A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification

Abstract The superheat degree (SD) in industrial aluminum electrolysis cell is a critical index that can maintain the energy balance, improve the current efficiency and improve production. However, the existing SD identification is mainly relying on artificial experience and the accuracy of SD is far from satisfactory. Further, artificial costs and physical equipment are expensive and time-consuming. In this paper, we propose a deep soft sensor method for SD detection. First, CNN is utilized for flame hole image feature extraction. Second, a semi-supervised extreme learning machine (ELM) that integrates Laplacian regularization is further used for SD classification. The main contributions of the paper are: (1) The proposed CNN-LapsELM utilizes the CNN for flame hole image feature extraction and then ELM for further classification, which fully takes advantage of CNN’s ability for complex feature extraction, ELM’s excellent generalization ability, and high computation efficiency. (2) Both the labeled and unlabeled samples are utilized for the CNN-LapsELM training process. It fully leverages the information contained in unlabeled data. At the same time, Laplacian regularization is utilized for learning the manifold structure of hole image samples, so the performance of the proposed CNN-LapsELM are improved. (3) The proposed CNN-LapsELM algorithm improves the generalization ability and robustness. The comparison result demonstrates that the CNN-LapsELM is superior to the existing SD identification and the accuracy is 87%.