A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation

Destro, Francesco; Facco, Pierantonio; García‐Muñoz, Salvador; Bezzo, Fabrizio; Barolo, Massimiliano

doi:10.1016/j.jprocont.2020.06.002

Cited by 34 publications

(8 citation statements)

References 36 publications

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“…Hybrid models are a new type of unstructured models that have been applied to several recent studies for biochemical process modeling (Cabaneros Lopez et al, 2021; Willis & von Stosch, 2017) and monitoring (Destro et al, 2020; Geinitz et al, 2020). These models incorporate a data‐driven model into a conventional unstructured kinetic model to enhance the model's accuracy and predictive ability (Carinhas et al, 2011; von Stosch et al, 2014).…”

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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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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“…This reasoning 22 is the same behind principal component analysis (PCA), and it has been widely used for multivariate process analysis, monitoring, and control. [23][24][25][26]…”

Section: Unsupervised Modelsmentioning

confidence: 99%

Industrial data science – a review of machine learning applications for chemical and process industries

et al. 2022

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“…Therefore, the warped time can potentially disclose information about how a batch is evolving with respect to the others Feature extraction [12,32] uses engineering knowledge to combine the available data in a non-linear fashion, in such a way as to derive new variables that can complement those coming from the plant sensors in order to disclose operation-relevant information that can aid the troubleshooting task. In a way, adding the extracted features to the available dataset represents a way to hybridize the data-driven model with first-principles information, an operation that is known to help fault detection and diagnosis [33,34]. One example that proved useful for the process under investigation was including information about the reaction stoichiometry.…”

Section: Data Pre-processingmentioning

confidence: 99%

Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

et al. 2021

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Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.

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A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation

Cited by 34 publications

References 36 publications

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Industrial data science – a review of machine learning applications for chemical and process industries

Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

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