Abstract
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LI Dong-xiong, Zhijin Cheng, Yanbing Song, Chengliang Liu, Hongliang Xiao, Shaoqing Wei
Abstract
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Heat transfer calculation methods in three-dimensional CFD model for pulverized coal-fired boilers[J/OL]
10.1016/j.applthermaleng.2019.114633 · 2020
Visualization of three-dimensional temperature distributions in a large-scale furnace via regularized reconstruction from radiative energy images: numerical studies[J/OL]
10.1016/s0022-4073(01)00130-3 · 2002
Numerical simulation of urea based selective non-catalytic reduction deNOx process for industrial applications[J/OL]
10.1016/j.enconman.2016.01.062 · 2016
Numerical simulation on NOX emissions in a municipal solid waste incinerator[J/OL]
10.1016/j.jclepro.2019.06.127 · 2019
Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction[J/OL]
10.1016/j.energy.2022.124552 · 2022
Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: plant data learned by deep neural network and least square support vector machine[J/OL]
10.1016/j.jclepro.2020.122310 · 2020
Prediction of SOx-NOx emission in coal-fired power plant using deep neural network[J/OL]
10.3390/machines11121042 · 2023
NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler[J/OL]
10.1016/j.jclepro.2023.136060 · 2023
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
Universality of deep convolutional neural networks[J/OL]
10.1016/j.acha.2019.06.004 · 2020
NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants[J/OL]
10.1016/j.energy.2024.132222 · 2024
Prediction of the NO emissions from thermal power plant using long-short term memory neural network[J/OL]
10.1016/j.energy.2019.116597 · 2020
Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network[J/OL]
10.1016/j.energy.2019.116482 · 2020
A hybrid NOx emission prediction model based on CEEMDAN and AM-LSTM[J/OL]
10.1016/j.fuel.2021.122486 · 2022
Temporal fusion transformers for interpretable multi-horizon time series forecasting[J/OL]
10.1016/j.ijforecast.2021.03.012 · 2021
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A feature optimized attention transformer with kinetic information capture and weighted robust Z-score for industrial NOx emission forecasting[J/OL]
10.1016/j.energy.2025.136276 · 2025
Transformer-based auto-encoder with combined multi-head-attention for industrial soft-sensor modeling[J/OL]
10.1016/j.engappai.2025.111681 · 2025
A remaining useful life prediction method for lithium-ion battery based on temporal transformer network[J/OL]
10.1016/j.procs.2022.12.383 · 2023
Dynamic prediction of NOx generation concentration based on kolmogorov–arnold network integrated deep learning method for a 660 MW coal-fired boiler[J/OL]
10.1016/j.energy.2025.139343 · 2025
Towards reliable and interpretable NOx emission prediction: a knowledge and data driven model for coal-fired boilers with spatiotemporal feature integration[J/OL]
10.1016/j.energy.2025.139572 · 2026
Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection[J/OL]
10.1016/j.enconman.2021.114367 · 2021
Mining data with random forests: a survey and results of new tests[J/OL]
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A novel gas turbine performance prediction model incorporating the residual connection and feature engineering methods[J/OL]
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Effects of reburning mechanically-activated micronized coal on reduction of NOx: computational study of a real-scale tangentially-fired boiler[J/OL]
10.1016/j.fuel.2017.10.132 · 2018
Two-dimensional computational fluid dynamics simulation of nitrogen and sulfur oxides emissions in a circulating fluidized bed combustor[J/OL]
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Combustion characteristics and NOx release of sludge combustion with coal in a 660 MW boiler[J/OL]
10.1016/j.applthermaleng.2024.124749 · 2025
Application of machine learning in optimizing proton exchange membrane fuel cells: a review[J/OL]
10.1016/j.egyai.2022.100170 · 2022
Improvement and optimization of coal dust concentration detection technology: based on the 3σ criterion and the kalman filtering composite algorithm[J/OL]
10.1016/j.flowmeasinst.2024.102598 · 2024
Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions[J/OL]
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STAnet: a spatiotemporal attention network for decoding auditory spatial attention from EEG[J/OL]
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Effect of secondary air on NO emission in a 440 t/h circulating fluidized bed boiler based on CPFD method[J/OL]
