Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

Mei, Congli; Su, Yong; Ding, Yuhan; Liao, Zhiling

doi:10.1016/j.cjche.2016.07.005

Cited by 27 publications

(10 citation statements)

References 21 publications

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“…Xiong et al presents a time delay control strategy and quality prediction for a sulfur recovery unit and a debutanizer column. A fault detection problem using the TEP case is presented in works as Fezai et al An optimization model for a catalytic reaction for the epoxidation of trans -stilbene is addressed in Yan et al A dynamic soft sensor modeling is implemented in Mei et al for a fermentation process in penicillin production. It is important to note that several of the Gaussian regression process based works involve online operations which involve analysis of data obtained from remote sensors …”

Section: Supervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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This work addresses recent developments for solving problems in process systems engineering based on machine learning algorithms. A general description of most popular supervised and unsupervised learning algorithms is presented, as well as the applications addressed in the current literature. Because of their wide usage and potential applications, support vector machines and neural networks are addressed as special cases. The approach used is fundamentally didactic. Therefore, several of the references included are recommendations for novice readers interested in entering the area of machine learning and data science. The applications were selected considering simplicity, popularity of the application, and accessibility for inexperienced readers, but with knowledge of the process systems engineering area. Finally, a critical perspective for future development and applications is provided. Epistemological issues and modeling limitations are discussed in order to analyze the real significance of data-driven strategies as well as a questioning of academic marketing in recent years.

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Section: Supervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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“…The posterior probability is used to group the process data into separate process phases. This concept was later adopted for a real industrial bioprocess ( Mei et al, 2017 ).…”

Section: Challenges In Soft Sensor Development For Bioprocessesmentioning

confidence: 99%

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Brunner

Siegl

Geier

et al. 2021

Front. Bioeng. Biotechnol.

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Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.

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“…By employing the GMR soft sensor model, the number of Gaussian component K should be predetermined. Bayesian inference criterion (BIC) is used to optimize the number of Gaussian component K [36]. The search range for component number K is set as K max = 10 and K min = 1.…”

Section: B Application To a Cobalt Oxalate Synthesis Pilot Plangmentioning

confidence: 99%

Soft Sensor Model Development for Cobalt Oxalate Synthesis Process Based on Adaptive Gaussian Mixture Regression

et al. 2019

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Average particle size is a critical quality variable for optimal operation of the cobalt oxalate synthesis process. However, the measurement for this variable is often achieved from offline laboratory assaying procedure with low sampling rate and low reliability. Therefore, a data-driven soft sensor model based on adaptive Gaussian mixture regression (AGMR) is presented in this paper. Firstly, a GMR based soft sensor model is developed for predicting the average particle size. Secondly, the prediction uncertainty obtained from the GMR model is used to assess the performance of the current soft sensor model. Thirdly, a dual updating algorithm based on the model performance assessment is constructed to track the timevarying behavior of the synthesis process. In the updating method, bias updating and moving window model updating methods are performed in turns based on the results of model performance assessment. The dual updating mechanism can avoid blind updating. Finally, a numerical example and a real industrial cobalt oxalate synthesis process application are used to demonstrate the effectiveness of the proposed method. INDEX TERMS Cobalt oxalate synthesis process, Gaussian mixture regression, soft sensor, model performance assessment, model updating.

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Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

Cited by 27 publications

References 21 publications

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Soft Sensor Model Development for Cobalt Oxalate Synthesis Process Based on Adaptive Gaussian Mixture Regression

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