Zhao Yantao, Ding Bochuan, Zhang Yuling, Yang Liming, Hao Xiaochen
School of Electrical Engineering, Yanshan University, 438 Hebei Avenue, Qinhuangdao 066004, China.
ISA Trans. 2021 Nov;117:180-195. doi: 10.1016/j.isatra.2021.01.058. Epub 2021 Feb 3.
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.
水泥熟料中游离氧化钙（f-CaO）的含量是衡量水泥质量的重要指标。针对水泥熟料生产过程中存在的强耦合、时变延迟和高度非线性等特点，提出了一种基于多变量时间序列分析和卷积神经网络（MVTS-CNN）的软测量模型，用于在线监测f-CaO含量。基于流程工业的特点，MVTS-CNN建模涉及对水泥生产中耦合关系和时变延迟的详细分析，以及神经网络在多变量时间序列特征提取中的应用。主要研究内容和贡献有四个方面：第一，进一步分析了生产系统中的强耦合，所提出的模型关注的是特定过程之间的数据耦合，而非控制耦合。第二，设计了一种多变量时间序列分析方法，根据延迟范围和最长活动持续时间等信息，选择在不同生产条件下可能对f-CaO含量有直接影响的时间序列。第三，设计并在MVTS-CNN模型中采用了一种多变量时间序列特征提取方法，以提取多变量时间序列特征，如活动持续时间差异特征、耦合特征、非线性特征和关键时间序列特征。第四，提出了一种新的定时匹配方法，该方法将多变量时间序列的粗略定时匹配与关键特征的详细定时匹配相结合，以处理各种生产条件下的时变延迟。与传统的卷积神经网络（CNN）、支持向量机（SVM）和长短期记忆网络（LSTM）相比，结果表明MVTS-CNN模型具有更高的精度、更好的泛化能力和更强的鲁棒性。
