Between the Poles of Data-Driven and Mechanistic Modeling for Process Operation

被引：79

作者：

Solle, Doerte ^{[1
]}

Hitzmann, Bernd ^{[2
]}

Herwig, Christoph ^{[3
]}

Remelhe, Manuel Pereira ^{[4
]}

Ulonska, Sophia ^{[5
]}

Wuerth, Lynn ^{[4
]}

Prata, Adrian ^{[4
]}

Steckenreiter, Thomas ^{[4
]}

机构：

[1] Leibniz Univ Hannover, Inst Tech Chem, Callinstr 5, D-30167 Hannover, Germany

[2] Univ Hohenheim, Inst Food Sci & Biotechnol, Dept Proc Analyt & Cereal Sci, Garbenstr 23, D-70599 Stuttgart, Germany

[3] TU Wien, Inst Chem Environm & Biol Engn, Christian Doppler Lab Mech & Physiol Methods Impr, Gumpendorfer Str 1a, A-1060 Vienna, Austria

[4] Bayer AG, Kaiser Wilhelm Allee, D-51373 Leverkusen, Germany

[5] TU Wien, Inst Chem Environm & Biol Engn, Res Div Biochem Engn, Gumpendorfer Str 1a, A-1060 Vienna, Austria

来源：

CHEMIE INGENIEUR TECHNIK | 2017年 / 89卷 / 05期

关键词：

Case studies; Chemometric models; Hybrid models; Modeling methodology; Validation; FED-BATCH CULTURES; OVERFLOW METABOLISM; PREDICTIVE CONTROL; EXPERT-SYSTEM; CELL-CULTURE; KNOWLEDGE; GROWTH; CALIBRATION; BIOPROCESS; CHEMOMETRICS;

D O I：

10.1002/cite.201600175

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The best method for process control is the use of model-based solutions, based on process analytical technology for online monitoring of critical process variables, product quality attributes, or a holistic process state estimation. Mechanistic models as well as data-driven techniques are essential for real-time process monitoring. Their main characteristics, advantages and disadvantages, and the link between both are discussed as well as the synergetic effects, benefits, and drawbacks resulting from their combination. Aspects and differences of the computational model life cycle management are highlighted.

引用

页码：542 / 561

页数：20

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