A novel virtual sample generation method based on a modified conditional Wasserstein GAN to address the small sample size problem in soft sensing

Abstract

In the modern chemical industry process, soft sensing has been widely used. However, the lack of valid and sufficient data has made it difficult to apply advanced soft sensor modeling methods to realistic scenarios. In this paper, a novel soft sensing method based on deep learning is proposed to handle the problem of small data. Aiming at handling the issue of small sample size, a novel virtual sample generation method embedding a deep neural network as a regressor into conditional Wasserstein generative adversarial networks with gradient penalty (rCWGAN) is presented. In rCWGAN, conditional variables are introduced to make the training supervised and a dual training algorithm is specially designed. With the advanced structure and the designed training algorithm, rCWGAN has powerful sample generation capabilities and can well predict quality variables. Finally, an experiment on the purified terephthalic acid (PTA) solvent system is carried out for the validation of the presented rCWGAN. The simulation results indicate that the presented rCWGAN has good sample approximation ability and acceptable prediction accuracy with the small sample size task.