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Effect of secondary air on NO emission in a 440 t/h circulating fluidized bed boiler based on CPFD method[J/OL]
10.1016/j.partic.2023.02.002 · doi-reference
STAnet: a spatiotemporal attention network for decoding auditory spatial attention from EEG[J/OL]
10.1109/tbme.2022.3140246 · doi-reference
Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions[J/OL]
10.1016/j.energy.2021.120542 · doi-reference
Improvement and optimization of coal dust concentration detection technology: based on the 3σ criterion and the kalman filtering composite algorithm[J/OL]
10.1016/j.flowmeasinst.2024.102598 · doi-reference
Application of machine learning in optimizing proton exchange membrane fuel cells: a review[J/OL]
10.1016/j.egyai.2022.100170 · doi-reference
Combustion characteristics and NOx release of sludge combustion with coal in a 660 MW boiler[J/OL]
10.1016/j.applthermaleng.2024.124749 · doi-reference
Two-dimensional computational fluid dynamics simulation of nitrogen and sulfur oxides emissions in a circulating fluidized bed combustor[J/OL]
10.1016/j.cej.2011.07.083 · doi-reference
Effects of reburning mechanically-activated micronized coal on reduction of NOx: computational study of a real-scale tangentially-fired boiler[J/OL]
10.1016/j.fuel.2017.10.132 · doi-reference
A novel gas turbine performance prediction model incorporating the residual connection and feature engineering methods[J/OL]
10.1016/j.energy.2025.137251 · doi-reference
Mining data with random forests: a survey and results of new tests[J/OL]
10.1016/j.patcog.2010.08.011 · doi-reference
Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection[J/OL]
10.1016/j.enconman.2021.114367 · doi-reference
Towards reliable and interpretable NOx emission prediction: a knowledge and data driven model for coal-fired boilers with spatiotemporal feature integration[J/OL]
10.1016/j.energy.2025.139572 · doi-reference
Dynamic prediction of NOx generation concentration based on kolmogorov–arnold network integrated deep learning method for a 660 MW coal-fired boiler[J/OL]
10.1016/j.energy.2025.139343 · doi-reference
A remaining useful life prediction method for lithium-ion battery based on temporal transformer network[J/OL]
10.1016/j.procs.2022.12.383 · doi-reference
Transformer-based auto-encoder with combined multi-head-attention for industrial soft-sensor modeling[J/OL]
10.1016/j.engappai.2025.111681 · doi-reference
A feature optimized attention transformer with kinetic information capture and weighted robust Z-score for industrial NOx emission forecasting[J/OL]
10.1016/j.energy.2025.136276 · doi-reference
Temporal fusion transformers for interpretable multi-horizon time series forecasting[J/OL]
10.1016/j.ijforecast.2021.03.012 · doi-reference
A hybrid NOx emission prediction model based on CEEMDAN and AM-LSTM[J/OL]
10.1016/j.fuel.2021.122486 · doi-reference
Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network[J/OL]
10.1016/j.energy.2019.116482 · doi-reference
Prediction of the NO emissions from thermal power plant using long-short term memory neural network[J/OL]
10.1016/j.energy.2019.116597 · doi-reference
NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants[J/OL]
10.1016/j.energy.2024.132222 · doi-reference
Experimental trends of NO in circulating fluidized bed combustion[J/OL]
10.1016/j.fuel.2008.12.020 · doi-reference
Universality of deep convolutional neural networks[J/OL]
10.1016/j.acha.2019.06.004 · doi-reference
NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler[J/OL]
10.1016/j.jclepro.2023.136060 · doi-reference
Prediction of SOx-NOx emission in coal-fired power plant using deep neural network[J/OL]
10.3390/machines11121042 · doi-reference
Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: plant data learned by deep neural network and least square support vector machine[J/OL]
10.1016/j.jclepro.2020.122310 · doi-reference
Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction[J/OL]
10.1016/j.energy.2022.124552 · doi-reference
Numerical simulation on NOX emissions in a municipal solid waste incinerator[J/OL]
10.1016/j.jclepro.2019.06.127 · doi-reference
Numerical simulation of urea based selective non-catalytic reduction deNOx process for industrial applications[J/OL]
10.1016/j.enconman.2016.01.062 · doi-reference
Visualization of three-dimensional temperature distributions in a large-scale furnace via regularized reconstruction from radiative energy images: numerical studies[J/OL]
10.1016/s0022-4073(01)00130-3 · doi-reference
Heat transfer calculation methods in three-dimensional CFD model for pulverized coal-fired boilers[J/OL]
10.1016/j.applthermaleng.2019.114633 · doi-reference