Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest
During the spent fuel reprocessing process, nitrogen oxides (NOx) gases are generated. The treatment and emission control of NOx rely on measurements from instrumentation. In situations where operating conditions fluctuate, the response capability of the treatment system exhibits a lag, resulting in...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-06-01
|
| Series: | International Journal of Advanced Nuclear Reactor Design and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468605025000432 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849424037958123520 |
|---|---|
| author | Xuankun Wei Yan Xu Xiaomeng Li Gengxin Fan Xuening Cheng Tiantian Yu Baihua Jiang |
| author_facet | Xuankun Wei Yan Xu Xiaomeng Li Gengxin Fan Xuening Cheng Tiantian Yu Baihua Jiang |
| author_sort | Xuankun Wei |
| collection | DOAJ |
| description | During the spent fuel reprocessing process, nitrogen oxides (NOx) gases are generated. The treatment and emission control of NOx rely on measurements from instrumentation. In situations where operating conditions fluctuate, the response capability of the treatment system exhibits a lag, resulting in a rapid short-term increase in NOx concentration during final emissions. To predict the trend of NOx concentration changes in the reprocessing process and enhance the response capability of the NOx treatment system, a NOx concentration prediction model was developed using the Random Forest algorithm, based on data collected from actual operations. Feature engineering was employed to select variables, further improving the model's R2 to 0.92. The results indicate that the Random Forest model demonstrates excellent predictive performance for NOx concentration data and outperforms traditional machine learning models. |
| format | Article |
| id | doaj-art-9d11a591b3544d2db950ed23ac1a285c |
| institution | Kabale University |
| issn | 2468-6050 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Advanced Nuclear Reactor Design and Technology |
| spelling | doaj-art-9d11a591b3544d2db950ed23ac1a285c2025-08-20T03:30:20ZengKeAi Communications Co., Ltd.International Journal of Advanced Nuclear Reactor Design and Technology2468-60502025-06-0172636910.1016/j.jandt.2025.04.011Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forestXuankun Wei0Yan Xu1Xiaomeng Li2Gengxin Fan3Xuening Cheng4Tiantian Yu5Baihua Jiang6China Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaChina Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaChina Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaChina Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaChina Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaChina Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaCorresponding author.; China Nuclear Power Engineering Co., Ltd, Beijing, 100840, ChinaDuring the spent fuel reprocessing process, nitrogen oxides (NOx) gases are generated. The treatment and emission control of NOx rely on measurements from instrumentation. In situations where operating conditions fluctuate, the response capability of the treatment system exhibits a lag, resulting in a rapid short-term increase in NOx concentration during final emissions. To predict the trend of NOx concentration changes in the reprocessing process and enhance the response capability of the NOx treatment system, a NOx concentration prediction model was developed using the Random Forest algorithm, based on data collected from actual operations. Feature engineering was employed to select variables, further improving the model's R2 to 0.92. The results indicate that the Random Forest model demonstrates excellent predictive performance for NOx concentration data and outperforms traditional machine learning models.http://www.sciencedirect.com/science/article/pii/S2468605025000432Spent fuel reprocessingMachine learningRandom forest |
| spellingShingle | Xuankun Wei Yan Xu Xiaomeng Li Gengxin Fan Xuening Cheng Tiantian Yu Baihua Jiang Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest International Journal of Advanced Nuclear Reactor Design and Technology Spent fuel reprocessing Machine learning Random forest |
| title | Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest |
| title_full | Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest |
| title_fullStr | Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest |
| title_full_unstemmed | Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest |
| title_short | Study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest |
| title_sort | study on prediction model of nitrogen oxide concentration in reprocessing plant based on random forest |
| topic | Spent fuel reprocessing Machine learning Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2468605025000432 |
| work_keys_str_mv | AT xuankunwei studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest AT yanxu studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest AT xiaomengli studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest AT gengxinfan studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest AT xueningcheng studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest AT tiantianyu studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest AT baihuajiang studyonpredictionmodelofnitrogenoxideconcentrationinreprocessingplantbasedonrandomforest |