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Published on: May 19, 2019
Modeling changes in biomass composition during microwave-based alkali pretreatment of switchgrass
Deepak R Keshwani1, Jay J Cheng
1Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0726, USA.
Biotechnology and Bioengineering
|August 19, 2009
Summary
This study models switchgrass biomass changes during microwave pretreatment using kinetic and fuzzy logic approaches. Both methods accurately predict lignin, cellulose, and xylan composition, aiding bioethanol production simulations.
Area of Science:
- Biomass Pretreatment
- Biochemical Engineering
- Computational Modeling
Background:
- Switchgrass is a key lignocellulosic feedstock for bioethanol.
- Optimizing pretreatment is crucial for efficient biomass conversion.
- Modeling biomass composition changes aids process design.
Purpose of the Study:
- To model biomass composition changes during microwave-assisted alkali pretreatment of switchgrass.
- To compare kinetic modeling and fuzzy inference system approaches.
- To assess model accuracy and uncertainty for lignin, cellulose, and xylan.
Main Methods:
- Developed kinetic models with time-dependent rate coefficients.
- Implemented a Mamdani-type fuzzy inference system.
- Used dielectric loss tangent and pretreatment time as predictors.
- Validated models with experimental data (1-3% NaOH, 5-20 min).
Main Results:
- Kinetic models predicted lignin and xylan with <2% error; cellulose with 5-7% error.
- Fuzzy inference system predicted lignin with <2% error; cellulose and xylan with <3% error.
- Model predictions showed low uncertainty across biomass components.
Conclusions:
- Both kinetic and fuzzy logic models effectively predict biomass composition changes.
- Fuzzy inference system demonstrated high accuracy and low uncertainty.
- These models can optimize bioethanol production processes from lignocellulosic materials.
