Soft-sensor development for monitoring the lysine fermentation process

Tokuyama, Kento; Shimodaira, Yoshiki; Kodama, Yohei; Matsui, Ryuzo; Kusunose, Yasuhiro; Fukushima, Satoshi; Nakai, Hiroaki; Tsuji, Yuichiro; Toya, Yoshihiro; Mizutani, Fumio; Shimizu, Hiroshi

doi:10.1016/j.jbiosc.2021.04.002

Cited by 11 publications

(12 citation statements)

References 22 publications

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“…In the field of process monitoring, ref. [69] uses AutoML to optimize the parameters of a soft-sensor for monitoring the lysine fermentation process. The authors in [70] propose in the future research that the AutoML approach could be used for parameter optimization of a Variational Auto Encoder for nonlinear process monitoring.…”

Section: Previous Workmentioning

confidence: 99%

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

Cerrada

Trujillo

Hernández

et al. 2022

MCA

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Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.

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Section: Previous Workmentioning

confidence: 99%

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

Cerrada

Trujillo

Hernández

et al. 2022

MCA

View full text Add to dashboard Cite

show abstract

“…To facilitate the transition from recipe-based operation to digital twin-based operation in biomanufacturing, an intermediate approach can be considered in which the model is updated continuously based on the operational status of a physical system it is representing (one-way communication) without a close-loop. This model is referred to as a "digital shadow" (Tokuyama et al, 2021). Developing a digital shadow requires a validated predictive model in which its state variables can be updated real time.…”

Section: Introductionmentioning

confidence: 99%

“…Steps for implementing a digital twin include developing a validated digital model, applying a digital shadow, and then implementing a digital twin (Tokuyama et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Despite this, GBA it is only rarely used (Tokuyama et al, 2021) as a soft-sensor model for fermentation processes (Ji & Sun, 2022;Reyes et al, 2022;Zhu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Developing a digital shadow requires a validated predictive model in which its state variables can be updated real time. Steps for implementing a digital twin include developing a validated digital model, applying a digital shadow, and then implementing a digital twin (Tokuyama et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation

Rydal,

Frandsen,

Nadal‐Rey

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Biotech & Bioengineering

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Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real‐time process monitoring, control, and optimization. With a digital shadow, real‐time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot‐scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot‐scale and lab‐scale fermentations were conducted for model development and validation. With all available pilot‐scale data, a data‐driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N‐RMSE) of 2%. This robust data‐driven soft sensor was able to estimate accurately in lab‐scale (volume < 20× pilot) with a N‐RMSE of 7.8%. A hybrid soft sensor was developed by combining the data‐driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot‐scale data with N‐RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale‐up and leads the way to a new generation of in silico‐based process development.

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A perspective‐driven and technical evaluation of machine learning in bioreactor scale‐up: A case‐study for potential model developments

Karimi Alavijeh,

Lee,

Gras

2024

Engineering in Life Sciences

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Bioreactor scale‐up and scale‐down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail‐safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale‐up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale‐up studies involving CHO cell‐generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small‐ and large‐scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale‐sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large‐scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.

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Soft-sensor development for monitoring the lysine fermentation process

Cited by 11 publications

References 22 publications

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation

A perspective‐driven and technical evaluation of machine learning in bioreactor scale‐up: A case‐study for potential model developments

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