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Sensors (Basel). 2018 Nov 2;18(11). pii: E3742. doi: 10.3390/s18113742.

Enhanced Clean-In-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks.

Author information

1
Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou 515063, China. simeone@stu.edu.cn.
2
Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou 515063, China. 17bdeng@stu.edu.cn.
3
Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, UK. Nicholas.Watson@nottingham.ac.uk.
4
Centre for Sustainable Manufacturing and Recycling Technologies (SMART), Loughborough University, Loughborough LE11 3TU, UK. E.B.Woolley@lboro.ac.uk.

Abstract

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.

KEYWORDS:

fluorosensing; image processing; monitoring; neural network; resource efficiency

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