Sensor Network Robustness Using Model-Based Data Reconciliation for Continuous Tablet Manufacturing

Moreno, Mariana; Ganesh, Sudarshan; Shah, Yash; Su, Qing; Nagy, Zoltán K.; Reklaitis, Gintaras V.

doi:10.1016/j.xphs.2019.03.011

Cited by 8 publications

(1 citation statement)

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“…Compared to random error minimization, correction and detection of systematic errors is more challenging and typically requires the utilization of mechanistic or hybrid models 130,133,135,136 . Data reconciliation methods for the reduction of systematic errors are already relatively well established in other industries, such as the chemical industry 135,137 . However, applying advanced data reconciliation methods in the biopharmaceutical industry is still a developing field, especially in the case of mammalian cells.…”

Section: Quality Control and Process Monitoringmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

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Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever‐evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end‐to‐end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

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Section: Quality Control and Process Monitoringmentioning

confidence: 99%