CAS OpenIR
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 PublicationACS OMEGA
ISSN2470-1343
Volume6Issue:41Pages:27183-27199
AbstractThree 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.
DOI10.1021/acsomega.1c03851
Language英语
WOS KeywordRESPONSE-SURFACE METHODOLOGY ; ARTIFICIAL NEURAL-NETWORKS ; PROMOTED IRON CATALYST ; MICROCHANNEL REACTOR ; SLURRY REACTOR ; OPTIMIZATION ; CONVERSION ; DEACTIVATION ; PERFORMANCE ; REGRESSION
Funding ProjectSchool 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 AreaChemistry
WOS SubjectChemistry, Multidisciplinary
Funding OrganizationSchool 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 IDWOS:000710449700036
PublisherAMER CHEMICAL SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/50975
Collection中国科学院过程工程研究所
Corresponding AuthorJin, Huan; Sun, Yong
Affiliation1.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.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Yixiao]'s Articles
[Hu, Jing]'s Articles
[Zhang, Xiyue]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Yixiao]'s Articles
[Hu, Jing]'s Articles
[Zhang, Xiyue]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Yixiao]'s Articles
[Hu, Jing]'s Articles
[Zhang, Xiyue]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.