摘要
垃圾焚烧发电厂余热锅炉主蒸汽参数对焚烧厂的经济效益、运营稳定性和安全性起着至关重要的作用。焚烧炉内的燃烧过程是一个多输入多输出非线性的物理化学过程,必须考虑各因素间的影响。本文从焚烧系统的运行数据中选取合适的建模变量,建立多个输入多个输出的LSTM神经网络模型,训练并测试模型的预测准确性。该模型可根据实时数据动态更新和优化,对主蒸汽参数进行准确预测,为现场人员调整控制策略提供理论指导,从而提高生产运行的稳定性和经济性。
The main steam parameters of waste heat boiler in waste incineration power plant play an important role in the economic benefits,operation stability and safety of the incineration plant.The combustion process in incinerator is a multiple inputs and multiple outputs nonlinear physical and chemical process,and the influence of various factors must be considered.In this paper,the appropriate modeling variables are selected from the operation data of the incineration system,and a LSTM neural network model with multiple inputs and multiple outputs is established to train and test the prediction accuracy of the model.The model can be dynamically updated and optimized according to real-time data.It can accurately predict the main steam parameters and provide theoretical guidance for field personnel to adjust the control strategy,so as to improve the stability and economy of production and operation.
作者
杨培培
骆嘉辉
姚心
张瑛华
刘海威
YANG Pei-pei;LUO Jia-hui;YAO Xin;ZHANG Ying-hua;LIU Hai-wei
出处
《有色设备》
2021年第1期15-19,共5页
Nonferrous Metallurgical Equipment
关键词
垃圾焚烧
神经网络
主蒸汽参数
waste incineration
neural network
main steam parameters
