Batch process quality prediction is an important application in the chemical industry. The complexity of batch processes is characterized by multiphase, nonlinearity, dynamics and uneven durations, so modeling of batch processes is very difficult. Moreover, there are other challenges in quality prediction. As the process trajectories over the whole running duration make contributions to the final targets, the prediction issue embraces tremendously high-dimensional inputs but very low-dimensional outputs. That is, the prediction suffers from a severe dimensional imbalance between inputs and outputs. To conquer these difficulties, this paper proposes a new deep learning-based framework for complex features and quality prediction. The long short-term memory is used to extract quality-relevant hidden features from a long-time sequence in each phase, significantly reducing the predictor dimensions. And these features from different phases are further integrated and compressed by a stacked auto-encoder. A practical industrial example testifies to the efficacy of the proposed framework.

In this letter, a double-active-layer (Zr:SiOx/C:SiOx) resistive switching memory device with a high on/off resistance ratio and small working current (0.02 mA), is presented. Through the analysis of Raman and Fourier transform infrared spectroscopy spectra, we find that graphene oxide exists in the C:SiOx layer. It can be observed that Zr:SiOx/C:SiOx structure has superior switching performance and higher stability compared with the single-active-layer (Zr:SiOx) structure, which is attributed to the existence of graphene oxide flakes formed during the sputter process. I-V characteristics under a series of increasing temperature were analyzed to testify the carrier hopping distance variation, which is further verified by our graphene oxide redox reaction model.