Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

中文翻译：

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智能工业清洁：利用基于机器学习的回归的多传感器方法。

有效清洁设备对于食品安全生产至关重要，但需要大量时间和资源，例如水、能源和化学品。为了优化食品生产设备的清洁，需要创新技术来监测设备表面污垢的清除情况。在这项工作中，光学和超声波传感器用于监测台式设备中具有不同理化性质的食品材料的污垢去除情况。开发定制的信号和图像处理程序来监控清洁过程，并开发神经网络回归模型来预测表面上残留的污垢量。结果表明，所研究的三种不同的食品污垢材料通过不同的清洁机制从测试部分去除，神经网络模型能够预测清洁过程中存在的污垢的面积和体积，准确度高达 98% 和 97% ， 分别。这项工作表明传感器和机器学习方法可以有效地结合起来来监控清洁过程。