An algorithm for operating a fed-batch fermentor at optimum specific-growth rate

Abstract Background: Hyaluronic acid (HA) is an important mucopolysaccharide of higher molecular weight range and holds sheer economic interest. Its applications are widely acknowledged in rheumatoid arthritis treatment, tissue engineering, and cosmetics industries. This present investigation aims for the fed-batch production of high molecular weight range HA by application of real-time metabolic heat measurements. Results: Fed-batch strategies based on 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, [[EQUATION]] was looped to the PID circuit, envisaged to control [[EQUATION]] to their desired setpoint values 0.05 [[EQUATION]] , 0.1 [[EQUATION]] and 0.15 [[EQUATION]] respectively. The developed FB strategy established a robust control on maintaining the specific growth rate (µ) close to the [[EQUATION]] value with a minimal tracking error. Exponential feed rate carried out with a lowest [[EQUATION]] of 0.05 [[EQUATION]] improved the MW of HA significantly 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 a FF control strategy at [[EQUATION]] . Biomass and Lactic acid (LA) concentrations were found to be concomitant with the increase in [[EQUATION]] from 0.05 [[EQUATION]] to 0.15 [[EQUATION]] . Superior control of µ at low [[EQUATION]] value was observed to influence positively the HA polymerization attributing to improved MW and desired Polydispersity Index (PDI) of HA. Conclusions: This present investigation attempts to address the metabolic bottleneck in synthesis of high MW HA by S. zooepidemicus and illustrates the application of calorimetric fed-batch control of µ at a narrower range. 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 metabolites albeit maintaining homeostasis.

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Fuzzy Logic-Based Adaptive Control of Specific Growth Rate in Fed-Batch Biotechnological Processes. A Simulation Study

Applied Sciences ◽

10.3390/app10196818 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6818

Author(s):

Mantas Butkus ◽

Jolanta Repšytė ◽

Vytautas Galvanauskas

Keyword(s):

Growth Rate ◽

Fuzzy Logic ◽

Adaptive Control ◽

Specific Growth Rate ◽

Control Algorithm ◽

Specific Growth ◽

Expert Knowledge ◽

Fed Batch ◽

Biotechnological Process ◽

Model Free

This article presents the development and application of a distinct adaptive control algorithm that is based on fuzzy logic and was used to control the specific growth rate (SGR) in a fed-batch biotechnological process. The developed control algorithm was compared with two adaptive control systems that were based on a model-free adaptive technique and gain scheduling technique. A typical mathematical model of recombinant Escherichia coli fed-batch cultivation process was selected to evaluate the performance of the fuzzy-based control algorithm. The investigated control techniques performed similarly when considering the whole process duration. The adaptive PI controller with fuzzy-based parameter adaptation demonstrated advantages over the previously mentioned algorithms—especially when compensating the deviations of the SGR. These deviations usually occur when the equipment malfunctions or process disturbances take place. The fuzzy-based control system was stable within the investigated ranges. It was determined that, regarding control quality, the investigated control algorithms are suited to control the SGR in a fed-batch biotechnological process. However, substrate feeding rate manipulation and limitation needs to be used. Taking into account the time needed to design and tune the controller, the developed controller is suitable for practical applications when expert knowledge is available. The proposed algorithm can be further adapted and developed to control the SGR in other cell cultivations while running the process under substrate limitation conditions.

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