VMD-SEAE-TL-Based Data-Driven soft sensor modeling for a complex industrial batch processes

Abstract

On complex batch industrial processes, soft sensor modeling plays a key role in process control and monitoring. Considering the nonlinearity, time-varying, and repetitive nature of the batch process, this paper proposes a soft sensor modeling method (VMD-SEAE-TL) based on variational mode decomposition (VMD), stacked enhanced autoencoder (SEAE) and transfer learning (TL) algorithms for online detection of key variables in batch industrial production processes. Firstly, the raw industrial process data are decomposed and reconstructed using VMD to achieve denoising and reduce the non-smooth characteristics of the data series. Secondly, based on the reconstructed process data, the SEAE network is used to deeply extract data feature information and achieve high accuracy regression prediction. Here, during the SEAE training process, each layer of the enhanced autoencoder (EAE) network reconstructs both the network input and the original input. The purpose of this operation is to extract the deep feature information of the process data and to ensure that there is no cumulative loss of the original input information. Further, it is considered that the working conditions in the batch industrial production process are time-varying, and this often makes it difficult for the model trained on the source domain data to accurately predict the trend of the process variables in the target domain. For this problem, the maximum mean deviation (MMD)-based transfer learning algorithm is introduced, which is used to solve the domain adaption problem with changing working conditions. Finally, based on two actual industrial process cases, the effectiveness and practicality of the proposed soft sensor are verified by various experimental results.