Modeling of Bioenergy Production

In this dissertation we address three different sustainability concepts: [1] modeling of biodiesel production via heterogeneous catalysis, [2] life cycle analysis for pyrolysis of switchgrass for using in power plant, and [3] modeling of pyrolysis of biomass. Thus we deal with Specific Aim 1, 2 and 3.

In Specific Aim 1, the models for esterification in biodiesel production via heterogeneous catalysis were developed. The models of the reaction over the catalysts were developed in two parts. First, a kinetic study was performed using a deterministic model to develop a suitable kinetic expression; the related parameters were subsequently estimated by numerical techniques. Second, a stochastic model was developed to further confirm the nature of the reaction at the molecular level. The deterministic and stochastic models were in good agreement.

In Specific Aim 2, life cycle analysis and life cycle cost for pyrolysis of switchgrass for using in power plant model were developed. The greenhouse gas (GHG) emission for power generation was investigated through life cycle assessment. The process consists of cultivation, harvesting, transportation, storage, pyrolysis, transportation and power generation. Here pyrolysis oil is converted to electric power through co- combustion in conventional fossil fuel power plants. The conventional power plants which are considered in this work are diesel engine power plant, natural gas turbine power plant, coal-fired steam-cycle power plant and oil-fired steam-cycle power plant. Several scenarios are conducted to determine the effect of selected design variables on the production of pyrolysis oil and type of conventional power plants.

In Specific Aim 3, pyrolysis of biomass models were developed. Since modeling of pyrolysis of biomass is complex and challenging because of short reaction times, temperatures as high as a thousand degrees Celsius, and biomass of varying or unknown chemical compositions. As such a deterministic model is not capable of representing the pyrolysis reaction system. We propose a new kinetic reaction model, which would account for significant uncertainty. Specifically we have employed fuzzy modeling using the adaptive neuro-fuzzy inference system (ANFIS) in order to describe the pyrolysis of biomass. The resulting model is in better agreement with experimental data than known deterministic models.