的反复试验来确定的，专注于优化溶剂混合物预测方法的研究有限。因此，本文旨在开发一种预测技术，以确定在不同酸性气体条件下的最佳溶剂组成。选择一种集成算法，极端梯度提升（XGBoost），来开发两个预测模型。第一个模型预测HS和CO的浓度，而第二个模型预测MDEA和PZ的组成。结果表明，XGBoost在两个模型中均优于其他算法。在大多数情况下，它的R值高于0.99，最低均方根误差（RMSE）和平均绝对误差（MAE）值小于1，表明预测稳健且一致。进一步分析预测的酸性气体浓度和溶剂组成，以研究溶剂组成在不同情况下对酸性气体吸收的影响。所提出的模型为优化溶剂组成以提高AGRUs在工业应用中的性能提供了有价值的见解。
