Comparative study of estimation methods of NOx emission with selection of input parameters for a coal-fired boiler

Kim, Beom Seok; Kim, Tae Young; Park, Tae Chang; Yeo, Yeong Koo

doi:10.1007/s11814-018-0087-8

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…In this section, the approach DDMMF is proposed as the predictive model in this paper, the prediction performance of the proposed model is compared with the ARIMA model in Reference [5], the PLS model in Reference [7], and the MLP model in Reference [10]. In the first case, the predicted results of the proposed model for operating condition 1 are compared with the corresponding ones obtained from the MLP, ARIMA and PLS models.…”

Section: Experimental Results Analysismentioning

confidence: 99%

“…Accordingly, employing these models in modern control methods is difficult. Several statistics-based models have been proposed for boiler combustion efficiency prediction in the literature [5][6][7]. In Reference [5], an autoregressive integrated moving average (ARIMA) model has been proposed, based on simulation data to predict the boiler combustion efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…The thermodynamic principles have been employed to develop a dynamic model for an ultra-supercritical coal-fired once-through boiler-turbine unit [6]. In Reference [7], the prediction accuracy of the boiler combustion efficiency has been verified through a partial least squares (PLS) model. Despite their simplicity and usefulness, these models are too sensitive to measurement errors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Tang

Cao

2019

Energies

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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.

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Section: Experimental Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tang

Cao

2019

Energies

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“…Boilers are the key equipment for energy conversion. The energy consumption management of a power plant mainly focuses on the modeling of a boiler combustion system based on big data, (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) the optimization of a combustion coal mixture strategy, (14)(15)(16) and boiler unit equipment improvement. (17) References 1-5 indicate the use of the neural network method to model the key parameters of boiler combustion optimization and the selection of input parameters that is only based on manual operation experience.…”

Section: Introductionmentioning

confidence: 99%

“…(17) References 1-5 indicate the use of the neural network method to model the key parameters of boiler combustion optimization and the selection of input parameters that is only based on manual operation experience. Kim et al (6) studied the estimation of NO X emission from coal-fired boilers of 500 MW units and the selection of input parameters. On the basis of auto regressive moving average (ARMA), artificial neural network (ANN), and least squares support vector machine (LSSVM) modeling methods, the appropriate input parameters of the NO X emission model were selected by a sensitivity analysis method.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy of Captive Power Plant Based on Hybrid Method

Liu¹,

Huang²,

Liu³

et al. 2019

Sensors and Materials

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Compared with a regular power plant, the load and fuel composition of a captive power plant vary greatly, resulting in complex and variable working conditions, and for specific operating conditions, there is a lack of data. Considering the irregularity and incompleteness of the data, a data-mining-based boiler energy management analysis method was proposed in this paper. This study adopted the random forest and recursive feature elimination (RF-RFE) method to select the features of distributed control system (DCS) monitoring data. According to different load intervals, some data with better running conditions were selected for clustering. According to the clustering center, adjustable parameters were optimized, and the obtained optimization results were input to the support vector machine model for energy consumption comparison. The results show that the method used in this study can reduce the unit energy consumption by 3-5% by adjusting the controllable parameters.

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Convolutional neural network approach for reduction of nitrogen oxides emissions from pulverized coal-fired boiler in a power plant for sustainable environment

Saif-ul-Allah

Khan

Ahmed

et al. 2023

Computers & Chemical Engineering

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Comparative study of estimation methods of NOx emission with selection of input parameters for a coal-fired boiler

Cited by 8 publications

References 18 publications

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

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

Research on Energy Management Strategy of Captive Power Plant Based on Hybrid Method

Convolutional neural network approach for reduction of nitrogen oxides emissions from pulverized coal-fired boiler in a power plant for sustainable environment

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