数据与模型驱动的水泥生料分解率软测量模型

水泥生料在分解炉内分解过程的质量指标是生料分解率（Raw meal decomposition ratio，RMDR），由于生料边界条件频繁变化且人工离线检测周期为2小时，致使产品质量指标合格率低且极易造成预热器C5下料管堵塞.为了解决上述问题，本文提出了基于数据与模型驱动的水泥生料分解率软测量模型，由基于Kullback-Leibler（KL）散度密度比的异常值检测、基于机理模型的生料分解率模型、基于层级Sigmoid（S）核函数的生料分解率模型、生料分解率离线检测模型和基于模糊模型的协调因子组成.实际应用结果表明，所提出的模型能够根据当前工况的变化选择正确的子模型，并且使生产远离故障工况.

Data and Model-based Soft Measurement Model of Cement Raw Meal Decomposition Ratio

The raw meal decomposition (RMDR) is a quality index in cement raw meal calcination process. The product quality index is low and it is easy to cause the preheater C5 feeding tube to be blocked because of varying boundary conditions and manual offline detection period of two hours. The product quality index is low and it is easy to cause the preheater C5 feeding tube to be blocked. To solve the above problem, a soft measurement model of raw meal decomposition ratio was proposed based on data and model. This model for raw meal calcination process consists of five modules, namely an outlier detection based on Kullback-Leibler (KL) divergence density ratio, a raw meal decomposition ratio model based on mechanism model, a raw meal decomposition ratio model based on hierarchical Sigmoid (S) kernel function, an offline detection model, and a coordination factor based on fuzzy model. The actual application results show that the model proposed can select right submodel according to current operating conditions, and is far from fault condition by the practical application results.