Deep learning near-infrared quality prediction based on multi-level dynamic feature

Abstract

As a fast, efficient, nondestructive, and pollution-free technology, near-infrared spectroscopy (NIRS) has become a practical method for online quality prediction in the process industry. The inherent dynamics of the industrial processes make the NIR spectral data no longer accord with the assumption of independent and identically distributed, which leads to the limitations of the static quality prediction models. In addition, the existing NIRS-based quality prediction methods lack consideration of the possible nonlinear relationship between NIR spectra and quality variables of complex organic samples and the over sensitivity brought by molecular-level detection. This paper proposes a multi-level dynamic feature-based deep learning NIR quality prediction method to solve the above problems. Firstly, short-term feature extraction is mainly based on 2D convolution neural networks (CNNs) to extract short-term dynamic features and eliminate the multi-collinearity and information redundancy with a 2D NIR dynamic spectral matrix input. Thereinto, considering the levels of short-term dynamic features, a combination of dilated CNN and the dense connection is proposed to construct the multi-level features and ensure the continuisty and integrity of the time dimension. Secondly, based on the extracted short-term dynamic features, the long-term dynamic spectral prediction constructed with gate recurrent unit (GRU) is proposed to capture long-term dependence between NIR spectra and predict the NIR spectrum at the next time step. Here, considering the inability of the original GRU to take account of the dynamic feature at all time steps and the possible time lag between industrial laboratory sampling and NIRS detection, temporal attention is utilized to redistribute and fuse the dynamic features of the GRU at all time steps. Finally, a fully connected layer is used to realize the regression from the NIR spectrum of the next moment to the corresponding quality variables. The effectiveness and accuracy of the method are verified by a case of the reaction and distillation process of 2,6 xylenol and corresponding NIR spectral data.