Knowledge Management System Of Institute of process engineering,CAS
| Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer-Tropsch Synthesis | |
| Wang, Yixiao1; Hu, Jing1; Zhang, Xiyue1; Yusuf, Abubakar1; Qi, Binbin2; Jin, Huan5; Liu, Yiyang3; He, Jun1; Wang, Yunshan4; Yang, Gang4; Sun, Yong1,6 | |
| 2021-10-19 | |
| Source Publication | ACS OMEGA
 |
| ISSN | 2470-1343 |
| Volume | 6Issue:41Pages:27183-27199 |
| Abstract | Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer-Tropsch (FT) synthesis. Using a 29 x 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C-2-C-15) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With additional validation data sets (15 x 36 matrix with total 540 data sets), the uncertainties of using three different models were compared and the outcomes were: RBFNN (+/- 5% uncertainties), RSM (+/- 10% uncertainties), and CKGA (+/- 30% uncertainties), respectively. A new effective strategy for kinetic study of the complex FT synthesis is proposed: RBFNN is used for data matrix generation with a limited number of experimental data sets (due to its fast converge and less computation time), CKGA is used for mechanism selections by the Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach using a genetic algorithm to find out potential reaction pathways, and RSM is used for statistical analysis of the investigated data matrix (generated from RBFNN through central composite design) upon responses and subsequent singular/multiple optimizations. The proposed strategy is a very useful and practical tool in process engineering design and practice for the product distribution during FT synthesis. |
| DOI | 10.1021/acsomega.1c03851 |
| Language | 英语 |
| WOS Keyword | RESPONSE-SURFACE METHODOLOGY ; ARTIFICIAL NEURAL-NETWORKS ; PROMOTED IRON CATALYST ; MICROCHANNEL REACTOR ; SLURRY REACTOR ; OPTIMIZATION ; CONVERSION ; DEACTIVATION ; PERFORMANCE ; REGRESSION |
| Funding Project | School of Engineering, Edith Cowan University, Australia ; University of Nottingham Ningbo China[I01210100011] ; University of Nottingham ; Qianjiang Talent Scheme[QJD1803014] ; National Key R&D Program of China[2018YFC1903500] ; Ningbo Science and Technology Innovation 2025 Key Project[2020Z100] ; Ningbo Municipal Commonweal Key Program[2019C10033] ; Ningbo Municipal Commonweal Key Program[2019C10104] ; Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province[2020E10018] |
| WOS Research Area | Chemistry |
| WOS Subject | Chemistry, Multidisciplinary |
| Funding Organization | School of Engineering, Edith Cowan University, Australia ; University of Nottingham Ningbo China ; University of Nottingham ; Qianjiang Talent Scheme ; National Key R&D Program of China ; Ningbo Science and Technology Innovation 2025 Key Project ; Ningbo Municipal Commonweal Key Program ; Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province |
| WOS ID | WOS:000710449700036 |
| Publisher | AMER CHEMICAL SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ipe.ac.cn/handle/122111/50975 |
| Collection | 中国科学院过程工程研究所 |
| Corresponding Author | Jin, Huan; Sun, Yong |
| Affiliation | 1.Univ Nottingham Ningbo, Key Lab Carbonaceous Wastes Proc & Proc Intensifi, Ningbo 315100, Peoples R China 2.China Univ Petr, Dept Petr Engn, Beijing 102249, Peoples R China 3.Univ Coll London UCL, Dept Chem, London WC1H 0AJ, England 4.Chinese Acad Sci, Inst Proc Engn, Natl Engn Lab Cleaner Hydromet Prod Technol, Beijing 100190, Peoples R China 5.Univ Nottingham Ningbo, Sch Comp Sci, Ningbo 315100, Peoples R China 6.Edith Cowan Univ, Sch Engn, Joondalup, WA 6027, Australia |
| Recommended Citation GB/T 7714 | Wang, Yixiao,Hu, Jing,Zhang, Xiyue,et al. Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer-Tropsch Synthesis[J]. ACS OMEGA,2021,6(41):27183-27199. |
| APA | Wang, Yixiao.,Hu, Jing.,Zhang, Xiyue.,Yusuf, Abubakar.,Qi, Binbin.,...&Sun, Yong.(2021).Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer-Tropsch Synthesis.ACS OMEGA,6(41),27183-27199. |
| MLA | Wang, Yixiao,et al."Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer-Tropsch Synthesis".ACS OMEGA 6.41(2021):27183-27199. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment