Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Bowler, Alexander; Escrig, Josep; Pound, Michael P.; Watson, Nick J.

doi:10.3390/fermentation7010034

Cited by 16 publications

(14 citation statements)

References 38 publications

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“…Two sets of fermentations were monitored: one in a 30 L laboratory scale vessel at the University of Nottingham and the second in a 2000 L industrial scale fermenter at the Totally Brewed brewery in Nottingham, UK. Full experimental details for the laboratory scale fermentations are included in [18]. The laboratory scale dataset consisted of 13 fermentations and the industrial scale dataset consisted of 5 fermentations.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the US waveform features, the process temperature was also used as an input. Although US sensors can accurately monitor fermentations without inclusion of the temperature as a feature [18], temperature sensors are already installed on most industrial vessels. As such, this data can be exploited in the ML models with no further effort in sensor installation or data collection.…”

Section: Ultrasonic Waveform Featuresmentioning

confidence: 99%

“…In-line and on-line sensors underpin this transformation by collecting the real-time data to provide automatic decision-making and minimise human involvement [6]. Several in-line and on-line methods to monitor alcoholic fermentation have been investigated, such as near-infrared spectroscopy [3,7], Raman spectroscopy [8,9], mid-infrared spectroscopy [10], Fourier transform infrared spectroscopy [11], MEMS resonators [12], CO 2 emission monitoring [13], and ultrasonic (US) sensors [14][15][16][17][18]. Typically, these techniques use calibration techniques to correlate sensor data to material composition across the full range of process conditions (e.g., temperature) [3].…”

Section: Introductionmentioning

confidence: 99%

“…Hussein et al, (2012) used the US velocity, process temperature, and nine signal features extracted from the time and frequency domains to predict wort density using an artificial neural network [14]. inputted time domain signal features into Long Short-Term Memory (LSTM) neural networks to predict the volume of alcohol percentage throughout fermentation [18].…”

Section: Introductionmentioning

confidence: 99%

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Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

2021

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Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling identification of lagging fermentations or prediction of ethanol production end points. Ultrasonic sensors have previously been used for in-line and on-line fermentation monitoring and are increasingly being combined with machine learning models to interpret the sensor measurements. However, fermentation processes typically last many days and so impose a significant time investment to collect data from a sufficient number of batches for machine learning model training. This expenditure of effort must be multiplied if different fermentation processes must be monitored, such as varying formulations in craft breweries. In this work, three methodologies are evaluated to use previously collected ultrasonic sensor data from laboratory scale fermentations to improve machine learning model accuracy on an industrial scale fermentation process. These methodologies include training models on both domains simultaneously, training models in a federated learning strategy to preserve data privacy, and fine-tuning the best performing models on the industrial scale data. All methodologies provided increased prediction accuracy compared with training based solely on the industrial fermentation data. The federated learning methodology performed best, achieving higher accuracy for 14 out of 16 machine learning tasks compared with the base case model.

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Section: Methodsmentioning

confidence: 99%

Section: Ultrasonic Waveform Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

2021

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“…Fully online methods have also been developed including NIR (Svendsen et al, 2016;Vann et al, 2017), FT-NIR (Veale et al, 2007) and dispersive Raman spectroscopy (Shaw et al, 1999). An US sensor was chosen for this CS due to affordability compared to other options, as well as the additional benefits of US sensors discussed in CS1 (Bowler et al, 2021).…”

Section: Case Study 3: Fermentation Introductionmentioning

confidence: 99%

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Watson

Bowler

Rady

et al. 2021

Front. Sustain. Food Syst.

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Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

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Temperature monitoring system of beer fermentation and brewing based on immune fuzzy PID controller

Song¹,

Meng²,

Chen

2023

Adv Control Appl

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Beer is one of the popular drinks, the temperature control in the process of beer fermentation plays a crucial role. The current temperature control method mainly uses the traditional PID control, but its control adjustment time is long, the overshoot is large, the control effect still needs to be improved. A beer fermentation and brewing temperature monitoring system based on immune fuzzy PID controller was designed in this experiment. Immune fuzzy PID controller is a nonlinear controller, which combines the advantages of traditional PID controller and fuzzy controller and refers to the regulatory mechanism of biological immune system, and obtains good suitable characteristics by controlling the parameter values of the system. PID converts the rule information into fuzzy information by fuzzy basic theory and stores it in computer database. By referring to the actual situation of PID, the computer uses fuzzy reasoning to adjust the PID parameters. The beer fermentation temperature monitoring system based on the traditional PID controller is compared with the proposed system. Under the control of the designed temperature monitoring system, the temperature has a certain effect on the fermentation speed of beer. The fermentation time of high temperature fermentation (16°C) is 3 days shorter than that of normal temperature fermentation (10°C). The robustness and applicability of the system are verified.

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Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

Cited by 16 publications

References 38 publications

Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Temperature monitoring system of beer fermentation and brewing based on immune fuzzy PID controller

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