Application of fuzzy reasoning to control of glucose and ethanol concentrations in baker's yeast culture

Park, Yong Soo; Shi, Zhong Ping; Shiba, Sumihisa; Chantal, Cayuela; Iijima, Shinji; Kobayashi, Takeshi

doi:10.1007/bf00182805

Cited by 30 publications

(8 citation statements)

References 18 publications

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“…Therefore, a constant rate of glucose feed results in oscillating concentrations of glucose, DO, and ethanol in the culture broth (Patnaik 2003a;Shi and Shimizu 1992). Shi and Shimizu (1992), Park et al (1993) and Shiba et al (1994) proposed fuzzy inference rules to control the feed rate of glucose so as to enhance ethanol production beyond what was possible through mechanistic models and conventional optimization.…”

Section: Fuzzy Modeling and Controlmentioning

confidence: 98%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

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Under different environmental conditions, microbial systems display complex behavioral patterns that are difficult to express quantitatively by mechanistic methods. Therefore, two alternate approaches based on different forms of intelligence have emerged. One approach uses methods of artificial intelligence (AI) such as neural networks, expert systems, and genetic algorithms to describe cellular behavior. The second methodology, which leads to the class of cybernetic models, relies on intelligence postulated to be possessed by the cells themselves. While both AI and cybernetic methods have been effective in many applications where mechanistic models are inadequate, all three methods have strengths and weaknesses. This recognition has led to the development of hybrid systems that combine two or more approaches for different aspects of a microbial system. However, the optimum design of hybrid systems still remains heuristic. The rationale and the developments from mechanistic to hybrid models are discussed here, and it is suggested that eventually a truly intelligent system should be self-evolving to maintain itself at the optimum configuration at all times.

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Section: Fuzzy Modeling and Controlmentioning

confidence: 98%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

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“…Application of hybrid control strategies commonly involved optimal and feedback controllers. The optimal controller, either programmed or feedforward control approach, serves as the primary controller, while the feedback controller serves as the compensator [7,8,9].…”

Section: List Of Symbolsmentioning

confidence: 99%

Comparative evaluation of various control schemes for fed-batch fermentation

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Hussain

Ramachandran

2002

Bioprocess and Biosystems Engineering

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The crucial problem associated with control of fed-batch fermentation process is its time-varying characteristics. A successful controller should be able to deal with this feature in addition to the inherent nonlinear characteristics of the process. In this work, various schemes for controlling the glucose feed rate of fed-batch baker's yeast fermentation were evaluated. The controllers evaluated are ®xed-gain proportional-integral (PI), scheduled-gain PI, adaptive neural network and hybrid neural network PI. The difference between the speci®c carbon dioxide evolution rate and oxygen uptake rate (Q c ± Q o ) was used as the controller variable. The evaluation was carried out by observing the performance of the controllers in dealing with setpoint tracking and disturbance rejection. The results con®rm the unsatisfactory performance of the conventional controller where signi®-cant oscillation and offsets exist. Among the controllers considered, the hybrid neural network PI controller shows good performance.

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“…For example, when cultures are in the late growth phase, the specific growth and glucose consumption rates usually decrease. Therefore, a high glucose feed rate resulted in an increase in 25 Because the glucose feed rate is affected by the cell growth and glucose consumption rates, we constructed membership functions for the rates of specific growth (p) and glucose consumption (r-,), as shown in Figure 2. The a value in AF is varied from a to b by the production rule for the determination of a (Table I).…”

Section: Feedback Control Of Glucose Feed Rate Using Fuzzy Control Almentioning

confidence: 99%

“…25 Concentrations of cells and dissolved oxygen in the culture broth were measured on-line by a turbidimeter (SSB-50, DKK Co., Tokyo)" and a dissolved oxygen meter (ABLE co., Tokyo), which were interfaced with an A/D converter [AD 12-4(98), Contec Co., Tokyo] to transmit signals to a personal computer (PC9801, NEC Co., Tokyo). The time intervals for measurement of the cell and DO concentrations were 2.5 and 1.25 min, respectively (i.e., two cell and four DO measurements ever 5 min).…”

Section: Computer-controlled Fermentation Systemmentioning

confidence: 99%

Improvement of cloned α‐amylase gene expression in fed‐batch culture of recombinant Saccharomyces cerevisiae by regulating both glucose and ethanol concentrations using a fuzzy controller

Shiba

Nishida

Park

et al. 1994

Biotech & Bioengineering

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The effect of ethanol concentration on cloned gene expression in recombinant Saccharomyces cerevisiae strain 20B-12 containing one of two plasmids, pNA3 and pNA7, was investigated in batch cultures. Plasmids pNA3 and pNA7 contain the alpha-amylase gene under the control of the SUC2 or PGK promoter, respectively. When the ethanol concentration was controlled at 2 to 5 g/L, the gene expressions were two times higher than those at 20 g/L ethanol. The increase the gene expression by maintaining both the ethanol and glucose concentrations at low levels, a fuzzy controller was developed. The concentrations of glucose and ethanol were controlled simultaneously at 0.15 and 2 g/L, respectively, in the production phase using the fuzzy controller in fed-batch culture. The synthesis of alpha-amylase was induced by the low glucose concentration and maintained at a high level of activity by regulating the ethanol concentration at 2 g/L. The secretory alpha-amylase was induced by the low glucose concentration and maintained at a high level of activity by regulating the ethanol concentration at 2 g/L. The secretory alpha-amylase activities of cells harboring plasmids pNA3 and pNA7 in fed-batch culture were 175 and 395 U/mL, and their maximal specific activities 7.7 and 12.4 U/mg dry cells, respectively. These values are two to three times higher in activity and three to four times higher in specific activity than those obtained when glucose only was controlled.

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Application of fuzzy reasoning to control of glucose and ethanol concentrations in baker's yeast culture

Cited by 30 publications

References 18 publications

Intelligent Models of the Quantitative Behavior of Microbial Systems

Intelligent Models of the Quantitative Behavior of Microbial Systems

Comparative evaluation of various control schemes for fed-batch fermentation

Improvement of cloned α‐amylase gene expression in fed‐batch culture of recombinant Saccharomyces cerevisiae by regulating both glucose and ethanol concentrations using a fuzzy controller

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