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Environ Technol. 2018 May 8:1-10. doi: 10.1080/09593330.2018.1470678. [Epub ahead of print]

Process identification of the SCR system of coal-fired power plant for de-NOx based on historical operation data.

Li J1, Shi R1,2, Xu C1, Wang S1.

Author information

1
a Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University , Nanjing , People's Republic of China.
2
b China Ship Development and Design Center , Wuhan , People's Republic of China.

Abstract

The selective catalytic reduction (SCR) system, as one principal flue gas treatment method employed for the NOx emission control of the coal-fired power plant, is nonlinear and time-varying with great inertia and large time delay. It is difficult for the present SCR control system to achieve satisfactory performance with the traditional feedback and feedforward control strategies. Although some improved control strategies, such as the Smith predictor control and the model predictive control, have been proposed for this issue, a well-matched identification model is essentially required to realize a superior control of the SCR system. Industrial field experiment is an alternative way to identify the SCR system model in the coal-fired power plant. But it undesirably disturbs the operation system and is costly in time and manpower. In this paper, a process identification model of the SCR system is proposed and developed by applying the asymptotic method to the sufficiently excited data, selected from the original historical operation database of a 350 MW coal-fired power plant according to the condition number of the Fisher information matrix. Numerical simulations are carried out based on the practical historical operation data to evaluate the performance of the proposed model. Results show that the proposed model can efficiently achieve the process identification of the SCR system.

KEYWORDS:

Fisher information matrix; Process identification; SCR system; asymptotic method; historical operation data

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