Hybrid Modelling and Multi- Parametric Control of Bioprocesses

doi:10.3390/books978-3-03842-746-9

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…13,114,115 These models capture the missing nonlinear relationships and interaction variables, all of which improve the models' predictive ability. 116 In addition, a hybrid model has been used to capture resin aging in chromatography and to improve the resin lifetime. 107 Integrating with on-line monitoring tools, a hybrid model can be applied for real-time optimization of downstream processes.…”

Section: Improvement Of Single Unit Operationmentioning

confidence: 99%

“…107 Integrating with on-line monitoring tools, a hybrid model can be applied for real-time optimization of downstream processes. 116 Other modeling methods, such as surrogate models, can also be used for dynamic process modeling, process scheduling, and sensitivity or feasibility analysis at a lower computational cost. 117 However, there are still limited reports that have incorporated hybrid modeling during continuous biopharmaceutical production for process development.…”

Section: Improvement Of Single Unit Operationmentioning

confidence: 99%

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Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies

Chopda

Gyorgypal

et al. 2021

J of Chemical Tech & Biotech

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Continuous bioprocessing is significantly changing the biological drugs (or biologics) manufacturing landscape by potentially improving product quality, process stability, and overall profitability, as was similarly seen during the adoption of advanced manufacturing processes for small molecule drugs in the past decade. However, the implementation of continuous manufacturing for biological processes producing protein-based drug molecules, such as monoclonal antibodies (mAbs), is facing several new hurdles. The barriers to continuous bioprocessing can be overcome through improved process understanding via better predictive capabilities enabled by hybrid modeling that can also lead to robust process control. This review article summarizes the recent advances and ongoing obstacles faced during the use of advanced process analytical technologies (PAT), process modeling, and control strategies to enable continuous manufacturing of mAbs. In addition, this review also discusses the process strategies and future directions of advanced continuous manufacturing approaches that have been adapted by other industries and that could be implemented for mAbs production soon.

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Section: Improvement Of Single Unit Operationmentioning

confidence: 99%

Section: Improvement Of Single Unit Operationmentioning

confidence: 99%

Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies

Chopda

Gyorgypal

et al. 2021

J of Chemical Tech & Biotech

View full text Add to dashboard Cite

show abstract

“…This has successfully been applied for chemical processes [18] and is an established state-of-the-art method in chemical industry [19]. However, fewer examples of its application in the biotechnological sector have been published so far [1,[20][21][22][23][24][25]. One of the very first applications in which the production of an antibiotic by a mycelial organism was optimized was reported in 1993 [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa

2020

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In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO 2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.

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Hybrid Modelling and Multi- Parametric Control of Bioprocesses

Cited by 2 publications

References 19 publications

Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies

Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies

Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa

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