摘要：AbstractIn industrial sintering processes, it is very important to monitor and control key quality indicators, which are often difficult to measure online. Soft sensor technology is a good solution for online prediction of quality indicators. Nowadays, deep learning is widely used in soft sensors due to its powerful ability in processing nonlinear data. In this paper, a supervised deep belief network (SDBN) is proposed by introducing quality variable into the input variables at each restricted Boltzmann machine to extract quality-related features for soft sensor. With case study on an actual industrial sintering process, SDBN shows much better prediction performance than the original deep belief network and stacked autoencoder.
