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Enhanced clean-in-place monitoring using ultraviolet induced fluorescence and neural networks

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posted on 07.11.2018 by Alessandro Simeone, Bin Deng, Nicholas Watson, Elliot Woolley
Clean-in-place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

Funding

This research was funded by Innovate UK, grant number 103936 and by the Engineering and Physical Sciences Research Council (EPSRC) UK, grant number EP/I033351/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Sensors

Citation

SIMEONE, A. ... et al, 2018. Enhanced clean-in-place monitoring using ultraviolet induced fluorescence and neural networks. Sensors, 18 (11), 3742.

Publisher

MDPI AG © The Authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

30/10/2018

Publication date

2018-11-02

Notes

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

ISSN

1424-8220

Language

en

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