07/11/2023
By Danielle Fretwell
The Francis College of Engineering, Department of Chemical Engineering, invites you to attend a Master's Thesis defense by Heba Morgan on "Generalization of parametric effects of reactor operations and feedstock properties on the yields and compositions of bio-oils produced from pine pyrolysis."
Candidate Name: Heba Morgan
Degree: Master’s
Defense Date: July 18, 2023
Time: 2 - 4 p.m.
Location: Perry Hall 415
Advisor: Hsi-Wu Wong, Ph.D., Associate Professor, Department of Chemical Engineering, University of Massachusetts Lowell
Committee Members
- Dongming Xie, Ph.D., Associate Professor, Department of Chemical Engineering, UMass Lowell
- Hunter Mack, Ph.D., Associate Professor, Department of Mechanical Engineering, UMass Lowell
Brief Abstract:
Literature data on pine pyrolysis are collected and analyzed by artificial neural networks (ANNs) to understand the parametric effects on the resultant mass yields of bio-oil, char, and non-condensable gases. Predictive ANN models that can quantitatively describe the oil, char, and gas yields using reactor operation conditions and feedstock properties for three different types of reactors are presented. The dependence of bio-oil compositions on reaction operation conditions and reactor types is also discussed. Our work reveals that reaction temperature is the most critical parameter determining oil, char, and gas yields, and a maximum bio-oil yield at approximately 60% was found at a reaction temperature between 450 and 600 oC regardless reactor types. Reaction time is only critical for tubular reactor operations, and the elemental compositions of the pine feedstock only have an effect during batch reactor operations. Feedstock particle size does not appear to affect overall bio-oil yields, but the selectivity toward individual bio-oil molecules was influenced, where larger particles promote the yields of secondary products. Our ANN generated models and the parametric analysis of individual families of bio-oil molecules can be used as a guidance to determine the best reactor types, reaction operation conditions, and feedstock properties for obtaining the most desirable bio-oil yields and compositions.