Towards the Development of Digital Twins for the Bio-manufacturing Industry

Gargalo, Carina L.; Heras, Simoneta Caño de las; Jones, Mark Nicholas; Udugama, Isuru A.; Mansouri, Seyed Soheil; Krühne, Ulrich; Gernaey, Krist V.

doi:10.1007/10_2020_142

Cited by 35 publications

(31 citation statements)

References 119 publications

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“…Many process industries are currently facing changing competitive landscapes and increased applications of digital information. 11 Therefore, industries must decide how to develop new technologies to benefit the most and positively disrupt the industrial chains of value creation and business models. This leads progressively to the introduction and adoption of digital methods to analyze, predict, and optimize the behavior of production systems at each product life cycle stage in real time.…”

Section: Discussionmentioning

confidence: 99%

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Modeling Drainage in Periodic Separation

Abildskov

Jørgensen

2022

Ind. Eng. Chem. Res.

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Simple models are developed for realistic operation modes of periodic separation. The models comprise instantaneous, simultaneous, and sequential draining. Using our models, the influence of a finite drainage time on separation performance is investigated. Linear case systems are modeled for stationary profiles for both simultaneous and sequential liquid drainage. Performance measures, such as tray efficiencies and mass transfer, are compared for different cases using a consistent modeling framework. We find that draining periods − in general − should be minimized, irrespective of the operating mode. For columns with a few stages, the simultaneous draining is most efficient. With a few stages, the differences are negligible. For higher numbers of stages, the sequential draining becomes superior, partially due to the enabling of mass transfer during draining. Two detailed study cases are provided.

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Section: Discussionmentioning

confidence: 99%

“…To make comparisons with classical trays, it is appropriate to know the impact of realistic draining methods. Also, the tools in this paper can be useful in the development of digital shadows and twins 11 and other digital transition concepts.…”

Section: Introductionmentioning

confidence: 99%

Modeling Drainage in Periodic Separation

Abildskov

Jørgensen

2022

Ind. Eng. Chem. Res.

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“…Model predictive control (MPC) has been successfully implemented in the chemical and petrochemical industries; thus, it is only expected that it has significant potential in improving bioprocess productivity. Simply speaking, MPC implementation applies dynamic models to adapt the control strategy in real-time [52]. MPC is mainly used for constrained optimization of unit operations [15].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

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The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

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“…These simulated trajectories can be used to model what is likely to happen to a patient in the future, if no outside intervention changes their clinical course. Digital twins have their roots in the domain of engineering and have been applied by NASA in the development of aerospace vehicles [5] as well as in biomanufacturing [4] and in civil engineering [6]. Although many applications of digital twins can be found in the context of industry, health care represents a place where digital twins can have a disruptive impact [7].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

et al. 2021

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Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUCadversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.

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Towards the Development of Digital Twins for the Bio-manufacturing Industry

Cited by 35 publications

References 119 publications

Modeling Drainage in Periodic Separation

Modeling Drainage in Periodic Separation

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

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