期刊
LIFE-BASEL
卷 11, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/life11060557
关键词
process monitoring; control systems; neural networks; fuzzy logic; automation; bioprocess control; open loop; closed loop
资金
- Department of Biotechnology, Ministry of Science and Technology [BT/COE/34/SP15097/2015]
- Biotechnology Industry Research Assistance Council [BT/NBM0159/04/2019]
Bioprocess control involves unique challenges such as non-linearity, variability, and complexity, requiring the use of modern control strategies. In addition to automation, control also includes aspects such as system architecture, software applications, hardware, and interfaces, all of which need to be optimized according to actual needs.
Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.
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