Systematic Parameter Estimation and Dynamic Simulation of Cold Contact Fermentation for Alcohol-Free Beer Production

Dylan Pilarski; Dimitrios Gerogiorgis

doi:10.3390/pr10112400

Systematic Parameter Estimation and Dynamic Simulation of Cold Contact Fermentation for Alcohol-Free Beer Production

Dylan Pilarski, Dimitrios Gerogiorgis

School of Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Global demand for Low-Alcohol Beer (LAB) and Alcohol-Free Beer (AFB) has surged due to flavor attributes, health benefits, and lifestyle changes, prompting efforts for process intensification. This paper aims to offer a detailed modelling basis for LAB manufacturing study and optimisation. A first-principles dynamic model for conventional beer manufacturing has been re-parameterized and used for dynamic simulation of Cold Contact Fermentation (CCF), an effective LAB and AFB production method, with concentrations tracked along plausible temperature manipulation profiles. Parameter estimation is pursued using industrial production data, with a detailed local sensitivity analysis portraying the effect of key parameter variation on sugar consumption, ethanol production, and key flavor component (ethyl acetate and diacetyl) evolution during (and final values after) CCF. Ethyl acetate (esters in general) affecting fruity flavors emerge as most sensitive to CCF conditions

Original language	English
Article number	2400
Journal	Processes
Volume	10
Issue number	11
Early online date	15 Nov 2022
DOIs	https://doi.org/10.3390/pr10112400
Publication status	E-pub ahead of print - 15 Nov 2022

Keywords / Materials (for Non-textual outputs)

beer
Cold Contact Fermentation (CCF)
Parameter estimation
Dynamic simulation

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10.3390/pr10112400Licence: Creative Commons: Attribution (CC-BY)

processes-10-02400Final published version, 5.3 MBLicence: Creative Commons: Attribution (CC-BY)

https://www.mdpi.com/2227-9717/10/11/2400Licence: Creative Commons: Attribution (CC-BY)

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RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories
Polydorides, N. (Principal Investigator) & Gerogiorgis, D. (Co-investigator)
Engineering and Physical Sciences Research Council
1/08/21 → 20/11/25
Project: Research
Data Analytics for High-Performance Multiobjective Beer Fermentation and Filtration Optimisation
Gerogiorgis, D. (Principal Investigator)
UK-based charities
1/09/17 → 31/10/18
Project: Research

Cite this

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title = "Systematic Parameter Estimation and Dynamic Simulation of Cold Contact Fermentation for Alcohol-Free Beer Production",

abstract = "Global demand for Low-Alcohol Beer (LAB) and Alcohol-Free Beer (AFB) has surged due to flavor attributes, health benefits, and lifestyle changes, prompting efforts for process intensification. This paper aims to offer a detailed modelling basis for LAB manufacturing study and optimisation. A first-principles dynamic model for conventional beer manufacturing has been re-parameterized and used for dynamic simulation of Cold Contact Fermentation (CCF), an effective LAB and AFB production method, with concentrations tracked along plausible temperature manipulation profiles. Parameter estimation is pursued using industrial production data, with a detailed local sensitivity analysis portraying the effect of key parameter variation on sugar consumption, ethanol production, and key flavor component (ethyl acetate and diacetyl) evolution during (and final values after) CCF. Ethyl acetate (esters in general) affecting fruity flavors emerge as most sensitive to CCF conditions",

keywords = "beer, Cold Contact Fermentation (CCF), Parameter estimation, Dynamic simulation",

author = "Dylan Pilarski and Dimitrios Gerogiorgis",

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doi = "10.3390/pr10112400",

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T1 - Systematic Parameter Estimation and Dynamic Simulation of Cold Contact Fermentation for Alcohol-Free Beer Production

AU - Pilarski, Dylan

AU - Gerogiorgis, Dimitrios

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N2 - Global demand for Low-Alcohol Beer (LAB) and Alcohol-Free Beer (AFB) has surged due to flavor attributes, health benefits, and lifestyle changes, prompting efforts for process intensification. This paper aims to offer a detailed modelling basis for LAB manufacturing study and optimisation. A first-principles dynamic model for conventional beer manufacturing has been re-parameterized and used for dynamic simulation of Cold Contact Fermentation (CCF), an effective LAB and AFB production method, with concentrations tracked along plausible temperature manipulation profiles. Parameter estimation is pursued using industrial production data, with a detailed local sensitivity analysis portraying the effect of key parameter variation on sugar consumption, ethanol production, and key flavor component (ethyl acetate and diacetyl) evolution during (and final values after) CCF. Ethyl acetate (esters in general) affecting fruity flavors emerge as most sensitive to CCF conditions

AB - Global demand for Low-Alcohol Beer (LAB) and Alcohol-Free Beer (AFB) has surged due to flavor attributes, health benefits, and lifestyle changes, prompting efforts for process intensification. This paper aims to offer a detailed modelling basis for LAB manufacturing study and optimisation. A first-principles dynamic model for conventional beer manufacturing has been re-parameterized and used for dynamic simulation of Cold Contact Fermentation (CCF), an effective LAB and AFB production method, with concentrations tracked along plausible temperature manipulation profiles. Parameter estimation is pursued using industrial production data, with a detailed local sensitivity analysis portraying the effect of key parameter variation on sugar consumption, ethanol production, and key flavor component (ethyl acetate and diacetyl) evolution during (and final values after) CCF. Ethyl acetate (esters in general) affecting fruity flavors emerge as most sensitive to CCF conditions

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KW - Cold Contact Fermentation (CCF)

KW - Parameter estimation

KW - Dynamic simulation

U2 - 10.3390/pr10112400

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M3 - Article

SN - 2227-9717

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JO - Processes

JF - Processes

IS - 11

M1 - 2400

ER -

Systematic Parameter Estimation and Dynamic Simulation of Cold Contact Fermentation for Alcohol-Free Beer Production

Abstract

Keywords / Materials (for Non-textual outputs)

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Projects

RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories

Data Analytics for High-Performance Multiobjective Beer Fermentation and Filtration Optimisation

Cite this