Bioprocess Control: Current Progress and Future Perspectives

Abstract

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.

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

“…Robust control strategies must be built on top of in-depth understandings of the process. Thus, mechanistic models, data-driven models, or hybrid models can be used to develop successful control strategies, which can be split up into open-loop strategies, closed-loop (or feedback) strategies, fuzzy control, and model predictive control [152,153].…”

“…One large drawback of these strategies is that they require precomputed knowledge profiles of growth kinetics, which is difficult in non-linear systems with a dynamically changing metabolism, as in the case of mammalian cells. Additionally, open-loop control strategies are unable to perform corrective measures when the system has deviated from the designed space as a result of disturbances impacting the process [152,153].…”

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

“…Soft sensors have become an important tool within the QbD/PAT framework, as reviewed by Mandenius and Gustavsson (2015) , Randek and Mandenius (2018) , and Rathore et al (2021) . One reason is that they are often the only means of determining critical process parameters (CPP) or critical quality attributes (CQA) online at all ( Capito et al, 2015 ; Melcher et al, 2015 ; Sauer et al, 2019 ; Spann et al, 2019 ; Walch et al, 2019 ; Pais et al, 2020 ; Wasalathanthri et al, 2020a ).…”

“…Making these quantities measurable by means of soft sensors, in turn, allows CPPs or CQAs to be closed-loop controlled ( Birle et al, 2015 ; Matthews et al, 2016 ; Voss et al, 2017 ; Brunner et al, 2020 ; Gomis-Fons et al, 2020 ). This type of control, also called inferential control, plays an important role in the automation of bioprocesses, since by far not all process quantities to be closed-loop controlled can be measured directly ( Rathore et al, 2021 ).…”

PID controls: the forgotten bioprocess parameters

“…In the process controls field, there exists a wide range of papers on PID tuning, mainly for traditional chemical processes [14][15][16][17]. For biologic processes, most PID tuning studies have focused on microbial fermentation processes [18,19], where the interaction of pH and DO control is not as strong due to non-bicarbonate buffered systems [20]. Conversely, for mammalian cell cultures, the traditional bicarbonate buffer uses gaseous CO 2 for pH control, which can affect O 2 solubility and thus DO control, and vice versa [21,22].…”