There are several solutions to measure the tank level in industrial applications. However, the environmental conditions inside this tank, such as turbulence and foam, can jeopardize measurement accuracy and precision. This article proposes a methodology to identify the presence of turbulence and foam in a fermentation tank. The proposal is based on the extraction, selection, and classification of statistical features by machine learning methods. The use of machine learning strategies and statistical features guarantees the necessary robustness and generality for industrial applications. Actual data obtained from a must fermentation tank of a sugar-alcohol industrial plant were used for training and verifying one Artificial Neural Network-based and three Support Vector Machine-based classifiers. These classifiers obtained accuracy over 98% for different environmental conditions proving the effectiveness of the proposed methodology.

有几种解决方案可用于测量工业应用中的储罐液位。但是，该水箱内部的环境条件（例如湍流和泡沫）会危害测量的准确性和精度。本文提出了一种方法来识别发酵罐中是否存在湍流和泡沫。该提议基于通过机器学习方法对统计特征的提取，选择和分类。机器学习策略和统计功能的使用保证了工业应用必要的鲁棒性和通用性。从糖醇工业工厂的必需发酵罐获得的实际数据用于训练和验证一个基于人工神经网络的分类器和三个基于支持向量机的分类器。