FUZZY MODELING AND CONTROL OF BATCH BEER FERMENTATION<sup>+</sup>

Venkateswarlu, Chimmiri; Gangiah, K.

doi:10.1080/00986449508936383

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…Since the introduction of fuzzy logic control by Zadeh [40], this technique has evolved into an established practice for the control of biotechnological processes [41][42][43][44]. The settings are commonly based on the values obtained by experience.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

“…Owing to the inherent complexity (time-varying behavior and nonlinearity) of biological processes they set up high challenges to the control system. Since the introduction of fuzzy logic control by Zadeh [40], this technique has evolved into an established practice for the control of biotechnological processes [41][42][43][44]. Unlike the crisp set theory, the fuzzy set theory allows the transition from the classic bivalent concept of truth to the gradual and multivalent truth concept.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

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Bioprocess monitoring and control via adaptive sensor calibration

Krause

Birle

Hussein

et al. 2011

Engineering in Life Sciences

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To ensure optimal product quality of bioprocesses, it is necessary to develop intelligent control systems with integrated monitoring of key parameters. Having optimal yeast propagation in brewing technology is important to increase the efficiency of subsequent processes. Major drawbacks are: lacks in online detection of yeast attributes and temporal control schemes. One solution is to accurately detect essential process parameters combined with expert knowledge of linguistic control mechanisms. Those needs can be fulfilled by fuzzy logic or state observers including process dynamics associated with accurate multivariate calibration of sensing devices. Ultrasonic‐based devices could monitor key parameter but their inline implementation is limited due to influences of the temperature and gas bubbles. Thus, incipient stages for calibration of the device including temperature dependencies using time and frequency properties of ultrasonic waves are presented. A multivariate model using offline measurements with a maximum prediction error of 0.48 g/100 g is reported in this study. Additionally, we show preliminary results of a mechanistic model for the temperature dependency of yeast growth adapted from the literature (biomass and ethanol production, substrate consumption). The results will lead to flexible control of temperature and aeration resulting in vital yeast and enhanced transparency of propagation progress according to the demands.

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Section: Fuzzy Logic Controlmentioning

confidence: 99%

Section: Fuzzy Logic Controlmentioning

confidence: 99%

Bioprocess monitoring and control via adaptive sensor calibration

Krause

Birle

Hussein

et al. 2011

Engineering in Life Sciences

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“…The design of an FC is composed of fuzzification, rule base, inference procedure, and defuzzification components, the details of which are described elsewhere (Ying et al, 1990;Venkateswarlu and Gangiah, 1995). The overall configuration of a dynamic fuzzy adaptive controller (DFAC) consists of a low-level basic control phase and a high-level dynamic learning phase.…”

Section: Dynamic Fuzzy Adaptive Controllermentioning

confidence: 99%

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Venkateswarlu

Anuradha

2004

Chemical Engineering Communications

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Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.

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“…También encuentran que el controlador con pocas reglas obtiene resultados similares al que cuenta con muchas reglas, pero requiere mayores ajustes y precisión en las variables difusas, aun así, los resultados son muy cercanos. (Venkateswarlu & Gangiah, 1995).…”

Section: Trabajos Basados En Control Difuso Acerca De La Fermentación...unclassified

“…dTE constituye la variación del error de temperatura en un intervalo de tiempo de 300 s, que se ha considerado como un tiempo adecuado basado en el conocimiento experto del sistema. Esta variación está dada por la diferencia entre la temperatura instantánea en el instante anterior y el actual, como se observa en la ecuación (23) y su desarrollo matemático en la ecuación (24) (Venkateswarlu & Gangiah, 1995).…”

Section: Controlador Difusounclassified

Desarrollo de un sistema de predicción y control de solventes durante la fermentación de cerveza con S. Cerevisiae mediante redes neuronales y lógica difusa

Moya Almeida

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Miguel de Cervantes Saavedra iv v AGRADECIMIENTOS «Gracias doy a Dios siempre por vosotros» (1 Co 1:4a RVR95).Largo y complejo es el proceso de investigación y escritura de una tesis doctoral. Sin su soporte hubiese resultado imposible terminarla. Por eso, quiero agradecer, con sincera honestidad, a aquellos que, consciente o inconscientemente, pusieron su grano de arena para levantar este trabajo.A Dios.A Natalia, mi esposa; quien, con su cariño, amor, mares de paciencia y toneladas de apoyo, simplemente, esta tesis nunca podría haber sido escrita.A Vinicio, Catalina, José y Ma. Alejandra, y a mi familia en general; su presencia, risas, palabras, y recuerdos, me acompañaron todos los días, dieron ánimo y fuerza.A las Prof. as Eva Cristina y Belén, mis tutoras académicas. Valoro y aprecio cada reunión, corrección, aporte, y todo el conocimiento brindado, honestamente creo que no pude haber recibido ayuda más valiosa que la suya. Mi respeto y admiración.A mis colegas cerveceros, jueces y catadores; son demasiados para ser nombrados de uno en uno, empero sé, se dan por aludidos. Me brindaron valiosa información técnica, ideas, espacios para difusión, pero sobre todo su amistad y respeto.A Raúl Alberto, Isabela Panterita y Camilo Tiburón, mis gatos; nadie podrá arrebatarles el haberme acompañado durante todos los días y noches que duró este trabajo.

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FUZZY MODELING AND CONTROL OF BATCH BEER FERMENTATION⁺

Cited by 9 publications

References 4 publications

Bioprocess monitoring and control via adaptive sensor calibration

Bioprocess monitoring and control via adaptive sensor calibration

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Desarrollo de un sistema de predicción y control de solventes durante la fermentación de cerveza con S. Cerevisiae mediante redes neuronales y lógica difusa

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FUZZY MODELING AND CONTROL OF BATCH BEER FERMENTATION+

Cited by 9 publications

References 4 publications

Bioprocess monitoring and control via adaptive sensor calibration

Bioprocess monitoring and control via adaptive sensor calibration

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Desarrollo de un sistema de predicción y control de solventes durante la fermentación de cerveza con S. Cerevisiae mediante redes neuronales y lógica difusa

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FUZZY MODELING AND CONTROL OF BATCH BEER FERMENTATION⁺