Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal co...

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Main Authors: Zhenhao Tang, Xiaoyan Wu, Shengxian Cao
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/12/9/1738
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spelling doaj-b5bc3a4c94334593a778c6d7ab8ceb132020-11-24T22:11:29ZengMDPI AGEnergies1996-10732019-05-01129173810.3390/en12091738en12091738Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load ConstraintsZhenhao Tang0Xiaoyan Wu1Shengxian Cao2School of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaA data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.https://www.mdpi.com/1996-1073/12/9/1738Combustion efficiencyNOx emissions constraintsboiler load constraintsleast square support vector machinedifferential evolution algorithmmodel predictive control
collection DOAJ
language English
format Article
sources DOAJ
author Zhenhao Tang
Xiaoyan Wu
Shengxian Cao
spellingShingle Zhenhao Tang
Xiaoyan Wu
Shengxian Cao
Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
Energies
Combustion efficiency
NOx emissions constraints
boiler load constraints
least square support vector machine
differential evolution algorithm
model predictive control
author_facet Zhenhao Tang
Xiaoyan Wu
Shengxian Cao
author_sort Zhenhao Tang
title Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
title_short Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
title_full Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
title_fullStr Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
title_full_unstemmed Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints
title_sort adaptive nonlinear model predictive control of the combustion efficiency under the nox emissions and load constraints
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2019-05-01
description A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.
topic Combustion efficiency
NOx emissions constraints
boiler load constraints
least square support vector machine
differential evolution algorithm
model predictive control
url https://www.mdpi.com/1996-1073/12/9/1738
work_keys_str_mv AT zhenhaotang adaptivenonlinearmodelpredictivecontrolofthecombustionefficiencyunderthenoxemissionsandloadconstraints
AT xiaoyanwu adaptivenonlinearmodelpredictivecontrolofthecombustionefficiencyunderthenoxemissionsandloadconstraints
AT shengxiancao adaptivenonlinearmodelpredictivecontrolofthecombustionefficiencyunderthenoxemissionsandloadconstraints
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