Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method  

作　　者：YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi 

机构地区：[1]The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,School of Control and Computer Engineering,North China Electric Power University,Beijing,102206,China [2]CHN Energy New Energy Technology Research Institute Co.,Ltd.,Beijing,102209,China [3]Department of Energy and Power Engineering,Tsinghua University,Beijing,100084,China

出　　处：《Journal of Thermal Science》2024年第3期1216-1230,共15页热科学学报（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Number:62276096)。

摘　　要：To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)units.Therefore,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been proposed.Firstly,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB units.The model parameters are determined based on the operating data of the unit and continuously optimized in real time.Then,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam pressure.Finally,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB units.The model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关 键 词：circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant 

分 类 号：TK229.66[动力工程及工程热物理—动力机械及工程]