Fuzzy control of ethanol concentration its application to maximum glutathione production in yeast fed‐batch culture

Abstract

A fuzzy logic controller (FLC) for the control of ethanol concentration was developed and utilized to realize the maximum production of glutathione (GSH) in yeast fedbatch culture. A conventional fuzzy controller, which uses the control error and its rate of change in the premise part of the linguistic rules, worked well when the initial error of ethanol concentration was small. However, when the initial error was large, controller overreaction resulted in an overshoot.An improved fuzzy controller was obtained to avoid controller overreaction by diagnostic determination of "glucose emergency states" (i.e., glucose accumulation or deficiency), and then appropriate emergency control action was obtained by the use of weight coefficients and modification of linguistic rules to decrease the overreaction of the controller when the fermentation was in the emergency state. The improved fuzzy controller was able to control a constant ethanol concentration under conditions of large initial error.The improved fuzzy control system was used in the GSH production phase of the optimal operation to indirectly control the specific growth rate mu to its critical value micro(c). In the GSH production phase of the fed-batch culture, the optimal solution was to control micro to micro(c) in order to maintain a maximum specific GSH production rate. The value of micro(c) also coincided with the critical specific growth rate at which no ethanol formation occurs. Therefore, the control of micro to micro(c) could be done indirectly by maintaining a constant ethanol concentration, that is, zero net ethanol formation, through proper manipulation of the glucose feed rate. Maximum production of GSH was realized using the developed FLC; maximum production was a consequence of the substrate feeding strategy and cysteine addition, and the FLC was a simple way to realize the strategy.

A fuzzy logic algorithm for identification of the harvesting threshold during PGA production by Bacillus megaterium

“…The objective was to improve α-amylase production in a bioreactor operated in fed-batch mode. Alfafara et al (1993) developed a fuzzy system to control the concentration of the ethanol and identify the point of maximum production of a peptide in fed-batch yeast cultivations. Sousa Jr. and Almeida (2001) proposed a fuzzy set of reasoning rules that permitted the automated start-up of the feeding of inverted sucrose in cephalosporin C batch production.…”

Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of Bacillus megaterium cellular concentrations

“…Many studies highlight the use of FL in fermentative processes, for example, see [8,38,39]. In contrast to the classical sets (non-fuzzy), fuzzy sets describe vague concepts by admitting partial memberships.…”

Enzymatic synthesis of glutathione using yeast cells in two-stage reaction

“…Some fermentation control schemes, such as dissolved oxygen (DO)-stat [7] and pH-stat [8], have been applied for enhancing GSH production by increasing cell density. Fuzzy control has also been used in fed-batch culture of yeast to reduce ethanol concentration and to enhance GSH production [9]. Supplementation of the precursor amino acids or related amino acid (serine, arginine, or methionine) during fermentation can effectively increase intracellular GSH content [10,11].…”