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...
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Autor*in:	Binbin Chen [verfasserIn] Panling Huang [verfasserIn] Jun Zhou [verfasserIn] Mindong Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP)

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 10(2022), 725, p 725
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:725, p 725

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr10040725

Katalog-ID:	DOAJ079295614

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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.
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language	English
source	In Processes 10(2022), 725, p 725 volume:10 year:2022 number:725, p 725
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topic_facet	stacking ensemble method granule moisture prediction fluidized bed granulation process parameters feature construction SHapley Additive exPlanations (SHAP) Chemical technology Chemistry
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container_title	Processes
authorswithroles_txt_mv	Binbin Chen @@aut@@ Panling Huang @@aut@@ Jun Zhou @@aut@@ Mindong Li @@aut@@
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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
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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
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title	An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation
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title_full	An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation
author_sort	Binbin Chen
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author_browse	Binbin Chen Panling Huang Jun Zhou Mindong Li
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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
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author2	Panling Huang Jun Zhou Mindong Li
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doi_str	10.3390/pr10040725
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up_date	2024-07-03T22:44:54.521Z
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An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation

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