An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation
Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The...
Ausführliche Beschreibung
Autor*in: |
Binbin Chen [verfasserIn] Panling Huang [verfasserIn] Jun Zhou [verfasserIn] Mindong Li [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Processes - MDPI AG, 2013, 10(2022), 725, p 725 |
---|---|
Übergeordnetes Werk: |
volume:10 ; year:2022 ; number:725, p 725 |
Links: |
---|
DOI / URN: |
10.3390/pr10040725 |
---|
Katalog-ID: |
DOAJ079295614 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ079295614 | ||
003 | DE-627 | ||
005 | 20230503061246.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230307s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/pr10040725 |2 doi | |
035 | |a (DE-627)DOAJ079295614 | ||
035 | |a (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TP1-1185 | |
050 | 0 | |a QD1-999 | |
100 | 0 | |a Binbin Chen |e verfasserin |4 aut | |
245 | 1 | 3 | |a An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. | ||
650 | 4 | |a stacking ensemble method | |
650 | 4 | |a granule moisture prediction | |
650 | 4 | |a fluidized bed granulation | |
650 | 4 | |a process parameters | |
650 | 4 | |a feature construction | |
650 | 4 | |a SHapley Additive exPlanations (SHAP) | |
653 | 0 | |a Chemical technology | |
653 | 0 | |a Chemistry | |
700 | 0 | |a Panling Huang |e verfasserin |4 aut | |
700 | 0 | |a Jun Zhou |e verfasserin |4 aut | |
700 | 0 | |a Mindong Li |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Processes |d MDPI AG, 2013 |g 10(2022), 725, p 725 |w (DE-627)750371439 |w (DE-600)2720994-5 |x 22279717 |7 nnns |
773 | 1 | 8 | |g volume:10 |g year:2022 |g number:725, p 725 |
856 | 4 | 0 | |u https://doi.org/10.3390/pr10040725 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2227-9717/10/4/725 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2227-9717 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 10 |j 2022 |e 725, p 725 |
author_variant |
b c bc p h ph j z jz m l ml |
---|---|
matchkey_str |
article:22279717:2022----::nnacdtcignebeehdogaueosuerdcinn |
hierarchy_sort_str |
2022 |
callnumber-subject-code |
TP |
publishDate |
2022 |
allfields |
10.3390/pr10040725 doi (DE-627)DOAJ079295614 (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Binbin Chen verfasserin aut An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry Panling Huang verfasserin aut Jun Zhou verfasserin aut Mindong Li verfasserin aut In Processes MDPI AG, 2013 10(2022), 725, p 725 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:10 year:2022 number:725, p 725 https://doi.org/10.3390/pr10040725 kostenfrei https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 kostenfrei https://www.mdpi.com/2227-9717/10/4/725 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 725, p 725 |
spelling |
10.3390/pr10040725 doi (DE-627)DOAJ079295614 (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Binbin Chen verfasserin aut An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry Panling Huang verfasserin aut Jun Zhou verfasserin aut Mindong Li verfasserin aut In Processes MDPI AG, 2013 10(2022), 725, p 725 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:10 year:2022 number:725, p 725 https://doi.org/10.3390/pr10040725 kostenfrei https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 kostenfrei https://www.mdpi.com/2227-9717/10/4/725 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 725, p 725 |
allfields_unstemmed |
10.3390/pr10040725 doi (DE-627)DOAJ079295614 (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Binbin Chen verfasserin aut An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry Panling Huang verfasserin aut Jun Zhou verfasserin aut Mindong Li verfasserin aut In Processes MDPI AG, 2013 10(2022), 725, p 725 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:10 year:2022 number:725, p 725 https://doi.org/10.3390/pr10040725 kostenfrei https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 kostenfrei https://www.mdpi.com/2227-9717/10/4/725 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 725, p 725 |
allfieldsGer |
10.3390/pr10040725 doi (DE-627)DOAJ079295614 (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Binbin Chen verfasserin aut An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry Panling Huang verfasserin aut Jun Zhou verfasserin aut Mindong Li verfasserin aut In Processes MDPI AG, 2013 10(2022), 725, p 725 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:10 year:2022 number:725, p 725 https://doi.org/10.3390/pr10040725 kostenfrei https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 kostenfrei https://www.mdpi.com/2227-9717/10/4/725 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 725, p 725 |
allfieldsSound |
10.3390/pr10040725 doi (DE-627)DOAJ079295614 (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Binbin Chen verfasserin aut An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry Panling Huang verfasserin aut Jun Zhou verfasserin aut Mindong Li verfasserin aut In Processes MDPI AG, 2013 10(2022), 725, p 725 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:10 year:2022 number:725, p 725 https://doi.org/10.3390/pr10040725 kostenfrei https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 kostenfrei https://www.mdpi.com/2227-9717/10/4/725 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 725, p 725 |
language |
English |
source |
In Processes 10(2022), 725, p 725 volume:10 year:2022 number:725, p 725 |
sourceStr |
In Processes 10(2022), 725, p 725 volume:10 year:2022 number:725, p 725 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry |
isfreeaccess_bool |
true |
container_title |
Processes |
authorswithroles_txt_mv |
Binbin Chen @@aut@@ Panling Huang @@aut@@ Jun Zhou @@aut@@ Mindong Li @@aut@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
750371439 |
id |
