Online cement clinker quality monitoring: A soft sensor model based on multivariate time series analysis and CNN

Abstract

The content of free calcium oxide (f-CaO) in cement clinker is an important index for cement quality. Aiming at the characteristics of strong coupling, time-varying delay and highly non-linearity in cement clinker production, a soft sensor model based on multivariate time series analysis and convolutional neural network (MVTS–CNN) is proposed for the online f-CaO content monitoring. Based on the process industry characteristics, the MVTS–CNN modeling involves the detailed analysis of coupling relationship and time-varying delay in cement production and the application of neural network in multivariate time-series feature extraction. The main researches and contributions are fourfold: First, the strong coupling in the production system is further analyzed, and the proposed model is focused on the data coupling between specific processes, not the control coupling. Second, a multivariate time series analysis method is designed to select the time series that may have direct impacts on the f-CaO content in different production conditions, which is founded on the information on time delay range and longest active duration. Third, a multivariate time series feature extraction method is designed and adopted in the MVTS–CNN model to extract the multivariate time series features, such as active duration difference features, coupling features, nonlinear features and key time series features. Fourth, a new timing matching method, which is combined the rough timing matching of multivariate time series and the detailed timing matching of key features, is proposed to deal with the time-varying delay in various production conditions. Compared with traditional CNN, support vector machines (SVM) and long-short term memory networks (LSTM), the results demonstrate that the MVTS–CNN model has higher accuracy, better generalization ability and superior robustness.