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AuthorUllah, Zahid
AuthorKhan, Muzammil
AuthorNaqvi, Salman Raza
AuthorKhan, Muhammad Nouman Aslam
AuthorFarooq, Wasif
AuthorAnjum, Muhammad Waqas
AuthorYaqub, Muhammad Waqas
AuthorAlMohamadi, Hamad
AuthorAlmomani, Fares
Available date2023-06-25T08:25:37Z
Publication Date2022
Publication NameProcess Safety and Environmental Protection
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.psep.2022.04.013
URIhttp://hdl.handle.net/10576/44777
AbstractThis study presents an integrated hybrid framework of data-driven (cascade forward neural network (CFNN)), metaheuristic (artificial bee colony (ABC)), and a mechanistic modeling (Aspen simulation) approach for the biomass pyrolysis process for bio-oil production. We applied CFNN and an ABC to predict and optimize bio-oil yield. The CFNN model achieved high prediction performance with a correlation coefficient value of 0.95 and a root mean squared error value of 0.39. Furthermore, the CFNN-ABC derived optimum parameters were then validated using a mechanistic model of the pyrolysis process. The CFNN and Aspen simulation results were following the experimental results, with an average deviation of 5%. The feature importance showed that the internal information about biomass was more relevant than external factors for bio-oil yield. The partial dependence plots were developed to know the insights into the biomass pyrolysis process. This study presents a modeling and simulation platform for bio-oil production that can increase the waste-to-energy process and can be helpful for academia.
SponsorThe corresponding author would like to acknowledge Pakistan Science Foundation (grant number: PSF/CRP/C-NUST/T-Helix (47) ) for financial support and National University of Sciences & Technology for technical support.
Languageen
PublisherElsevier
SubjectArtificial bee colony
Aspen plus
Bioenergy
Biomass
Cascade neural network
Machine learning
TitleAn integrated framework of data-driven, metaheuristic, and mechanistic modeling approach for biomass pyrolysis
TypeArticle
Pagination337-345
Volume Number162
dc.accessType Abstract Only


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