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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.