Metabolic heat based specific growth rate estimators: Does the choice of estimation model influence the state of bioprocesses?

Accurate and reliable estimation of specific growth rate (μ) in real-time is pivotal for reliable process monitoring of a bioprocess and subsequent implementation of advanced control strategies. Gibbs free energy dissipation is imminent for any biological system, and the metabolic heat flow measurements (calorimetry) formed the basis for estimating μ. However, the rationale behind selecting a suitable μ estimator model based on calorimetric perspective remains unexplored. The present investigation addresses the notion behind the selection of an appropriate estimator for μ and the assessment of the estimator models was illustrated using different types of energy metabolism, namely, high exothermic and low exothermic processes. The results indicated that the μ values from the instantaneous heat flow estimator significantly deviated (10-fold higher) from the offline values for highly exothermic process. Notably, the cumulative heat-based estimator accurately estimated μ values on both types of energy metabolism with performance metrics <0.005 h-1.

Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

A review of yeast: High cell-density culture, molecular mechanisms of stress response and tolerance during fermentation

Yeast is widely used in the fermentation industry, and the major challenges in fermentation production system are high capital cost and low reaction rate. High cell-density culture is an effective method to increase the volumetric productivity of the fermentation process, thus making the fermentation process faster and more robust. During fermentation, yeast is subjected to various environmental stresses, including osmotic, ethanol, oxidation, and heat stress. To cope with these stresses, yeast cells need appropriate adaptive responses to acquire stress tolerances to prevent stress-induced cell damage. Since a single stressor can trigger multiple effects, both specific and nonspecific effects, general and specific stress responses are required to achieve comprehensive protection of cells. Since all these stresses disrupt protein structure, the upregulation of heat shock proteins and trehalose genes is induced when yeast cells are exposed to stress. A better understanding of the research status of yeast HCDC and its underlying response mechanism to various stresses during fermentation is essential for designing effective culture control strategies and improving the fermentation efficiency and stress resistance of yeast.

Special Issue on “Bioreactor System: Design, Modeling and Continuous Production Process”

Biochemical engineering deals with the processing of biological or chemical materials using enzymes or living cells as biological catalysts [...]

Real-time metabolic heat-based specific growth rate soft sensor for monitoring and control of high molecular weight hyaluronic acid production by Streptococcus zooepidemicus

This present investigation addressing the metabolic bottleneck in synthesis of high MW HA by Streptococcus zooepidemicus and illustrates the application of calorimetric fed-batch control of µ at a narrower range. Feedforward (FF) and feedback (FB) control was devised to improve the molecular weight (MW) of HA production by S. zooepidemicus. Metabolic heat measurements (Fermentation calorimetry) were modeled to decipher real-time specific growth rate, [Formula: see text] was looped into the PID circuit, envisaged to control [Formula: see text] to their desired setpoint values 0.05 [Formula: see text], 0.1 [Formula: see text], and 0.15 [Formula: see text] respectively. Similarly, a predetermined exponential feed rate irrespective of real-time µ was carried out in FF strategy. The developed FB strategy established a robust control capable of maintaining the specific growth rate (µ) close to the [Formula: see text] value with a minimal tracking error. Exponential feed rate carried out with a lowest [Formula: see text] of 0.05 [Formula: see text] showed an improved MW of HA to 2.98 MDa and 2.94 MDa for the FF and FB-based control strategies respectively. An optimal HA titer of 4.73 g/L was achieved in FF control strategy at [Formula: see text]. Superior control of µ at low [Formula: see text] value was observed to influence HA polymerization positively by yielding an improved MW and desired polydispersity index (PDI) of HA. PID control offers advantage over conventional fed-batch method to synthesize HA at an improved MW. Calorimetric signal-based µ control by PID negates adverse effects due to the secretion of other end products albeit maintaining regular metabolic activities. KEY POINTS: First report to compare HA productivities by feedforward and feedback control strategy. Inherent merits of regulating µ at narrower range were entailed. Relationship between operating µ and HA molecular weight was discussed.