Institute of Technology, University of Pará, Belém 66075-110, Brazil.
Department of Automation, Specialist engineer, Aluminum of Brazil (ALBRAS), Barcarena 68445-000, Brazil.
Sensors (Basel). 2019 Nov 29;19(23):5255. doi: 10.3390/s19235255.
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.
原铝生产是一个连续而复杂的过程，必须在闭环中运行，这阻碍了改进生产的实验可能性。从这个意义上说，拥有在不直接作用于工厂的情况下对该过程进行计算模拟的方法很重要，因为这种直接干预可能是危险、昂贵和耗时的。本文通过结合实际数据、人工神经网络技术和聚类方法来创建软传感器，以估计温度、电解质浴中的氟化铝百分比和铝还原槽（槽）的金属水平，从而解决了这个问题。该方法使用了一种创新的策略，通过自动聚类按部分和槽的寿命对整个数据集进行分割，为软传感器创建了软传感器。该方法创建的软传感器具有较小的估计均方误差和较高的泛化能力。结果表明，该方法在铝工业中的软传感器具有有效性和可行性，可用于改进过程控制和节省资源。
