Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

被引:10
|
作者
Liang, Xiufan [1 ]
Li, Yiguo [1 ]
Wu, Xiao [1 ]
Shen, Jiong [1 ]
机构
[1] Southeast Univ, Key Lab Energy Thermal Convers & Control, Minist Educ, Nanjing 210096, Jiangsu, Peoples R China
来源
ENERGIES | 2018年 / 11卷 / 03期
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
pulverizing system; soft sensor; inferential control; moving horizon estimation; multi-model predictive control; STATE ESTIMATION; SOFT SENSOR; MILL MODEL; IDENTIFICATION; ALGORITHM;
D O I
10.3390/en11030589
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.
引用
收藏
页数:27
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