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A multi-sensor approach for fouling level assessment in clean-in-place processes
conference contributionposted on 2016-11-16, 12:13 authored by Alessandro Simeone, Nicholas Watson, Ian Sterritt, Elliot WoolleyElliot Woolley
Clean-in-place systems are largely used in food industry for cleaning interior surfaces of equipment without disassembly. These processes currently utilise an excessive amount of resources and time, as they are based on an open loop (no feedback) control philosophy with process control dependent on conservative over estimation assumptions. This paper proposes a multi-sensor approach including a vision and acoustic system for clean-in-place monitoring, endowed with ultraviolet optical fluorescence imaging and ultrasonic acoustic sensors aimed at assessing fouling thickness within inner surfaces of vessels and pipeworks. An experimental campaign of Clean-in-place tests was carried out at laboratory scale using chocolate spread as fouling agent. During the tests digital images and ultrasonic signal specimens were acquired and processed extracting relevant features from both sensing units. These features are then inputted to an intelligent decision making support tool for the real-time assessment of fouling thickness within the clean-in-place system.
This work was funded by the Innovate UK Technology - Inspired Innovation Collaborative Technical Feasibility Studies - Electronics, Sensors and photonics, Self- Optimising Clean in Place (SOCIP), Project ref. 132205.
- Mechanical, Electrical and Manufacturing Engineering
Published inProcedia Cirp
Pages134 - 139
CitationSIMEONE, A. ...et al., 2016. A multi-sensor approach for fouling level assessment in clean-in-place processes. Procedia Cirp, 55, pp. 134–139.
Source5th CIRP Global Web Conference Research and Innovation for Future Production
Publisher© The Authors. Published by Elsevier
- VoR (Version of Record)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper was presented at the 5th CIRP Global Web Conference Research and Innovation for Future Production, Patras, Greece, 4-6th October. This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported Licence (CC BY-NC-ND). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/4.0/