基于混合建模的水泥生料分解过程动态特性研究

为掌握水泥分解炉运行过程的动态特性，采用机理建模与神经网络相结合的方法构建了水泥分解炉一维特性模型，并结合工业数据对该方法的可行性进行验证。结果表明，模型能够准确地计算炉内温度、气体浓度等参数，具有良好的泛化性能。基于所提出的模型，研究了炉内各状态参数的稳态分布特性。此外，对喷煤量、生料下料量、喷氨量以及高温风机转速等操作变量进行阶跃实验，分析上述操作变量改变时分解炉出口温度及出口NO _x 含量的动态响应情况。研究所得相关动态特性规律可以为控制系统的分析、设计和优化提供参考与依据。

Research on dynamic characteristics of cement raw meal decomposition process based on hybrid modeling

In order to understand the dynamic characteristics of cement precalciner, a one-dimensional hybrid model combined of reaction mechanism and neural network was constructed. Feasibility of this method was verified by comparison with industrial data. The results show that the hybrid model can accurately calculate temperature, gas concentration and other parameters in the furnace. Distribution of each parameter along the direction of flue gas flow is consistent with the actual situation. Based on the proposed model, the steady-state distribution characteristics of various state parameters in the furnace were studied. Furthermore, step response tests were carried out. Dynamic responses of temperature and NO _x concentration at furnace outlet were researched when the coal feeding rate, limestone feeding rate, ammonia injection rate and other manipulated parameters were changed respectively. The relevant dynamic characteristics obtained from the research can provide reference for the analysis, design and optimization of the control system.