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All Outputs (29)

Net zero roadmap modelling for sustainable dairy manufacturing and distribution (2023)
Journal Article
Malliaroudaki, M. I., Watson, N. J., Glover, Z. J., Nchari, L. N., & Gomes, R. L. (2023). Net zero roadmap modelling for sustainable dairy manufacturing and distribution. Chemical Engineering Journal, 475, Article 145734. https://doi.org/10.1016/j.cej.2023.145734

Energy-derived carbon emissions from dairy manufacturing and distribution are significant. Meeting a net zero carbon target is a global priority, and to that end the dairy industry is engaging an emission-reduction strategy. Modelling tools that can... Read More about Net zero roadmap modelling for sustainable dairy manufacturing and distribution.

Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements (2023)
Journal Article
Bowler, A., Ozturk, S., di Bari, V., Glover, Z. J., & Watson, N. J. (2023). Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements. Food Control, 147, Article 109622. https://doi.org/10.1016/j.foodcont.2023.109622

In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learni... Read More about Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements.

Linking the yield stress functionality of polyglycerol polyricinoleate in a highly filled suspension to its molecular properties (2022)
Journal Article
Price, R., Gray, D., Watson, N., Vieira, J., & Wolf, B. (2022). Linking the yield stress functionality of polyglycerol polyricinoleate in a highly filled suspension to its molecular properties. LWT - Food Science and Technology, 165, Article 113704. https://doi.org/10.1016/j.lwt.2022.113704

Polyglycerol polyricinoleate (PGPR) is a food emulsifier with a unique yield stress reducing efficacy in fat-based suspensions. There are many commercially available PGPRs, and the different products vary in their impact on the yield stress. Choosing... Read More about Linking the yield stress functionality of polyglycerol polyricinoleate in a highly filled suspension to its molecular properties.

Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess (2022)
Journal Article
Fisher, O., Watson, N. J., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. (2022). Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess. Biochemical Engineering Journal, 185, Article 108499. https://doi.org/10.1016/j.bej.2022.108499

Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing mod... Read More about Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess.

A review of ultrasonic sensing and machine learning methods to monitor industrial processes (2022)
Journal Article
Bowler, A. L., Pound, M. P., & Watson, N. J. (2022). A review of ultrasonic sensing and machine learning methods to monitor industrial processes. Ultrasonics, 124, Article 106776. https://doi.org/10.1016/j.ultras.2022.106776

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved general... Read More about A review of ultrasonic sensing and machine learning methods to monitor industrial processes.

Energy management for a net zero dairy supply chain under climate change (2022)
Journal Article
Malliaroudaki, M. I., Watson, N. J., Ferrari, R., Nchari, L. N., & Gomes, R. L. (2022). Energy management for a net zero dairy supply chain under climate change. Trends in Food Science and Technology, 126, 153-167. https://doi.org/10.1016/j.tifs.2022.01.015

Background: The dairy industry requires substantial energy resources at all stages of production and supply to meet consumer needs in terms of quantity, quality and food safety. The expected future climate change effects will cause serious uncertaint... Read More about Energy management for a net zero dairy supply chain under climate change.

How hydrocolloids can control the viscoelastic properties of acid-swollen collagen pastes (2022)
Journal Article
Sobanwa, M., Foster, T. J., Yakubov, G., & Watson, N. J. (2022). How hydrocolloids can control the viscoelastic properties of acid-swollen collagen pastes. Food Hydrocolloids, 126, Article 107486. https://doi.org/10.1016/j.foodhyd.2022.107486

The interactions between proteins and polysaccharides are of considerable importance in the food industry. In this study, the effect of adding non-charged methylcellulose (MC), hydroxypropylmethylcellulose (HPMC), medium (GM) and high (GH) molecular... Read More about How hydrocolloids can control the viscoelastic properties of acid-swollen collagen pastes.

Intelligent Sensors for Sustainable Food and Drink Manufacturing (2021)
Journal Article
Watson, N. J., Bowler, A. L., Rady, A., Fisher, O. J., Simeone, A., Escrig, J., …Adedeji, A. A. (2021). Intelligent Sensors for Sustainable Food and Drink Manufacturing. Frontiers in Sustainable Food Systems, 5, Article 642786. https://doi.org/10.3389/fsufs.2021.642786

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 pro... Read More about Intelligent Sensors for Sustainable Food and Drink Manufacturing.

Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation (2021)
Journal Article
Bowler, A. L., Pound, M. P., & Watson, N. J. (2021). Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation. Fermentation, 7(4), Article 253. https://doi.org/10.3390/fermentation7040253

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 ident... Read More about Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation.

