微生物发酵过程多阶段软测量建模的代谢信息神经网络

IF 3.8 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Peng Su, HanQi Cao, Fei Liu
{"title":"微生物发酵过程多阶段软测量建模的代谢信息神经网络","authors":"Peng Su, HanQi Cao, Fei Liu","doi":"10.1021/acs.iecr.5c00516","DOIUrl":null,"url":null,"abstract":"To effectively monitor and control the fermentation process, an accurate real-time measurement of important variables is necessary. However, given the complexity of microbial fermentation and the significant differences in process characteristics at different stages, a single data-driven model for soft sensors cannot fully capture microbial growth patterns. Therefore, we propose a multistage microbial fermentation soft-sensing modeling method that integrates microscopic metabolic information. Initially, this paper adds consideration to the transition phase between the various stages of the fermentation process. Fuzzy C-mean clustering is used to partition the microbial growth stages. Next, unlike conventional physics-informed neural networks that only utilize macroscopic physical information, we constrain the training of the neural network by using dynamic metabolic flux analysis to obtain the rate of change of intracellular and extracellular metabolite concentrations and then incorporate it into the network to model each growth phase. Finally, dynamic time warping is applied to identify the current stage of the variable data during online measurements. The multistage model is then used for real-time measurement, and a case study of penicillin production validates the effectiveness of the proposed method.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":"10 1","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metabolic-Informed Neural Network for Multistage Soft-Sensing Modeling of Microbial Fermentation Processes\",\"authors\":\"Peng Su, HanQi Cao, Fei Liu\",\"doi\":\"10.1021/acs.iecr.5c00516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To effectively monitor and control the fermentation process, an accurate real-time measurement of important variables is necessary. However, given the complexity of microbial fermentation and the significant differences in process characteristics at different stages, a single data-driven model for soft sensors cannot fully capture microbial growth patterns. Therefore, we propose a multistage microbial fermentation soft-sensing modeling method that integrates microscopic metabolic information. Initially, this paper adds consideration to the transition phase between the various stages of the fermentation process. Fuzzy C-mean clustering is used to partition the microbial growth stages. Next, unlike conventional physics-informed neural networks that only utilize macroscopic physical information, we constrain the training of the neural network by using dynamic metabolic flux analysis to obtain the rate of change of intracellular and extracellular metabolite concentrations and then incorporate it into the network to model each growth phase. Finally, dynamic time warping is applied to identify the current stage of the variable data during online measurements. The multistage model is then used for real-time measurement, and a case study of penicillin production validates the effectiveness of the proposed method.\",\"PeriodicalId\":39,\"journal\":{\"name\":\"Industrial & Engineering Chemistry Research\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Industrial & Engineering Chemistry Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.iecr.5c00516\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial & Engineering Chemistry Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1021/acs.iecr.5c00516","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

摘要

为了有效地监测和控制发酵过程,需要对重要变量进行精确的实时测量。然而,考虑到微生物发酵的复杂性和不同阶段过程特征的显著差异,单一的软传感器数据驱动模型无法完全捕捉微生物的生长模式。因此,我们提出了一种集成微观代谢信息的多阶段微生物发酵软测量建模方法。首先,本文考虑了发酵过程各个阶段之间的过渡阶段。采用模糊c均值聚类对微生物生长阶段进行划分。接下来,与仅利用宏观物理信息的传统物理信息神经网络不同,我们通过使用动态代谢通量分析来约束神经网络的训练,以获得细胞内和细胞外代谢物浓度的变化率,然后将其纳入网络以模拟每个生长阶段。最后,在在线测量过程中,应用动态时间规整来识别变量数据的当前阶段。然后将多阶段模型用于实时测量,青霉素生产的案例研究验证了所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Metabolic-Informed Neural Network for Multistage Soft-Sensing Modeling of Microbial Fermentation Processes

Metabolic-Informed Neural Network for Multistage Soft-Sensing Modeling of Microbial Fermentation Processes
To effectively monitor and control the fermentation process, an accurate real-time measurement of important variables is necessary. However, given the complexity of microbial fermentation and the significant differences in process characteristics at different stages, a single data-driven model for soft sensors cannot fully capture microbial growth patterns. Therefore, we propose a multistage microbial fermentation soft-sensing modeling method that integrates microscopic metabolic information. Initially, this paper adds consideration to the transition phase between the various stages of the fermentation process. Fuzzy C-mean clustering is used to partition the microbial growth stages. Next, unlike conventional physics-informed neural networks that only utilize macroscopic physical information, we constrain the training of the neural network by using dynamic metabolic flux analysis to obtain the rate of change of intracellular and extracellular metabolite concentrations and then incorporate it into the network to model each growth phase. Finally, dynamic time warping is applied to identify the current stage of the variable data during online measurements. The multistage model is then used for real-time measurement, and a case study of penicillin production validates the effectiveness of the proposed method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信