Efficient chemical equilibria calculation by artificial neural networks for ammonia cracking and synthesis

IF 3 Q2 ENGINEERING, CHEMICAL Digital Chemical Engineering Pub Date : 2024-08-08 DOI:10.1016/j.dche.2024.100176
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 ,&nbsp;Theresa Kunz ,&nbsp;Sina Ramsayer,&nbsp;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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用人工神经网络高效计算氨裂解和合成过程中的化学平衡
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.10
自引率
0.00%
发文量
0
期刊最新文献
The trust region filter strategy: Survey of a rigorous approach for optimization with surrogate models Multi-agent distributed control of integrated process networks using an adaptive community detection approach Industrial data-driven machine learning soft sensing for optimal operation of etching tools Process integration technique for targeting carbon credit price subsidy Robust simulation and technical evaluation of large-scale gas oil hydrocracking process via extended water-energy-product (E-WEP) analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1