DOAJ079295614 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ079295614</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503061246.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230307s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/pr10040725</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ079295614</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP1-1185</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Binbin Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stacking ensemble method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">granule moisture prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fluidized bed granulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">process parameters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature construction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SHapley Additive exPlanations (SHAP)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemical technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemistry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Panling Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jun Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mindong Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Processes</subfield><subfield code="d">MDPI AG, 2013</subfield><subfield code="g">10(2022), 725, p 725</subfield><subfield code="w">(DE-627)750371439</subfield><subfield code="w">(DE-600)2720994-5</subfield><subfield code="x">22279717</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:725, p 725</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/pr10040725</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2227-9717/10/4/725</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2227-9717</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="e">725, p 725</subfield></datafield></record></collection>
|
callnumber-first |
T - Technology |
author |
Binbin Chen |
spellingShingle |
Binbin Chen misc TP1-1185 misc QD1-999 misc stacking ensemble method misc granule moisture prediction misc fluidized bed granulation misc process parameters misc feature construction misc SHapley Additive exPlanations (SHAP) misc Chemical technology misc Chemistry An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation |
authorStr |
Binbin Chen |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)750371439 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TP1-1185 |
illustrated |
Not Illustrated |
issn |
22279717 |
topic_title |
TP1-1185 QD1-999 An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) |
topic |
misc TP1-1185 misc QD1-999 misc stacking ensemble method misc granule moisture prediction misc fluidized bed granulation misc process parameters misc feature construction misc SHapley Additive exPlanations (SHAP) misc Chemical technology misc Chemistry |
topic_unstemmed |
misc TP1-1185 misc QD1-999 misc stacking ensemble method misc granule moisture prediction misc fluidized bed granulation misc process parameters misc feature construction misc SHapley Additive exPlanations (SHAP) misc Chemical technology misc Chemistry |
topic_browse |
misc TP1-1185 misc QD1-999 misc stacking ensemble method misc granule moisture prediction misc fluidized bed granulation misc process parameters misc feature construction misc SHapley Additive exPlanations (SHAP) misc Chemical technology misc Chemistry |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Processes |
hierarchy_parent_id |
750371439 |
hierarchy_top_title |
Processes |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)750371439 (DE-600)2720994-5 |
title |
An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation |
ctrlnum |
(DE-627)DOAJ079295614 (DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79 |
title_full |
An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation |
author_sort |
Binbin Chen |
journal |
Processes |
journalStr |
Processes |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
author_browse |
Binbin Chen Panling Huang Jun Zhou Mindong Li |
container_volume |
10 |
class |
TP1-1185 QD1-999 |
format_se |
Elektronische Aufsätze |
author-letter |
Binbin Chen |
doi_str_mv |
10.3390/pr10040725 |
author2-role |
verfasserin |
title_sort |
enhanced stacking ensemble method for granule moisture prediction in fluidized bed granulation |
callnumber |
TP1-1185 |
title_auth |
An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation |
abstract |
Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. |
abstractGer |
Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. |
abstract_unstemmed |
Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
725, p 725 |
title_short |
An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation |
url |
https://doi.org/10.3390/pr10040725 https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79 https://www.mdpi.com/2227-9717/10/4/725 https://doaj.org/toc/2227-9717 |
remote_bool |
true |
author2 |
Panling Huang Jun Zhou Mindong Li |
author2Str |
Panling Huang Jun Zhou Mindong Li |
ppnlink |
750371439 |
callnumber-subject |
TP - Chemical Technology |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/pr10040725 |
callnumber-a |
TP1-1185 |
up_date |
2024-07-03T22:44:54.521Z |
_version_ |
1803599682756149248 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ079295614</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503061246.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230307s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/pr10040725</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ079295614</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf5c5e6093a4e4f1bafc63f87f7b3fe79</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP1-1185</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Binbin Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters <i<MAE</i<, <i<MAPE</i<, <i<RMSE</i<, and <i<Adj. R</i<<sup<2</sup< were 0.0596, 1.5819, 0.0844, and 0.99485, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stacking ensemble method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">granule moisture prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fluidized bed granulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">process parameters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">feature construction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SHapley Additive exPlanations (SHAP)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemical technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemistry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Panling Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jun Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mindong Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Processes</subfield><subfield code="d">MDPI AG, 2013</subfield><subfield code="g">10(2022), 725, p 725</subfield><subfield code="w">(DE-627)750371439</subfield><subfield code="w">(DE-600)2720994-5</subfield><subfield code="x">22279717</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:725, p 725</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/pr10040725</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/f5c5e6093a4e4f1bafc63f87f7b3fe79</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2227-9717/10/4/725</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2227-9717</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="e">725, p 725</subfield></datafield></record></collection>
|
score |
7.399766 |