Kinetic Analysis of Bio-Oil Aging by Using Pattern Search Method


Bio-oil derived from fast pyrolysis of lignocellulosic biomass is unstable, and aging would occur during its storage, handling, and transportation. The kinetic analysis of bio-oil aging is fundamental for the investigation of bio-oil aging mechanisms and the utilization of bio-oil as biofuels, biomaterials or biochemicals. The aging kinetic experiments of bio-oil from poplar wood pyrolysis were conducted at different aging temperatures of 303, 333, 353, and 363 K for different specified periods of time in capped glass vessels. The traditional method with two separate fittings was employed to fit experimental data, and the results indicated that the obtained kinetic parameters could not fit the experimental data well. An advanced approach for kinetic modeling of bio-oil aging has been developed by simultaneously processing experimental data at different aging temperatures and using the pattern search method. The aging kinetic model with the optimized parameters predicted the aging kinetic experimental data of the bio-oil sample very well for different aging temperatures.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Chemical Engineering & Applied Chemistry
College of Engineering & Physical Sciences > Energy and Bioproducts Research Institute (EBRI)
College of Engineering & Physical Sciences
Additional Information: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Ind. Eng. Chem. Res., copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see
Uncontrolled Keywords: Chemistry(all),Chemical Engineering(all),Industrial and Manufacturing Engineering
Publication ISSN: 1520-5045
Last Modified: 11 Mar 2024 08:31
Date Deposited: 06 Jan 2020 11:42
Full Text Link:
Related URLs: ... cs.iecr.9b05629 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-01-29
Published Online Date: 2020-01-02
Accepted Date: 2020-01-01
Authors: Zhang, Shukai
Li, Chong
Guo, Xiaojuan
Rahman, Md.Maksudur
Zhang, Xingguang
Yu, Xi (ORCID Profile 0000-0003-3574-6032)
Cai, Junmeng



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record