A multi-sensor approach for fouling level assessment in clean-in-place processes

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

A multi-sensor approach for fouling level assessment in clean-in-place processes

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

Assessing surface cleanliness: a pathway to sustainability

This is a conference paper abstract presented at the 9th International Conference on Food Chemistry & Technology (FCT-2023), Paris, France, 27th - 29th November 2023.</p

Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning

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 always over-clean, incurring significant economic and environmental costs. This work has studied the use of ultrasonic measurements and a range of different machine learning classification methods to monitor the fouling removal of food materials in plastic and metal cylindrical pipes. The experimental results showed that the developed techniques could predict the presence of fouling with prediction confidence as high as 100% for both plastic and metal pipes. The sensor technique performed marginally better in the plastic pipe and similar performance was found for the all of the machine learning methods studied. This work has demonstrated the potential of low-cost ultrasonic sensors to monitor and therefore optimise cleaning processes within pipes. It is discussed how new data set labelling strategies will be required for the techniques to be used effectively within production environments

21st Century Meat Inspector project report

Poultry is the most widely consumed meat in the UK, and its effective inspection within processing facilities is essential to ensure regulatory compliance. Poultry inspection is performed manually and is extremely challenging due to the short time available to inspect each bird and the sustained level of concentration required.  The project focused specifically on post-mortem inspection of poultry, adopting a benefits realisation approach to determine the requirements for any new technologies and ensure that business benefits are delivered to all stakeholders within the poultry chain.  This interdisciplinary project included expertise in a variety of complimentary inspection technologies; optical (visual, Near-Infrared, Infrared, Hyperspectral), X-ray and Ultrasonic and IT-enabled benefits realisation management with the Hartree Centre (STFC), a food business operator (referred to throughout as Food Co.) and CSB as project partners. The main findings of the project include: the main requirements for any new digital technologies to assist meat inspectors (MIs) and poultry facilities were identified as: clear business benefits; robust and reliable; easy to use and clean deep learning can be used to identify abnormal colour from carcass images with a sufficient number of training images, but more efficient data labelling methods are required hyperspectral optical and X-ray imaging methods can identify quality issues such as wooden breast and white stripe in chicken breasts. </p

Ultrasonic measurements and machine learning for monitoring the removal of surface fouling during clean-in-place processes

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-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials

Intelligent sensors for sustainable food and drink manufacturing

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

BackgroundTranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding.MethodsWe did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124.FindingsBetween July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98).InterpretationWe found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial.</div