The objective of this signature program is to develop high-fidelity computational tools capable of predicting the thermochemical conversion process of lignocellulosic biomass to produce valued products. Thermochemical conversion of biomass involves complex physicochemical processes, including multiphase hydrodynamics, thermal decomposition, and chemical reactions occurring in gas and solid phases.
The research areas in this signature program encompass computational fluid dynamics modeling of multiphase chemically reacting flows and direct numerical simulation of biomass particle evolution under reactor conditions. Multi-scale simulations by coupling CFD and DNS are also a focus area of research. The fundamental details of the evolution of biomass particles in reactors remain largely unknown because of the difficulty of applying highly sensitive, non-intrusive experimental techniques in practical reactor environments. It is anticipated that numerical simulation, based on the foundation of accurate models, can help reveal the fundamental phenomena of biomass thermochemical conversion in harsh reactor environments where particle-level experimental data are not attainable.
This program will bring together faculty from multiple disciplines including fundamental sciences, engineering, mathematics, and computer science. The long-term goal is to create innovative analytical models that can be used for reactor design and optimization using a variety of biomass feedstock under various operating conditions.
Kong is an associate professor in the Mechanical Engineering Department at Iowa State University. He has extensive experience in the modeling and experiment of energy conversion systems, including biomass gasification and fast pyrolysis, internal combustion engines, and technoeconomic analysis of biorefineries. The goal of his research is to create predictive models that can be used as a tool for the design and optimization of future energy systems.
Qingang Xiong, firstname.lastname@example.org
Qingang Xiong is a Senior Technical Staff of the Iowa NSF EPSCoR Program. His research is focused on developing high-fidelity parallel computational methods to simulate multiphase chemically reacting flows. Dr. Xiong obtained his Ph.D. from Institute of Process Engineering, Chinese Academy of Sciences, where he conducted GPU-accelerated direct numerical simulation (DNS) of particle flows for many applications. Currently he is developing an open-source computer code to simulate fast pyrolysis of biomass. He also conducts high-resolution DNS of the physicochemical evolution of biomass particles in fast pyrolysis reactors.
Developing Computational Tools to Simulate Biomass Thermochemical Conversion
This research is part of the Iowa NSF EPSCoR project. We are developing open-source computational models for simulating the thermochemical conversion process of nonfood lignocellulosic biomass to energy products. The models will be able to predict the complex physical and chemical processes associated with biomass fast pyrolysis, gasification, and combustion. Currently the code, BIOTC (BIOmass Thermochemical Conversion), is capable of simulating a large number of solid biomass particles in a fast pyrolysis reactor and of predicting the yield of syngas, tar (to be condensed to produce pyrolysis oil), and biochar. Further improvements of the code are in progress. Based on the code, users can further implement updated submodels to describe essential physical and chemical processes. It is anticipated that the models can also be used by experimentalists, who are not experts in modeling, to help identify key experimental parameters and designs. These computational models will be incorporated as part of cyberinfrastructure to support bioenergy research. The long-term goal is to create open-source virtual engineering tools for the design and optimization of reactors to convert biomass energy. This work is led by Song-Charng Kong and Qingang Xiong.
Physicochemical Evolution of Bio-Oil During Gasification
Fast pyrolysis of biomass to produce bio-oil has moved to the forefront of bioenergy research and development. Bio-oil, which is a mixture of complex oxygenated hydrocarbon species, is much easier to transport than bulky solid biomass. A novel approach is to convert biomass to bio-oil at widely distributed small-scale processing plants, transport bio-oil to a centralized location, gasify bio-oil to syngas, and upgrade the syngas to transportation fuels. This research will investigate this approach through a combination of experimental and analytical studies that can potentially lead to the large-scale commercialization of this technology that has the potential to turn agricultural residues (e.g., corn cobs, corn stover, switchgrass, etc) into valued feedstock. This work is sponsored by Iowa Energy Center. In this research, we develop liquid atomization models to predict bio-oil spray atomization, multi-component vaporization models to simulate the gasification of the complex compounds in bio-oil drops, and chemical kinetics to describe the chemical reactions leading to the formation of synthesis gas. This work is led by Song-Charng Kong.
Modeling Fast Pyrolysis of Dense Biomass
Particle Flows in Auger Reactors
Fast pyrolysis of lignocellulosic biomass utilizing auger reactors is of great interest because of its rapid heat and mass transfer. In an auger reactor, biomass particles and steel shots, serving as heat carriers, are packed inside the reactor. The dense solid flows are pushed forward by the auger, and the physicochemical processes of converting biomass to tar, syngas, and biochar are extremely complex. This research is to create various physical and chemical models to numerically characterize these processes. Important components of the numerical models include solid-solid contact heat transfer, mixing of solids, physical evolution of solid particles, and gas phase chemical reactions. It is anticipated that the resulting models will be used as a tool to help design and optimize the auger geometry and motion and the reactor operating conditions. This work is led by Song-Charng Kong.