Bioprocess control
Bioprocess control for cellular agriculture is inherently complex, dynamic, and non-linear, involving numerous interacting parameters that are difficult to manage with traditional methods. There must be a balance between scalability, cost, and environmental sustainability if these systems are to achieve their goals. Accurate modeling and adaptive control strategies are crucial to enhancing efficiency and productivity in this field. The application of artificial intelligence offers a promising pathway to overcome these hurdles.
A prominent theme across recent research is the application artificial intelligence for bioprocess modeling and optimization. Deep learning, particularly convolutional neural networks (CNNs), has been successfully employed to create models that drastically reduce computational time in some applications, enabling more efficient design and optimization (Del Rio‐Chanona et al., 2019). Similarly, AI-accelerated computational fluid dynamics (CFD) simulations demonstrate significant promise in mixing processes, a critical component of overall manufacturing output (Rojek et al., 2021).
Various algorithms, including neural networks (ANNs), support vector machines (SVMs), and random forests, have been evaluated for predicting fermentation outcomes (Roell et al., 2022). This comparative analysis found that random forests and SVMs provided relatively more accurate predictions, especially with limited experimental data. Moreover, combination ANN and genetic algorithm methods have proven effective in developing time-dependent control strategies in fermentation processes (Peng et al., 2013). These studies collectively underscore the potential for a diverse set of AI techniques to make an impact in bioprocess control for cellular agriculture.
Complementing these optimization efforts, real-time monitoring of bioprocess parameters is crucial for effective control and validation. Near-Infrared Spectroscopy (NIRS) showed promise as a rapid technique for on-line monitoring of key parameters during fermentation (Tamburini et al., 2014). These signals can be integrated with AI techniques for real-time monitoring and feedback on the production process.
Further reading
- Reference texts: Encyclopedia of Meat Sciences, 3rd ed. (Dikeman, ed., 2024) catalogued in
OtherResources.md— specifically the Modeling in meat science: Microbiology chapter (Paulsen & Smulders 2024) as the conventional-meat process-modeling reference, and the Physicochemistry & quality chapter cluster (water-holding capacity, protein functionality, color and texture deviations) for the substrate-level meat-quality endpoints cultivated-meat bioprocess work ultimately targets.