Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice

Qianchao Wang, Hongcan Xu, Lei Pan and Li Sun
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Qianchao Wang: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
Hongcan Xu: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
Lei Pan: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
Li Sun: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China

Sustainability, 2020, vol. 12, issue 10, 1-18

Abstract: Boiler forced draft systems play a critical role in maintaining power plant safety and efficiency. However, their control is notoriously intractable in terms of modelling difficulty, multiple disturbances and severe noise. To this end, this paper develops a data-driven paradigm by combining some popular data analytics methods in both modelling and control. First, singular value decomposition (SVD) is utilized for data classification, which further cooperates with back propagation (BP) neural network to de-noise the measurements. Second, prediction error method (PEM) is used to analyze the historical data and identify the dynamic model, whose responses agree well with the actual plant data. Third, by estimating the lumped disturbances via the real-time data, active disturbance rejection control (ADRC) is employed to control the forced draft system, whose stability is analyzed in the frequency domain. Simulation results demonstrate the efficiency and superiority of the proposed method over proportional-integral-differential (PID) controller and model predictive controller, depicting a promising prospect in the future industry practice.

Keywords: boiler forced draft system; singular value decomposition (SVD); back propagation neural network; prediction error method (PEM); active disturbance rejection control (ADRC); model predictive control (MPC) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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