430912 Process Model of Biomass Gasification Using Aspen Simulation

Thursday, November 12, 2015: 9:21 AM
250B (Salt Palace Convention Center)
Anand Alembath, Chemical Engineering, Missouri University of Science and Technology, Rolla, MO, Hassan Golpour, Chemical & Biochemical Engineering, Missouri University of Science and Technology, Rolla, MO and Joseph D. Smith, Idaho National Laboratory, Idaho Falss, ID

To meet the demand of increasing energy needs, our current focus is on developing a commercial biomass gasification process. Efforts to scale the smaller scale laboratory reactor to commercial operation relies on first developing a comprehensive Aspen based process model, identifying the key operating parameters for the reactor and then optimizing the overall process. The process model attempts to mimic the actual biomass gasification system at steady state by analyzing each of the four zones inside a downdraft gasifier including: 1) Drying, 2) Pyrolysis, 3) Combustion and 4) Gasification zone. Using this multi-zonal approach, the process model can be easily modified for different operating facilities and conditions.

The current model is used to analyze the following important operational aspects of the gasifier system:

a)      Syngas produced.

b)      Liquid tar produced and gaseous tar present in the syngas.

c)      Overall Energy Balance.

d)      Air to Fuel Ratio.

e)      Char Conversion.

f)       Temperature profile through the gasifier.

Primary interest has been paid to the energy balance. The system, at steady state must be capable of operating without any external heat added to maintain the desired gasifier temperature profile. Energy release in the combustion zone must provide heat duty to support the other three processes occurring in the system. For this reason, reactors describing each processes inside the gasifier are kinetically modeled using a CSTR with specific surface and volumetric reactions. The solid-gas reaction rates are described using a shrinking core model.

ASPEN process parameters were identified to match different operating factors and used to optimize the overall process. Results were verified using experimental yield data collected from a lab scale biomass gasifier operated at the Missouri S&T Hybrid Energy Center and compared to previously published gasifier data.

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