Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes
The development of post-processing technology for spent nuclear fuel is essential to ensuring the sustainable growth of nuclear energy. However, post-processing facilities release copious amounts of emissions with high concentrations of nitrogen oxides (NOx), making the accurate measurement of their...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-06-01
|
| Series: | International Journal of Advanced Nuclear Reactor Design and Technology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468605024000413 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850252428036800512 |
|---|---|
| author | Jiaxin Liu Shuo Yang Qichao Li Leiming Ji Xuefeng Hou Liudong Hou Jing Ma |
| author_facet | Jiaxin Liu Shuo Yang Qichao Li Leiming Ji Xuefeng Hou Liudong Hou Jing Ma |
| author_sort | Jiaxin Liu |
| collection | DOAJ |
| description | The development of post-processing technology for spent nuclear fuel is essential to ensuring the sustainable growth of nuclear energy. However, post-processing facilities release copious amounts of emissions with high concentrations of nitrogen oxides (NOx), making the accurate measurement of their concentrations in radioactive settings greatly challenging. The application of machine learning strategies to predict NOx emissions offers a promising approach for improving the measurement and management of NOx in post-processing facilities, owing to their potential for cost reduction and operational expediency compared to conventional methods. Therefore, this study presents the outcomes of predictive activities for NOx emissions using machine learning. We employed a vector autoregression (VAR) model that considers the influence of other pollutants on NOx emissions. The results confirm that the VAR model sufficiently predicts NOx emissions. Furthermore, this study reveals the intricate interplay and feedback loops among various pollutants, thereby providing guidance for formulating comprehensive pollution control strategies. Finally, a lightweight and precise NOx forecasting model was developed by extracting the primary features affecting NOx predictions. This model has substantial significance for elevating the precision of pollutant emission forecasts and offers substantive support for the development and sustainable growth of the nuclear chemical industry. |
| format | Article |
| id | doaj-art-c7fe0cdaaf1f408d81600dddb5f2eec5 |
| institution | OA Journals |
| issn | 2468-6050 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Advanced Nuclear Reactor Design and Technology |
| spelling | doaj-art-c7fe0cdaaf1f408d81600dddb5f2eec52025-08-20T01:57:39ZengKeAi Communications Co., Ltd.International Journal of Advanced Nuclear Reactor Design and Technology2468-60502024-06-016211712210.1016/j.jandt.2024.12.002Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributesJiaxin Liu0Shuo Yang1Qichao Li2Leiming Ji3Xuefeng Hou4Liudong Hou5Jing Ma6Corresponding author.; China Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaChina Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaChina Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaChina Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaChina Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaCorresponding author.; China Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaCorresponding author.; China Nuclear Power Engineering Co., Ltd., Beijing, 100142, PR ChinaThe development of post-processing technology for spent nuclear fuel is essential to ensuring the sustainable growth of nuclear energy. However, post-processing facilities release copious amounts of emissions with high concentrations of nitrogen oxides (NOx), making the accurate measurement of their concentrations in radioactive settings greatly challenging. The application of machine learning strategies to predict NOx emissions offers a promising approach for improving the measurement and management of NOx in post-processing facilities, owing to their potential for cost reduction and operational expediency compared to conventional methods. Therefore, this study presents the outcomes of predictive activities for NOx emissions using machine learning. We employed a vector autoregression (VAR) model that considers the influence of other pollutants on NOx emissions. The results confirm that the VAR model sufficiently predicts NOx emissions. Furthermore, this study reveals the intricate interplay and feedback loops among various pollutants, thereby providing guidance for formulating comprehensive pollution control strategies. Finally, a lightweight and precise NOx forecasting model was developed by extracting the primary features affecting NOx predictions. This model has substantial significance for elevating the precision of pollutant emission forecasts and offers substantive support for the development and sustainable growth of the nuclear chemical industry.http://www.sciencedirect.com/science/article/pii/S2468605024000413 |
| spellingShingle | Jiaxin Liu Shuo Yang Qichao Li Leiming Ji Xuefeng Hou Liudong Hou Jing Ma Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes International Journal of Advanced Nuclear Reactor Design and Technology |
| title | Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes |
| title_full | Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes |
| title_fullStr | Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes |
| title_full_unstemmed | Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes |
| title_short | Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes |
| title_sort | machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes |
| url | http://www.sciencedirect.com/science/article/pii/S2468605024000413 |
| work_keys_str_mv | AT jiaxinliu machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes AT shuoyang machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes AT qichaoli machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes AT leimingji machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes AT xuefenghou machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes AT liudonghou machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes AT jingma machinelearningforforecastingfactoryconcentrationsofnitrogenoxidesfromunivariatedataexploitingtrendattributes |