What question did this study set out to answer?

To develop an accurate hybrid predictive model for non-stationary and nonlinear NOx emissions in coal-fired power generation units under load fluctuations.

August 1, 2026Journal of Engineering ResearchOpen Access

NOx emission prediction of boiler combustion system based on CEEMDAN-VMD-LSTM model

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To develop an accurate hybrid predictive model for non-stationary and nonlinear NOx emissions in coal-fired power generation units under load fluctuations.
Designed a secondary decomposition framework using CEEMDAN and adaptive variational mode decomposition parameterized by permutation entropy and modal decomposition energy ratio.
Applied sample entropy to aggregate components with similar complexity and eliminate model redundancy.
Trained a long short-term memory network to capture temporal patterns using operational data from a 660 MW coal-fired boiler.
The proposed hybrid framework achieved superior prediction performance relative to independent and conventional benchmark models.
Demonstrated prediction accuracy with a mean absolute percentage error of 0.48% and an R2 of 0.9981.

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Implication

Predictive modeling study demonstrates accurate boiler NOx emission forecasting in coal-fired power plants, suggesting improved real-time low-emission combustion control.

Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/6a76cdd6a0847c3df1ec3728 https://doi.org/10.1016/j.jer.2026.07.024

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Implication

Predictive modeling study demonstrates accurate boiler NOx emission forecasting in coal-fired power plants, suggesting improved real-time low-emission combustion control.

NOx emission prediction of boiler combustion system based on CEEMDAN-VMD-LSTM model

Key Points

Discussion

Implication

Cite This Study

NOx emission prediction of boiler combustion system based on CEEMDAN-VMD-LSTM model

Key Points

Discussion

Implication

Cite This Study

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