A survey on multistage/multiphase statistical modeling methods for batch processes

Yao, Yuan; Gao, Furong

doi:10.1016/j.arcontrol.2009.08.001

Cited by 210 publications

(166 citation statements)

References 62 publications

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“…The benefits of building multiple phase models instead of a single batch model are shown by many examples. An overview is provided by Yao and Gao [25].…”

Section: Model Identificationmentioning

confidence: 99%

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Kerkhof

Gins

Vanlaer

et al. 2012

Computers & Chemical Engineering

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“…The benefits of building multiple phase models instead of a single batch model are shown by many examples. An overview is provided by Yao and Gao [25].…”

Section: Model Identificationmentioning

confidence: 99%

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Kerkhof

Gins

Vanlaer

et al. 2012

Computers & Chemical Engineering

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“…As a result, it is of critical importance to on-line identification of phases in fermentation process, phase partition is a crucial procedure before multi-phase modeling. The effectiveness of a multi-phase model is problematical without a proper phase division (Doan et al, 2007;Sun et al, 2011;Yao and Gao, 2009;Luo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Another method for detecting phase change uses singular points detection based only on online measurements (Maiti et al, 2009;Régis et al, 2008). The reader can find more multi-phase analysis methods in (Yao and Gao, 2009;Camacho et al, 2008;Doan et al, 2007;Luo et al, 2016), which give different kinds of phase identification methods. Knowledge based phase identification fails when the process prior knowledge is not enough to divide processes into phases legitimately and difficult to customise for diverse fermentation processes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

Zheng¹,

Pan²

2017

American Journal of Biochemistry and Biotechnology

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Glutamate fermentation is inherently nonlinear, multi-phase and an aerobic fermentation process. As long measurement delays and expensive apparatus cost, on-line measurement of the product concentration is not necessarily available. The present fermentation process monitoring and quality prediction involve manual interpretation of highly informative, however, the concentrations of substrates, biomass and products are only low frequency off-line measurements. In this paper, we propose a novel Multi-Phase Support Vector Regression (MPSVR) based soft sensor model for online quality prediction of glutamate concentration. The glutamate fermentation process can be divided into a sequence of five phases by detecting the trend variation events (also termed as singular points or inflection point) of online measured O 2 in the exhaust gas, the Inflection Point (IP) are easily identified through combining Moving Window (MW) with Pearson Correlation Coefficient (PCC). For each estimation phase, SVR soft sensor model are constructed and their performance is evaluated against fermentation data in a 5 L fermenter. The efficiency of the proposed soft sensor model for online product quality prediction has been demonstrated to be superior compared to that of reported techniques in a 5 L glutamate fermentation process.

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“…Although the multiphase monitoring strategy based on multiple phase models [7,8] is applicable, a single-model-based non-Gaussian monitoring method without phase division is still of interest, since it can relieve the computation complexity.…”

Section: Introductionmentioning

confidence: 99%