Hannes Stagge , Theresa Kunz , Sina Ramsayer, Robert Güttel
{"title":"Efficient chemical equilibria calculation by artificial neural networks for ammonia cracking and synthesis","authors":"Hannes Stagge , Theresa Kunz , Sina Ramsayer, Robert Güttel","doi":"10.1016/j.dche.2024.100176","DOIUrl":null,"url":null,"abstract":"<div><p>The calculation of chemical equilibria in detailed reactor simulations frequently requires elaborate numerical solution of the governing equations in an iterative way, which is often computationally expensive and can significantly increase the overall computation time. In order to reduce these computational costs, we introduce a ready-to-use tool, <sup>AN</sup>NH<sub>3</sub>, for calculation of equilibrium composition for synthesis and cracking of ammonia based on a neural network. This tool provides excellent agreement with the conventional approach in the range of 135–1000 °C and 1–100 bar and is ca. 100 times faster than conventional stoichiometry-based concepts by replacing the iterative solution process with neural network inference. While speed-up is significant even for the relatively simple case of ammonia synthesis and decomposition, we expect an even higher performance gain for the equilibrium calculation in reaction systems where more components and multiple reactions are involved.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100176"},"PeriodicalIF":3.0000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000383/pdfft?md5=0496e96ed6bb816a7f908f08d67c84db&pid=1-s2.0-S2772508124000383-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Chemical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772508124000383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The calculation of chemical equilibria in detailed reactor simulations frequently requires elaborate numerical solution of the governing equations in an iterative way, which is often computationally expensive and can significantly increase the overall computation time. In order to reduce these computational costs, we introduce a ready-to-use tool, ANNH3, for calculation of equilibrium composition for synthesis and cracking of ammonia based on a neural network. This tool provides excellent agreement with the conventional approach in the range of 135–1000 °C and 1–100 bar and is ca. 100 times faster than conventional stoichiometry-based concepts by replacing the iterative solution process with neural network inference. While speed-up is significant even for the relatively simple case of ammonia synthesis and decomposition, we expect an even higher performance gain for the equilibrium calculation in reaction systems where more components and multiple reactions are involved.
在详细的反应器模拟中计算化学平衡时,经常需要以迭代方式对控制方程进行精细的数值求解,这通常计算成本很高,而且会大大增加整体计算时间。为了降低这些计算成本,我们推出了一种基于神经网络的即用型工具 ANNH3,用于计算氨合成和裂解的平衡组成。在 135-1000 °C 和 1-100 bar 范围内,该工具与传统方法具有极佳的一致性,并且通过使用神经网络推理取代迭代求解过程,比传统的基于化学计量学的概念快约 100 倍。即使在相对简单的氨合成和分解情况下,速度提升也非常明显,我们预计在涉及更多成分和多种反应的反应系统中,平衡计算的性能提升会更大。