Convolutional feature extraction for process monitoring using ultrasonic sensors (2021)
Journal Article
Bowler, A., Pound, M., & Watson, N. (2021). Convolutional feature extraction for process monitoring using ultrasonic sensors. Computers and Chemical Engineering, 155, Article 107508. https://doi.org/10.1016/j.compchemeng.2021.107508

Ultrasonic sensors are a low-cost and in-line technique and can be combined with machine learning for industrial process monitoring. However, training accurate machine learning models for process monitoring using sensor data is dependant on the featu... Read More about Convolutional feature extraction for process monitoring using ultrasonic sensors.

Transfer learning for process monitoring using reflection-mode ultrasonic sensing (2021)
Journal Article
Bowler, A. L., & Watson, N. J. (2021). Transfer learning for process monitoring using reflection-mode ultrasonic sensing. Ultrasonics, 115, Article 106468. https://doi.org/10.1016/j.ultras.2021.106468

The fourth industrial revolution is set to integrate entire manufacturing processes using industrial digital technologies such as the Internet of Things, Cloud Computing, and machine learning to improve process productivity, efficiency, and sustainab... Read More about Transfer learning for process monitoring using reflection-mode ultrasonic sensing.

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning (2021)
Journal Article
Bowler, A., Escrig, J., Pound, M., & Watson, N. (2021). Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation, 7(1), Article 34. https://doi.org/10.3390/fermentation7010034

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultraso... Read More about Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning.

Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study (2021)
Journal Article
FISHER, O. J., WATSON, N. J., PORCU, L., BACON, D., RIGLEY, M., & GOMES, R. L. (2021). Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study. Journal of Cleaner Production, 296, Article 126242. https://doi.org/10.1016/j.jclepro.2021.126242

Process manufacturing industries constantly strive to make their processes increasingly sustainable from an environmental and economic perspective. A manufacturing system model is a powerful tool to holistically evaluate various manufacturing configu... Read More about Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression (2020)
Journal Article
Simeone, A., Woolley, E., Escrig, J., & Watson, N. J. (2020). Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression. Sensors, 20(13), Article 3642. https://doi.org/10.3390/s20133642

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for inn... Read More about Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.

Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes (2020)
Journal Article
Escrig, J. E., Simeone, A., Woolley, E., Rangappa, S., Rady, A., & Watson, N. (2020). Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes. Food and Bioproducts Processing, 123, 1-13. https://doi.org/10.1016/j.fbp.2020.05.003

Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clea... Read More about Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes.

Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems (2020)
Journal Article
Watson, N. J., Fisher, O. J., Escrig, J. E., Witt, R., Porcu, L., Bacon, D., …Gomes, R. L. (2020). Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Computers and Chemical Engineering, 140, Article 106881. https://doi.org/10.1016/j.compchemeng.2020.106881

The increasing availability of data, due to the adoption of low-cost industrial internet of things technologies, coupled with increasing processing power from cloud computing, is fuelling increase use of data-driven models in manufacturing. Utilising... Read More about Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems.

Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning (2020)
Journal Article
Escrig, J., Woolley, E., Simeone, A., & Watson, N. (2020). Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning. Food Control, 116, Article 107309. https://doi.org/10.1016/j.foodcont.2020.107309

Food and drink production equipment is routinely cleaned to ensure it remains hygienic and operating under optimal conditions. A limitation of existing cleaning systems is that they do not know when the fouling material has been removed so nearly al... Read More about Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning.

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning (2020)
Journal Article
Bowler, A. L., Bakalis, S., & Watson, N. J. (2020). Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning. Sensors, 20(7), Article 1813. https://doi.org/10.3390/s20071813

Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-co... Read More about Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning.

Intelligent Resource Use to Deliver Waste Valorisation and Process Resilience in Manufacturing Environments (2020)
Journal Article
Fisher, O. J., Watson, N. J., Escrig, J. E., & Gomes, R. L. (2020). Intelligent Resource Use to Deliver Waste Valorisation and Process Resilience in Manufacturing Environments. Johnson Matthey Technology Review, 64(1), 93-99. https://doi.org/10.1595/205651320x15735483214878

© 2020 Johnson Matthey Circular economy (CE) thinking has emerged as a route to sustainable manufacture, with related cradle-to-cradle implications requiring implementation from the design stage. The challenge lies in moving manufacturing environment... Read More about Intelligent Resource Use to Deliver Waste Valorisation and Process Resilience in Manufacturing Environments.