Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application - Lei, Yongxiang; Karimi, Hamid Reza; Cen, Lihui; Chen, Xiaofang; Xie, Yongfang (Control engineering practice, 2021)
Data-driven soft modeling has been extensively used for industrial processes to estimate key quality indicators which are hard to measure by some physical devices. However,the existing deep soft methods faces the challenge of training efficiency, gradient diminishing and explosion. Constructing an accurate and robust soft model is still a challenging topic from an application point of view. This paper develops an effective and efficient soft method (SAE-WELM) for processes modeling. First, a stacked autoencoder (SAE) is used to extract the deep features. Then, a top-layer extreme learning machine (ELM) is further applied to a plant-wide industrial aluminum production process. The activation function is wavelet kernel. Finally, the approximation and convergence of the proposed SAE-WELM are theoretically proved. The industrial case demonstrates that SAE-WELM captures the deep features faster than other iterative-based neural networks, and the accuracy and robustness outperform the existing state-of-the-art methods. •The traditional deep architectures contain the issue of gradient diminishing and exploding. To improve the training efficiency and generalization ability, a novel deep soft model based on ELM with a wavelet kernel function is proposed.•A data-driven soft model was developed which can preserve the universal approximation ability and extract the deep and complex features in-process data. In contrast to the Gaussian kernel, the proposed approach combines the wavelet kernel in ELM, the generalization ability and training speed can be greatly improved.•To obtain a stable and applicable model, the approximation ability and convergence of the proposed SAE-WELM are proved by constructing a Lyapunov function.