Data driven stochastic modelling and simulation of cooling demand within breweries

Hubert, Stefan; Helmers, Thorben; Groß, F.; Delgado, Antonio

doi:10.1016/j.jfoodeng.2015.06.032

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…Where the density and specific heat capacity of the wort is described using the empirical relationships established in [21], the fermentation time of the wort will be influenced by the type of beer, primarily categorized as either lager or ale. As the fermentation process has been identified as a process with significant energy flexibility potential within the brewery, the energy flexibility within this paper will focus on the fermentation tanks [14].…”

Section: ) Fermentation Processmentioning

confidence: 99%

Energy Flexibility Potential in the Brewery Sector: A Multi-agent Based Simulation of 239 Danish Breweries

Howard

Engvang

et al. 2022

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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The beverage industry is a typical food processing industry, accounts for significant energy consumption, and has flexible demands. However, the deployment of energy flexibility in the beverage industry is complex and challenging. Furthermore, activation of energy flexibility from the whole brewery industry is necessary to ensure grid stability. Therefore, this paper assesses the energy flexibility potential of Denmark's brewery sector based on a multi-agent-based simulation. 239 individual brewery facilities are simulated, and each facility, as an agent, can interact with the energy system market and make decisions based on its underlying parameters and operational restrictions. The results show that the Danish breweries could save 1.56 % of electricity costs annually while maintaining operational security and reducing approximately 1745 tonnes of CO2 emissions. Furthermore, medium-size breweries could obtain higher relative benefits by providing energy flexibility, especially those producing lager and ale. The result also shows that the breweries' relative saving potential is electricity marketdependent.

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Section: ) Fermentation Processmentioning

confidence: 99%

Energy Flexibility Potential in the Brewery Sector: A Multi-agent Based Simulation of 239 Danish Breweries

Howard

Engvang

et al. 2022

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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“…Special attention must be paid to modeling using petri nets or so-called reference nets and simulation based on them. Hubert et al [25] model and simulate the cooling demand within small and medium-sized breweries. Based on an extensive database with different recipes, a forecast can be made over a period of almost one year and a detailed validation be presented.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Energy and Media Demand of Batch-Oriented Production Systems in the Beverage Industry

et al. 2022

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The global brewing industry is facing enormous environmental challenges and is urgently required to produce sustainably and efficiently. The rising costs of energy and electricity are forcing small and medium-sized breweries in particular, which are confronted with barriers such as lack of capital and know-how, to make substantial changes. This article presents an extended approach to prognose the energy and media demand for batch-oriented production of a brewery. Therefore, based on a modeling editor as well as a standardized data structure and an approach to determine the simulation-relevant parameters, a solution for fast and easy model generation was developed. Extensive measurement recordings within a brewhouse were performed to create a comprehensive model with recipe-specific parameters and detailed production plans. A simulation model can be generated automatically from a configuration file in a simulation environment that has been extended to include the mapping of batch-oriented operation. A validation is presented and a maximum deviation of the electrical and thermal energy demand of 1–2% is achieved. In combination with a preliminary work, the holistic simulation of the complex combined production of batch-oriented and discrete operation within the brewery is presented and allows comprehensive analysis as well as optimization towards sustainable production.

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“…The need for specialized knowledge and data-driven mining techniques to describe bread quality has been well exposed both in theory and practice ( Della Valle et al., 2014 ; Hadiyanto et al., 2008 ; Liu and Scanlon, 2003 ; Parimala and Sudha, 2015 ; Zanoni et al., 1993 , 1994 ; Zhang and Datta, 2006 ). Data-driven product development – supported by modern data-mining and knowledge discovery tools – is well within the broader future scope of food engineering in general as this field is called upon to make the most of the innovative information technology ( Hubert et al., 2016 ; Saguy et al., 2013 ; Thakur et al., 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Taguchi-generalized regression neural network micro-screening for physical and sensory characteristics of bread

Besseris

2018

Heliyon

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Generalized regression neural networks (GRNN) may act as crowdsourcing cognitive agents to screen small, dense and complex datasets. The concurrent screening and optimization of several complex physical and sensory traits of bread is developed using a structured Taguchi-type micro-mining technique. A novel product outlook is offered to industrial operations to cover separate aspects of smart product design, engineering and marketing. Four controlling factors were selected to be modulated directly on a modern production line: 1) the dough weight, 2) the proofing time, 3) the baking time, and 4) the oven zone temperatures. Concentrated experimental recipes were programmed using the Taguchi-type L9(34) OA-sampler to detect potentially non-linear multi-response tendencies. The fused behavior of the master-ranked bread characteristics behavior was smart sampled with GRNN-crowdsourcing and robust analysis. It was found that the combination of the oven zone temperatures to play a highly influential role in all investigated scenarios. Moreover, the oven zone temperatures and the dough weight appeared to be instrumental when attempting to synchronously adjusting all four physical characteristics. The optimal oven-zone temperature setting for concurrent screening-and-optimization was found to be 270–240 °C. The optimized (median) responses for loaf weight, moisture, height, width, color, flavor, crumb structure, softness, and elasticity are: 782 g, 34.8 %, 9.36 cm, 10.41 cm, 6.6, 7.2, 7.6, 7.3, and 7.0, respectively.

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Data driven stochastic modelling and simulation of cooling demand within breweries

Cited by 8 publications

References 33 publications

Energy Flexibility Potential in the Brewery Sector: A Multi-agent Based Simulation of 239 Danish Breweries

Energy Flexibility Potential in the Brewery Sector: A Multi-agent Based Simulation of 239 Danish Breweries

Simulation of Energy and Media Demand of Batch-Oriented Production Systems in the Beverage Industry

Taguchi-generalized regression neural network micro-screening for physical and sensory characteristics of bread

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