Modeling and Control of Beer Fermentation for Optimal Flavor and Performance

Yerolla, Raju; Harsha, Nekkanti Sri; P, Suhailam; Besta, Chandra Shekar

doi:10.1002/ceat.202200376

Cited by 3 publications

(1 citation statement)

References 91 publications

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“…A binary distillation column was considered for transfer function identification and control [29]. A fermenter for the production of beer was modeled and controlled with the objective to restore flavor and improving performance [30]. The final value theorem was used to accelerate the search for parameters over the Gray Wolf Optimizer through a metaheuristic algorithm for electrical and electro-mechanical systems [31].…”

Section: Introductionmentioning

confidence: 99%

Parametric Identification of a Countercurrent Aqueous Hydrolysis Process for the Production of Glycerine under Higher Pressure and Temperature

Thirunavukarasu,

Panda,

Panda

et al. 2023

Chem Eng & Technol

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Glycerine is produced by aqueous hydrolysis of tallow fat in the Twitchell process under high pressure (40–60 bar) and temperature (250–270 °C). An effort was made to improve the degree of hydrolysis under optimal operating conditions by varying the feed ratio in a 20‐L Parr reactor. The 1:4 blend of water/tallow at 256 °C and 60 bar was recorded as optimal operating conditions (non‐catalytic), yielding a degree of hydrolysis of 98.4 %. A survey shows that there is a need to find alternate operating control strategies for achieving better efficiency and safe operation. Material and energy balances across the countercurrent hydrolysis tower were developed to calculate the glycerine and free fatty acids in the raffinate and extract phases, using the first principle for dynamic analysis. The model equations were validated and used to identify simple model structures suitable for designing model‐based strategies to control the process temperature in the extraction process.

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Section: Introductionmentioning

confidence: 99%

Parametric Identification of a Countercurrent Aqueous Hydrolysis Process for the Production of Glycerine under Higher Pressure and Temperature

Thirunavukarasu,

Panda,

Panda

et al. 2023

Chem Eng & Technol

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Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

2024

Adv Control Appl

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Temperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable‐domain structure to propose a variable‐domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable‐domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable‐domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable‐domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.

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Endogenous ethanol- and thermo-tolerant strain from Malaysia pineapple peel: Screening and molecular characterisation

Selvanathan,

Abdul Manas,

Zainol

et al. 2024

Food Bioscience

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Modeling and Control of Beer Fermentation for Optimal Flavor and Performance

Cited by 3 publications

References 91 publications

Parametric Identification of a Countercurrent Aqueous Hydrolysis Process for the Production of Glycerine under Higher Pressure and Temperature

Parametric Identification of a Countercurrent Aqueous Hydrolysis Process for the Production of Glycerine under Higher Pressure and Temperature

Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

Endogenous ethanol- and thermo-tolerant strain from Malaysia pineapple peel: Screening and molecular characterisation

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