Digital Twins for Bioprocess Control Strategy Development and Realisation

Appl, Christian; Moser, André; Baganz, Frank; Hass, Volker C.

doi:10.1007/10_2020_151

Cited by 10 publications

(11 citation statements)

References 46 publications

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“…Process control strategies developed with the new DT for enzymatic hydrolysis processes in the STR can be transferred to the real process quickly and with a high probability of success. This has already been demonstrated for the cultivation process of S. cerevisiae [4].…”

Section: Discussion and Outlookmentioning

confidence: 84%

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Development of a Digital Twin for Enzymatic Hydrolysis Processes

2021

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Enzymatic hydrolysis processes can be used to produce organic nutrient media from renewable raw materials. However, many of these processes are not optimally designed, so expensive enzymes and substrates are wasted. Mathematical models and Digital Twins (DTs) are powerful tools, which can be used to optimize bioprocesses and, thus, increase the yield of the desired products. Individual enzymatic hydrolysis processes have already been modeled, but models for the combined starch hydrolysis and proteolysis, or DTs, are not available yet. Therefore, an easily adaptable, dynamic, and mechanistic mathematical model representing the kinetics of the enzymatic hydrolysis process of the combined starch hydrolysis and proteolysis was developed and parameterized using experimental data. The model can simulate the starch hydrolysis process with an agreement of over 90% and the proteolysis process with an agreement of over 85%. Subsequently, this model was implemented into an existing DT of a 20 L stirred tank reactor (STR). Since the DT cannot only map the kinetics of the enzymatic process, but also the STR with the associated periphery (pumps, heating jacket, etc.), it is ideally suited for future process control strategy development and thus for the optimization of enzymatic hydrolysis processes.

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Section: Discussion and Outlookmentioning

confidence: 84%

“…DTs also may represent the reactor macro-kinetics and the dynamics of the system. They are thus ideally suited for systematic optimization of dynamic processes [4,5].…”

Section: Introductionmentioning

confidence: 99%

Development of a Digital Twin for Enzymatic Hydrolysis Processes

2021

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“…Udugama et al [22] presented a framework built upon a five-stage pathway starting from a basic steady state process model and ending to a fully-fledged digital twin for a second-generation ethanol fermentation process. Digital twins can be used as a control strategy development tool to enable the development of optimized controllers which can increase the efficiency of bioprocess systems while they are in normal operation [25].…”

Section: B Digital Twins In Process Industrymentioning

confidence: 99%

A Methodology for Generating a Digital Twin for Process Industry: A Case Study of a Fiber Processing Pilot Plant

et al. 2022

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Digital twins are now one of the top trends in Industry 4.0, and many companies are using them to increase their level of digitalization, and, as a result, their productivity and reliability. However, the development of digital twins is difficult, expensive, and time consuming. This article proposes a semiautomated methodology to generate digital twins for process plants by extracting process data from engineering documents using text and image processing techniques. The extracted information is used to build an intermediate graph model, which serves as a starting point for generating a model in a simulation software. The translation of a graph-based model into a simulation software environment necessitates the use of simulator-specific mapping rules. This paper describes an approach for generating a digital twin based on a steady state simulation model, using a Piping and Instrumentation Diagram (P&ID) as the main source of information. The steady state modeling paradigm is especially suitable for use cases involving retrofits for an operational process plant, also known as a brownfield plant. A methodology and toolchain is proposed, consisting of manual, semi-automated and fully automated steps. A pilot scale brownfield fiber processing plant was used as a case study to demonstrate our proposed methodology and toolchain, and to identify and address issues that may not occur in laboratory scale case studies. The article concludes with an evaluation of unresolved concerns and future research topics for the automated development of a digital twin for a brownfield process system.

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“…Consequently, a generalized concept and notion for DT are still evolving. Notwithstanding this, the DT is proliferating other domains such as space cooling/heating [ 28 , 29 ], wind turbine fault diagnosis [ 30 , 31 , 32 ], bio-processes [ 33 , 34 ], energy [ 35 ], oil and gas [ 36 ], automotive [ 37 , 38 , 39 ], infrastructure [ 40 , 41 ], and defense [ 42 ] to name a few. More recently, DT concepts are being proposed in healthcare and medicine and are expected to provide significant advantages [ 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Padmapritha

Srinivasan

Kesavadev

et al. 2023

JCM

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Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

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Digital Twins for Bioprocess Control Strategy Development and Realisation

Cited by 10 publications

References 46 publications

Development of a Digital Twin for Enzymatic Hydrolysis Processes

Development of a Digital Twin for Enzymatic Hydrolysis Processes

A Methodology for Generating a Digital Twin for Process Industry: A Case Study of a Fiber Processing Pilot Plant

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

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