Artificial intelligence applications for fault detection and diagnosis in pharmaceutical bioprocesses: a review

Aghaee, Mohammad; Mishra, Abhishek; Krau, Stephane; Tamer, Ibrahim Melih; Budman, Hector

doi:10.1016/j.coche.2024.101025

Current Opinion in Chemical Engineering

2024

DOI: 10.1016/j.coche.2024.101025

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Artificial intelligence applications for fault detection and diagnosis in pharmaceutical bioprocesses: a review

Mohammad Aghaee,

Abhishek Mishra,

Stephane Krau

et al.

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2024

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Cited by 2 publications

References 47 publications

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Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery

Zhang,

Wang,

Zhang

et al. 2024

Advanced Engineering Informatics

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Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery

Zhang,

Wang,

Zhang

et al. 2024

Advanced Engineering Informatics

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Unsupervised Hybrid Models Integrating Deep Autoencoders and Process Controllers’ Models for Enhanced Process Monitoring and Fault Detection

Aghaee,

Krau,

Tamer

et al. 2024

Ind. Eng. Chem. Res.

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This paper introduces a novel hybrid process monitoring model that integrates long short-term memory autoencoders with process controllers’ models. The parameters of the hybrid model are optimized by minimizing a novel loss function, which combines the mean square error (MSE) between controlled variables and their reconstructions from the LSTM-AE model, along with the MSE of manipulated variables and their reconstructions obtained with the numerically implemented and exactly a priori known controller equations. The effectiveness of the proposed method is evaluated on the benchmark of an industrial-scale penicillin process as a batch case study and the Tennessee Eastman plant process under a decentralized control strategy as a continuous case study. A comparative analysis of the proposed hybrid model with an equivalent nonhybrid LSTM-AE model, which does not utilize process controllers’ equations, highlights the superiority of the proposed hybrid monitoring model in fault detection. These improvements result from the use of an LSTM-AE network with fewer parameters, thus making it less susceptible to overfitting.

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Artificial intelligence applications for fault detection and diagnosis in pharmaceutical bioprocesses: a review

Cited by 2 publications

References 47 publications

Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery

Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery

Unsupervised Hybrid Models Integrating Deep Autoencoders and Process Controllers’ Models for Enhanced Process Monitoring and Fault Detection

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