Frederico G Fonseca attended Chemical Engineering in Lisbon Tech (IST-ULisbon) and briefly integrated the group in 2015, in a small project entitled “Moisture content as a design and operational parameter for fast pyrolysis”. In February 2018, he started his doctoral project focusing on the modeling of fast pyrolysis based on the materialized bioliq® concept. This work consists of the development of a model in a commercial chemical engineering simulation software (Aspen Plus™), which was chosen due to its academic and industrial ubiquity.
To ensure that the desired rigor and flexibility of the model are ensured, a great focus is put in the representation of biomass, the reaction network that characterizes fast pyrolysis, and the produced bio-oil. Within this context, Mr. Fonseca applied successfully for a KHYS-funded research exchange that focused on the use of Near-Infrared Spectroscopy (NIRS) to develop predictive models to estimate properties (such as moisture, ash content) of wheat straw. This project took place in the NIRS Laboratory (http://www.nirsresearch.com), of the Agricultural Engineering Department of the King Mongkut’s Institute of Technology, Ladkrabang, Bangkok, Thailand.
Non-funded participant of the the Bioeconomy Graduate Program BBW ForWerts (https://biooekonomie-bw.uni-hohenheim.de/bbwforwerts).
Topics of focus
- Aspen Plus™ modeling and simulation
- Sensitivity analyses to influence of feedstock characteristics
- Sensitivity analyses to influence of thermodynamic modeling parameters
- Development of a predictive statistical model for possible future online estimation of relevant feedstock parameters
- Development of a representative bio-oil mixture for lignocellulosic feedstocks
- Gathering and comparison of literature-published kinetic pathways for pyrolysis
- Fitting of such pathways to degradation kinetics for currently studied materials
- Recognition of critical pathways using DETCHEM (collab with Dr. Karla Herrera)
- Estimation of degradation kinetics (isoconversional, curve fitting)
- Estimation of lignocellulosic content