In the aluminum reduction process, the flame hole is an influential index of the whole production situation, which reflects the distribution of the physical field of the reduction cell, the current efficiency and the lifespan of the aluminum reduction cell. Therefore, flame hole situation detection and identification are critical and significant in the whole process of aluminum electrolysis. However, in the practical industrial production, flame hole identification result is coming from the experimental operation workers, with the loss of workers and different experimental levels, the real-time measurement of the flame hole index is still a challenge beyond solution. This article develops a flame hole image classification method based on wavelet extreme learning machine. First, a deep feature set is extracted from the original images with a convolutional neural network. Then, a classification model based on an extreme learning machine with wavelet activation function is developed. Finally, the proposed convolutional neural network–weighted extreme learning machine model is applied to superheat degree real-time detection in the industrial electrolysis cell. The proposed method is evaluated on aluminum production which outperforms existing other superheat degree methods in accuracy and robustness.
A New Control Strategy for the Aluminum Reduction Process Using Economic Model Predictive Control
