Year of Publication


Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation




Chemical and Materials Engineering

First Advisor

Dr. Naresh Shah

Second Advisor

Dr. Gerald P. Huffman


The past few years have seen an upsurge in the use of renewable biomass as a source of energy due to growing concerns over greenhouse gas emissions caused by the combustion of fossil fuels and the need for energy independence due to depleting fossil fuel resources. Although coal will continue to be a major source of energy for many years, there is still great interest in replacing part of the coal used in energy generation with renewable biomass. Combustion converts inherent chemical energy of carbonaceous feedstock to only thermal energy. On the other hand, partial oxidation processes like gasification convert chemical energy into thermal energy as well as synthesis gas which can be easily stored or transported using existing infrastructure for downstream chemical conversion to higher value specialty chemicals as well as production of heat, hydrogen, and power.

Devolatilization or pyrolysis plays an important role during gasification and is considered to be the starting point for all heterogeneous gasification reactions. Pyrolysis kinetic modeling is, therefore, an important step in analyzing interactions between blended feedstocks. The thermal evolution profiles of different coal-biomass blends were investigated at various heating rates using thermogravimetric analysis. Using MATLAB, complex models for devolatilization of the blends were solved for obtaining and predicting the global kinetic parameters. Parallel first order reactions model, distributed activation energy model and matrix inversion algorithm were utilized and compared for this purpose. Using these global kinetic parameters, devolatilization rates of unknown fuel blends gasified at unknown heating rates can be accurately predicted using the matrix inversion method.

A unique laboratory scale auto-thermal moving bed gasifier was also designed and constructed for studying the thermochemical conversion of coal-biomass blends. The effect of varying operating parameters was analyzed for optimizing syngas production. In addition, stable carbon isotope analysis using Gas Chromatography-Combustion-Isotope Ratio Mass Spectrometry (GC-C-IRMS) was used for qualitatively and quantitatively measuring individual contributions of coal and biomass feedstocks for generation of carbonaceous gases during gasification. The predictive models utilized and experimental data obtained via these methods can provide valuable information for analyzing synergistic interactions between feedstocks and also for process modeling and optimization.