Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids
The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) d...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/4107429 |
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| author | Ya-Qing Gu Tao Shu Bin Ge Ping Wang Chen Gao Hamid Heydari |
| author_facet | Ya-Qing Gu Tao Shu Bin Ge Ping Wang Chen Gao Hamid Heydari |
| author_sort | Ya-Qing Gu |
| collection | DOAJ |
| description | The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process. |
| format | Article |
| id | doaj-art-39bd6f689600431fbd2e2064949a9c47 |
| institution | Kabale University |
| issn | 1687-806X 1687-8078 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-39bd6f689600431fbd2e2064949a9c472025-02-03T01:25:48ZengWileyInternational Journal of Chemical Engineering1687-806X1687-80782021-01-01202110.1155/2021/41074294107429Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic LiquidsYa-Qing Gu0Tao Shu1Bin Ge2Ping Wang3Chen Gao4Hamid Heydari5College of Electrical Engineering, Shandong Huayu University of Technology, Dezhou, Shandong 253034, ChinaModern Information Technology Center, Sichuan Vocational and Technical College, Suining, Sichuan 629000, ChinaInstitute of Information Engineering, Shandong Huayu University of Technology, Dezhou, Shandong 253034, ChinaCollege of Electrical Engineering, Shandong Huayu University of Technology, Dezhou, Shandong 253034, ChinaExperiment Management Center, Dezhou University, Dezhou, Shandong 253033, ChinaInstitute of Petroleum Engineering, University of Tehran, Tehran, IranThe use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process.http://dx.doi.org/10.1155/2021/4107429 |
| spellingShingle | Ya-Qing Gu Tao Shu Bin Ge Ping Wang Chen Gao Hamid Heydari Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids International Journal of Chemical Engineering |
| title | Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids |
| title_full | Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids |
| title_fullStr | Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids |
| title_full_unstemmed | Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids |
| title_short | Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids |
| title_sort | using an sgb decision tree approach to estimate the properties of crm made by biomass pretreated with ionic liquids |
| url | http://dx.doi.org/10.1155/2021/4107429 |
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