基于焚烧运行参数的垃圾热值软测量模型

在垃圾焚烧过程中,入炉垃圾热值的变化对燃烧的稳定性会产生很大的影响.针对在垃圾焚烧过程中垃圾热值难以在线测量的实际状况,采用基于小脑神经网络的垃圾热值预测模型,利用垃圾发电厂在线运行数据作为输入参数,实现垃圾热值的在线预测.研究表明,该软测量模型具有实时性好、能够预测垃圾热值整体变化趋势等优点.该模型初步应用于某垃圾发电厂,结果表明,其具有较好的实时性与准确度,在垃圾燃烧过程自动控制系统中具有较好的应用前景.

Indirect Measurement Model for Waste Heating Value Based on Incineration Operational Parameters

In the refuse incineration process,the stability of incineration is always influenced strongly the heating value of refuse.However,it is difficult to get an on-line method of measuring refuse heating value in practice,due to the varying sources and unstable composition of refuse.A heating value prediction model of refuse based on cerebellar model articulation controller(CMAC) was built,in which the monitoring parameters of incinerator were used as inputs data.Compared with other models,the new model can predict the integrated variation of the waste heating value and realize the real-time measurement.With this model,the heating value of refuse was predicted successfully in a refuse power plant,indicating that it is a feasible way for the soft measurement process and the incineration control system.