Study on soft sensor modeling method for sign of contaminated fermentation broth in Chlortetracycline fermentation process

Wang, Mei-Chun; Han, Xiang; Sun, Yumei; Sun, Qi; Chen, Xiangguang

doi:10.1080/10826068.2020.1793173

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Furthermore, a single global soft sensor model is usually complex in structure and requires a lot of time to solve it. Wang and Han [14] proposed a single soft sensor model based on a recurrent wavelet neural network and Gaussian process regression methods for online prediction of whether Chlortetracycline fermentation broth is contaminated by non-target bacteria. Although the experimental results show that the proposed soft sensor model can be used to predict the occurrence of pollution during the Chlortetracycline fermentation process, and the effectiveness of the modeling method is verified based on field data, the Chlortetracycline fermentation process is a very complex and non-linear process, and most microbial fermentation processes including Chlortetracycline can be divided into four stages: adjustment, logarithmic growth, stabilization and decay phases.…”

Section: Introductionmentioning

confidence: 99%

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Wang

et al. 2021

Sensors

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The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

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Section: Introductionmentioning

confidence: 99%

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Wang

et al. 2021

Sensors

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Establishing a novel procedure to detect deviations from standard milk processing by using online Raman spectroscopy

2022

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An online soft sensor method for biochemical reaction process based on JS-ISSA-XGBoost

Zhang,

Wang,

Shen

et al. 2023

BMC Biotechnol

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Background A method combining offline techniques and the just-in-time learning strategy (JITL) is proposed, because the biochemical reaction process often encounters changing features and parameters over time. Methods Firstly, multiple sub-databases in the fermentation process are constructed offline by an improved fuzzy C-means algorithm and the sample data are adaptively pruned by a similarity query threshold. Secondly, an improved eXtreme Gradient Boosting (XGBoost) method is used on the online modeling stage to build soft sensor models, and the multi-similarity-driven just-in-time learning strategy is used to increase the diversity of the model. Finally, to improve the generalization of the whole algorithm, the output of the base learner is fused by an improved Stacking integration model and then the predictive output is performed. Results Applying the constructed soft sensor model to the problem of predicting cell concentration and product concentration in Pichia pastoris fermentation process. The experimental results show that the root mean square error of the cell concentration is 0.0260, the coefficient of determination is 0.9945, the root mean square error of the product concentration is 2.6688, and the coefficient of determination is 0.9970. It shows that the proposed method has the advantages of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method. Conclusion The JS-ISSA-XGBoost is an extensive and excellent soft measurement model that meets the practical needs for real-time monitoring of parameters and prediction of control in biochemical reactions.

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Study on soft sensor modeling method for sign of contaminated fermentation broth in Chlortetracycline fermentation process

Cited by 4 publications

References 7 publications

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Establishing a novel procedure to detect deviations from standard milk processing by using online Raman spectroscopy

An online soft sensor method for biochemical reaction process based on JS-ISSA-XGBoost

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