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Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.

Alessandro Simeone1, Elliot Woolley2, Josep Escrig3

  • 1Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou 515063, China.

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Summary
This summary is machine-generated.

Innovative sensors and machine learning accurately monitor food equipment cleaning. This technology optimizes resource use by predicting remaining fouling, ensuring safer food production with high precision.

Keywords:
Clean-in-Placeartificial neural networksdigital manufacturingindustry 4.0machine learningoptical sensorsprocess optimisationregressionultrasonic sensors

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Area of Science:

  • Food Science and Technology
  • Sensor Technology
  • Machine Learning Applications

Background:

  • Effective cleaning of food production equipment is critical for food safety.
  • Current cleaning processes are resource-intensive, requiring significant water, energy, and chemicals.
  • There is a need for advanced monitoring technologies to optimize equipment cleaning efficiency.

Purpose of the Study:

  • To develop and evaluate innovative sensor-based methods for monitoring the removal of food fouling.
  • To utilize signal and image processing with machine learning to predict residual fouling.
  • To assess the effectiveness of optical and ultrasonic sensors in real-time cleaning assessment.

Main Methods:

  • Utilized optical and ultrasonic sensors to monitor fouling removal from a benchtop rig.
  • Developed tailored signal and image processing techniques for cleaning monitoring.
  • Implemented a neural network regression model to predict the quantity of remaining fouling.

Main Results:

  • Investigated the removal mechanisms of three distinct food fouling materials.
  • Achieved high prediction accuracies for remaining fouling: 98% for area and 97% for volume.
  • Demonstrated the capability of sensors and machine learning to accurately track cleaning progress.

Conclusions:

  • Sensors combined with machine learning provide an effective solution for monitoring food equipment cleaning.
  • This approach can lead to optimized resource utilization (water, energy, chemicals) in food processing.
  • The developed technology enhances the safety and efficiency of food production environments.