Combining model structure identification and hybrid modelling for photo‐production process predictive simulation and optimisation

Zhang, Dongda; Savage, Thomas; Cho, Bovinille Anye

doi:10.1002/bit.27512

Cited by 32 publications

(16 citation statements)

References 41 publications

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“…Hybrid modeling applications in the biopharma sector are currently boosting particularly for Process analytical technology (PAT) [13,[73][74][75] and quality by design (QbD) [7,34,35,45] . Industry 4.0 [13,76] , big data [13,77] , and digital twin [47,78] are recently added subjects that introduce new concepts that challenge the application of hybrid modeling to the digitalization of biopharmaceutical processes. A report from an expert panel discussion of European academics and industrialists has addressed the drivers, challenges, and enablers of hybrid modeling applications in the biopharmaceutical industry [45] .…”

Section: Research Gapmentioning

confidence: 99%

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Agharafeie

Oliveira

Ramos

et al. 2023

Preprint

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Due to the complexity of biological processes, developing model-based strategies for monitoring, optimization and control is nontrivial. Hybrid neural models, combining mechanistic modeling with artificial neural networks, have been reported as powerful tools for bioprocess applications. In this paper, a systematic literature review is presented focused on the application of hybrid neural models to bioprocesses by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) over the last 30 years. This analysis showed that hybrid neural modeling has covered a wide range of microbial processes, animal cells, mixed microbial cultures, and enzyme biocatalysis. Hybrid neural models have been mainly applied for predictive modeling/process analysis, process monitoring/software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, process analytical technology, quality-by-design, and more recently, digital twins. Hybrid modeling experienced a decline in the number of publications after a peak in 2004 and is now surging again. A “model scale” research gap was identified, which will likely narrow by a better integration with deep learning and systems biology in the near future. The biopharma sector is currently a major driver but applications to biologics quality attributes (e.g. glycosylation), new modalities and downstream unit operations are significant research gaps.

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Section: Research Gapmentioning

confidence: 99%

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Agharafeie

Oliveira

Ramos

et al. 2023

Preprint

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“…Bioprocesses often exhibit high variability due to their complex underlying process mechanisms. With the involvement of multiple phases (gas, liquid, and solid), the underlying process can behave in an unpredictable way over a broad range of time and length scales (Zhang et al, 2020). As a result, developing an accurate process model with high reliability (i.e., low uncertainty) is particularly challenging.…”

Section: Introductionmentioning

confidence: 99%

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Vega-Ramon

Xian-feng

Savage

et al. 2021

Biotech & Bioengineering

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Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

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“…The black‐box and white‐box modules can be arranged in three different ways, that is, parallel to each other or one of the two serial structures depending on their order (von Stosch et al, 2014), as shown in Figure 3. In parallel structure, the black box model can map the residuals between experimental and white box model predictions so that mechanistic model outputs can be corrected for future prediction (Bhutani et al, 2006) or to account for the unknown dynamics in numerical integration (Zhang et al, 2020). Very similar framework with some differences in objective, ML algorithm has been adopted in different studies (see Table 1).…”

Section: Hybrid Model Structural Frameworkmentioning

confidence: 99%

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

Mahanty

2023

Biotech & Bioengineering

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Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework, is increasingly being adopted in bioprocess modeling, model‐based experimental design (digital‐twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data‐driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an “optimal” model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data‐driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid‐model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

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Combining model structure identification and hybrid modelling for photo‐production process predictive simulation and optimisation

Cited by 32 publications

References 41 publications

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

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