Fermentation Processes: Modeling, Optimization and Control: 2nd Edition

Fermentation is an important cornerstone of bioengineering, which plays a critical role in the production of a wide array of products including pharmaceuticals, biofuels, food additives, industrial chemicals and enzymes. As a biological process that involves the metabolic activity of microorganisms, fermentation is inherently complex, nonlinear, and dynamic. This complexity poses significant challenges to researchers and engineers who aim to optimize product yield and quality, and enhance process efficiency.

To address these challenges, significant attention has been devoted to the development of robust strategies for modeling, monitoring, and controlling fermentation processes. Accurate modeling provides a foundation for understanding the underlying biological and physicochemical phenomena that enable simulation, prediction, and process design in the fermentative production of target products. Meanwhile, real-time monitoring is essential for tracking key process variables such as biomass concentration, substrate consumption, and product formation, thereby offering insight into the state of the system. Lastly, advanced control techniques ensure that the process operates within optimal conditions, despite disturbances and uncertainties, to maximize productivity and ensure regulatory compliance.

The integration of these elements is vital for the transition from empirical, trial-and-error methods to data-driven and model-based approaches in modern bioprocessing. The synergy between these technologies, such as modern measurement devices, new algorithms for optimization and process control, and computational hardware, has profound implications for sustainability issues. Traditional fermentation processes can be resource-intensive, often requiring significant inputs of water, energy, and raw materials. Through AI-enhanced optimization, waste can be minimized, energy efficiency can be achieved, and the overall environmental impact can be significantly reduced. These initiatives not only improve productivity but also help forge pathways to a more sustainable industrial future.

Under this framework, this Special Issue features several contributions that are focused on novel tools applied to fermentative processes, such as machine learning for improved protein production, heuristic and theoretical optimization procedures based on control strategies, improved culture media or the use of nonconventional microorganisms and bioreactor designs and configurations to maximize specific bioproducts like organic acids, as well as online strategies for the estimation of key operational variables in fermentation (Contributions 1–10).