Machine-learning classification of environmental conditions inside a tank by analyzing radar curves in industrial level measurements

Author(s)

Borg, Denis ; Sestito, Guilherme Serpa ; da Silva, Maira Martins

• Document type: Journal article
• Source: FLOW MEASUREMENT AND INSTRUMENTATION; v. 79, p. 9-pg., 2021-04-09.

Abstract

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. (AU)

• FAPESP's process: 18/21336-0 - Towards high speed parallel kinematic machines, Phase II: instrumentation, modeling and control of a flexible manipulator
• Grantee: Maira Martins da Silva
• Support Opportunities: Regular Research Grants