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.
Highlights
Increasing concerns over hygiene in the food and pharmaceuticals processing industry, coupled with high levels of risk aversion to cross contamination of foods, emphasizes the importance of system cleaning within the food production industry
Laboratory scale results are a review of current monitoring capabilities and fouling assessment techniques, a system of hardware obtained from a purpose-built CIP test rig and an image processing procedure described
Lin et al [21] applied artificial neural network (ANN) to multi-spectral data analysis and modelling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface
Summary
Increasing concerns over hygiene in the food and pharmaceuticals processing industry, coupled with high levels of risk aversion to cross contamination of foods ( allergens), emphasizes the importance of system cleaning within the food production industry. Rates of fluid in the small internal volume components (i.e., pipework and heat exchangers), in CIP systems are highly effective at removing system fouling and are suitable for automation: combination with various cleaning and sanitizing fluids (e.g., caustic soda) at elevated temperatures they are employed extensively in the majority of modern food and pharma production [2]. 70% of a food and beverage water use [3] Such is CIP systems are highly system fouling and processer are suitable for automation: the prevalence andthey resource intensity of the technique, it is imperative controlled as are employed extensively in the majority of modernthat foodthe andprocess pharmabe production optimally as possible. Laboratory scale results are a review of current monitoring capabilities and fouling assessment techniques, a system of hardware obtained from a purpose-built CIP test rig and an image processing procedure described. The paper concludes with a discussion of future developments of the technology and it’s applicably to real industrial environments
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