ArticleSensors (Basel, Switzerland)2019
Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 16 citations in OpenAlex.
- Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning.Sensors (Basel, Switzerland) · 2021Article
- RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process.Sensors (Basel, Switzerland) · 2021Article
- Multi-Model- and Soft-Transition-Based Height Soft Sensor for an Air Cushion Furnace.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors at 1 institution in 1 country.
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Abstract
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.
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