Industry 4.0 in Action: Digitalisation of a Continuous Process Manufacturing for Formulated Products

Ntamo, D.; Lopez-Montero, Eduardo; Mack, John; Omar, Chalak S.; Highett, M.I.; Moss, D.; Mitchell, Niall A.; Soulatintork, P.; Moghadam, Peyman Z.; Zandi, Mohammad

doi:10.1016/j.dche.2022.100025

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Here, the potential of DDM to enable real‐time decisions is being explored by developing soft sensors for acquiring process data on stream compositions, thus ruling out offline measurements. With upcoming industrial IoT technologies, soft sensor training has become more feasible with increase in the variety of data availability (Eifert et al, 2020; Mitra & Murthy, 2022; Ntamo et al, 2022). With this, the capability of soft sensor to handle large, unclean datasets can increase, thereby significantly reducing the cost of sensors.…”

Section: Bioreactor Controlmentioning

confidence: 99%

“…While developing ML models or DDM, model generalization (Kim, 2017) and overfitting (Srivastava et al, 2014) are two main sources of errors that need to be adequately addressed. As the sensor training has become more feasible with increase in the variety of data availability (Eifert et al, 2020;Mitra & Murthy, 2022;Ntamo et al, 2022) (Abid et al, 2018).…”

Section: Advancements In Data Analyticsmentioning

confidence: 99%

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Advances in bioreactor control for production of biotherapeutic products

Nikita

Mishra

Gupta

et al. 2023

Biotech & Bioengineering

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Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics.Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

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Section: Bioreactor Controlmentioning

confidence: 99%

Section: Advancements In Data Analyticsmentioning

confidence: 99%

Advances in bioreactor control for production of biotherapeutic products

Nikita

Mishra

Gupta

et al. 2023

Biotech & Bioengineering

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“…The DF techniques combined with AI or machine learning can support the decision-making based on key performance indicators in industrial chemical plants [ 179 ]. Platforms are available, in which in silico development and optimization are performed by data-driven models and digital twins for pharmaceutical systems [ 180 ].…”

Section: Integrating Df Into Patmentioning

confidence: 99%

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

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The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

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“…While the advantages of continuous manufacturing and automation have been recognized in the pharmaceutical industry, implementation and routine use of such processes have reached different levels of maturity in different parts of the manufacturing workflow from the raw materials to the finished drug product. Continuous manufacturing appears to be more advanced in drug product manufacturing than in drug substance manufacturing. − While continuous technologies have been subject to research and development in drug substance manufacturing for over two decades, they are far from routine when it comes to their industrial application. Continuous reaction technologies are invaluable for syntheses that are difficult or impossible to handle in batch reactors. − In crystallization technology, which is the dominant unit operation for isolation and purification of small-molecule drug substances and intermediates, continuous technologies have made limited impact.…”

Section: Introductionmentioning

confidence: 99%

Additive Manufacturing as a Rapid Prototyping and Fabrication Tool for Laboratory Crystallizers─A Proof-of-Concept Study

Nys,

König,

Neugebauer

et al. 2023

Org. Process Res. Dev.

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While large-scale crystallizer design profits from many years of accumulated knowledge, traditional fabrication technologies limit the possibilities for easy and rapid lab-scale design, fabrication, and subsequently testing of crystallizer design variants. Additive manufacturing (three-dimensional (3D) printing) affords an opportunity to overcome the challenges associated with scaling down equipment using traditional fabrication technologies and materials of construction such as glass or metal alloys. Moreover, additive manufacturing provides flexibility in design and the ability to rapidly redesign and prototype novel designs, limited, perhaps, only by the suitability of available materials of construction. Surprisingly, this technology has not yet found widespread use in crystallizer design. In this contribution, we present a concept study for a 3D-printed prototype crystallizer. We discuss additive manufacturing as a tool for rapid design and fabrication of down-scaled crystallizers based upon a design using the classic Oslo-type crystallizer as a starting point. The initial crystallizer design and fabrication process, subsequent design modifications, and investigation of the crystallizer characteristics are discussed here with a view to applications in pharmaceutical continuous crystallization.

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Industry 4.0 in Action: Digitalisation of a Continuous Process Manufacturing for Formulated Products

Cited by 11 publications

References 18 publications

Advances in bioreactor control for production of biotherapeutic products

Advances in bioreactor control for production of biotherapeutic products

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Additive Manufacturing as a Rapid Prototyping and Fabrication Tool for Laboratory Crystallizers─A Proof-of-Concept Study

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