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Open AccessArticle

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

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Institute of Technology, University of Pará, Belém 66075-110, Brazil
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Department of Automation, Specialist engineer, Aluminum of Brazil (ALBRAS), Barcarena 68445-000, Brazil
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Reduction Area, Process Engineering Manager, Aluminum of Brazil (ALBRAS), Barcarena 68445-000, Brazil
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Department of Automation, Manager of Energy, Utilities, Automation, and Predictive, Aluminum of Brazil (ALBRAS), Barcarena 68445-000, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5255; https://doi.org/10.3390/s19235255
Received: 9 September 2019 / Revised: 16 October 2019 / Accepted: 22 October 2019 / Published: 29 November 2019
(This article belongs to the Section Intelligent Sensors)
Primary aluminum production is an uninterrupted and complex process that must operate in a closed loop, hindering possibilities for experiments to improve production. In this sense, it is important to have ways to simulate this process computationally without acting directly on the plant, since such direct intervention could be dangerous, expensive, and time-consuming. This problem is addressed in this paper by combining real data, the artificial neural network technique, and clustering methods to create soft sensors to estimate the temperature, the aluminum fluoride percentage in the electrolytic bath, and the level of metal of aluminum reduction cells (pots). An innovative strategy is used to split the entire dataset by section and lifespan of pots with automatic clustering for soft sensors. The soft sensors created by this methodology have small estimation mean squared error with high generalization power. Results demonstrate the effectiveness and feasibility of the proposed approach to soft sensors in the aluminum industry that may improve process control and save resources. View Full-Text
Keywords: primary aluminum production; soft sensor; neural network; real data; estimation; clustering methods primary aluminum production; soft sensor; neural network; real data; estimation; clustering methods
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Souza, A.M.F.; Soares, F.M.; Castro, M.A.G.; Nagem, N.F.; Bitencourt, A.H.J.; Affonso, C.M.; Oliveira, R.C.L. Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods. Sensors 2019, 19, 5255.

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