NO  emission prediction of coal-fired power units under uncertain classification of operating conditions

Wang, Jiangjiang; Feng, Yingsong; Ye, Shaoming; Ma, Zherui; Dong, Fuxiang

doi:10.1016/j.fuel.2023.127840

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…LSTM has demonstrated excellent performance in learning from historical data over extended periods, while CNN has been extensively researched for capturing spatial patterns and extracting hidden features. Bi‐directional LSTM (BiLSTM) has been utilized for NOx emission prediction based on classified working conditions 16 . LSTM was employed for modeling NOx emissions and demonstrated superior performance compared to LSSVM 17 .…”

Section: Introductionmentioning

confidence: 99%

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NOx emission reduction of coal‐fired power plants through data‐driven model and particle swarm optimization

Abebe,

Seo,

Kang

et al. 2024

Env Prog and Sustain Energy

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Several studies have aimed to predict and control carbon emissions from coal‐fired power plants. However, the highly complex combustion mechanisms in coal‐fired power plant boilers pose a significant challenge in direct modeling and optimization. To tackle this challenge, this study introduced a data‐driven approach along with model‐based process optimization to mitigate NOx emissions from coal‐fired power plants. The process involved collecting a 5‐month operational dataset containing 67 controllable parameters from a 500 MW coal‐fired power plant. Steady‐state data was isolated from the load output using a moving average method, followed by the application of an isolation forest algorithm to detect and remove anomalies. Correlation analysis was then used to evaluate parameter relationships and eliminate redundant ones. Subsequently, a NOx prediction model was developed, combining an extra tree regressor data‐driven prediction model with particle swarm optimization to optimize the most influential controllable parameters for reducing NOx emissions. Testing the proposed model across four different target loads consistently resulted in a reduction of over 20% in NOx emissions by optimizing boiler combustion parameters, representing a significant achievement in optimizing coal‐fired combustion.

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Section: Introductionmentioning

confidence: 99%

“…Bi-directional LSTM (BiLSTM) has been utilized for NOx emission prediction based on classified working conditions. 16 LSTM was employed for modeling NOx emissions and demonstrated superior performance compared to LSSVM. 17 LSTM has also been enhanced through feature quantity weight analysis based on the Relief algorithm.…”

mentioning

confidence: 99%

NOx emission reduction of coal‐fired power plants through data‐driven model and particle swarm optimization

Abebe,

Seo,

Kang

et al. 2024

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…It has outstanding advantages over narrow neural networks in time-series feature extraction and regression prediction. [20] Some scholars have used deep learning algorithms to study emission predictions. For example, Song et al [21] proposed an improved MI feature selection algorithm and predicted the NOx concentration from a 300 MW cogeneration boiler using a short-and long-term memory neural networks (LSTM) model.…”

Section: Introductionmentioning

confidence: 99%

Modelling nitrogen oxide emission trends from the municipal solid waste incineration process using an adaptive bi‐directional long and short‐term memory network

Li,

Yao,

Chen

et al. 2023

Can J Chem Eng

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Accurately predicting trends in NOx emission is essential for effectively controlling pollution in municipal solid waste incineration (MSWI) power plants. However, the MSWI process exhibits notable dynamic nonlinearity, time series characteristics, and fluctuations that are distinct from those present in fossil fuel combustion processes. Therefore, the model must possess excellent capabilities in handling time series and nonlinear features while achieving adaptive updates to account for complex working conditions. To address these issues, we have developed a robust prediction model for NOx emission trends using the bi‐directional long short‐term memory (Bi‐LSTM) deep learning algorithm. This model encompasses maximum information coefficient and expert experience for input variables selection, parameter optimization using the linear inertial weight particle swarm algorithm (LDWPSO), and an adaptive update strategy based on probabilistic statistics. The prediction performance of this model was compared to that of the traditional and widely used backpropagation neural network (BPNN), extreme learning machine (ELM), and LSTM. Furthermore, we verified the adaptive update effect of the proposed model using additional data. The results demonstrate that the proposed model exhibits robust prediction and adaptive capabilities. This study's originality is presenting a satisfactory trend prediction for NOx emission from the MSWI process using an adaptive LDWPSO‐(Bi‐LSTM) model. It will be essential for the optimization and control of NOx emissions from the MSWI process.

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Multi-parameter co-optimization for NOx emissions control from waste incinerators based on data-driven model and improved particle swarm optimization

Li,

Yao,

Chen

et al. 2024

Energy

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NO emission prediction of coal-fired power units under uncertain classification of operating conditions

Cited by 7 publications

References 35 publications

NOx emission reduction of coal‐fired power plants through data‐driven model and particle swarm optimization

NOx emission reduction of coal‐fired power plants through data‐driven model and particle swarm optimization

Modelling nitrogen oxide emission trends from the municipal solid waste incineration process using an adaptive bi‐directional long and short‐term memory network

Multi-parameter co-optimization for NOx emissions control from waste incinerators based on data-driven model and improved particle swarm optimization

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