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

Abstract

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.

Fermentation Process Control and Optimization

“…Butkus et al [57] implemented a fuzzy model adapted to the PI controller and studied the specific growth rate of recombi-nant protein by E. coli. The PI controller input is the error between set point and measured specific growth rate from the measurable OUR and the weight of culture broth.…”

Fed-batch growth of Rhizopus oryzae: Eliminating ethanol formation by controlling glucose addition

“…A root mean square specific growth rate control error of 23 ± 6% is reported by Brignoli et al using an optimised novel proportional-integral (PI) feedforward-feedback controller with a first order Savitzky-Golay noise filter algorithm [46]. Butkus et al also proposes a fuzzy logic-based strategy for specific growth rate adaptive control of E. coli [47].…”

Simple Gain-Scheduled Control System for Dissolved Oxygen Control in Bioreactors

“…Therefore, conventional control systems with fixed-gain controllers are not able to provide the required performance [5]. Temperature, pH, dissolved oxygen concentration, and other basic process variables are usually controlled in these systems [6].…”

“…However, development of a model-based control algorithm is a time-consuming task, and, 2 of 12 in addition, online measurements of the process variables require advanced instrumentation of the controlled process. Expert, knowledge-driven adaptive fuzzy systems are effective; however, they require deep process knowledge [3,6,9]. An approach of development for the control systems of dissolved oxygen concentration (DOC) and pH based on artificial neural network (ANN) models is presented in [10,11].…”