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Circulating Fluidized Bed Coal-Saving
Optimization Control Method
Tengfei Jiang 1 , Debin Yin 2 , Dewei Li 1 , ,YugengXi 1 ,andWuZhou 1
1 Department of automation, Shanghai Jiao Tong University,
Key Laboratory of System Control and Information Processing,
Ministry of Education, Shanghai, 200240
dwli@sjtu.edu.cn
2 Shanghai Xinhua Control Technology (Group) CO., LTD, Shanghai 200241
Abstract. The circulating fluidized bed boiler is widely used in thermal
power plants. With the proposal of energy-saving emission reduction,
how to reduce coal consumption while ensure the output steam quality
at the same time has become an important topic. This paper combines
the technology of RTO (real-time optimization) and zone control in DMC
(dynamic matrix control) to achieve this goal. The proposed method adds
the coal consumption into the objective function of DMC controller and
the operation point of the boiler is permitted to change within a zone
which can be set according to the actual requirements of the circulating
fluidized bed boiler. The zone control in DMC provides the freedom to
reduce the coal consumption and achieves the economic optimal target.
Compared to the simple use of constrained DMC control, the proposed
method is verified to be remarkable coal-saving by the case study of a
150 t/h boiler of a power plant in Sichuan.
Keywords: Circulating fluidized bed boiler, Dynamic Matrix Control,
Real Time Optimization, Zone Control, Coal-saving Control.
1 Introduction
Compared with the conventional boiler, the circulating fluidized bed (CFB)
boiler can be universal to bituminous coal, lignite, anthracite, coal gangue and
other fuels. Meanwhile, the unique hydrodynamic properties and structure of
CFB boilers improve the combustion eciency. These advantages make the CFB
boiler widely[1].
The CFB boiler is a MIMO thermodynamic system with large inertia and
large time delay, strong coupling and nonlinear characteristics. Hence, it is di-
cult to design the automatic control system for the CFB boiler. [2] studied the
This work is supported in part by the National Science Foundation of China (Grant
No. 60934007, 61074060), State Key Laboratory of Synthetical Automation for Pro-
cess Industries, and Innovation Program of Shanghai Municipal Education Com-
mission (Grant No. 11CXY08), Shanghai Jiao Tong University graduate innovative
ability special fund(Grant No. Z-030-008).
 
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