Corn stover semi-mechanistic enzymatic hydrolysis model with tight parameter confidence intervals for model-based...

Area of Science:

• Biochemical Engineering
• Biomass Conversion
• Process Modeling

Background:

• Uncertainty in model parameters is crucial for decision-makers but often unreported or too large to be useful.
• Existing models for enzymatic saccharification of pretreated lignocellulosic biomass lack robust uncertainty quantification.

Purpose of the Study:

• To develop and validate a semi-mechanistic model for enzymatic saccharification of dilute acid pretreated corn stover.
• To identify model parameters with statistically significant and usefully tight confidence intervals.

Main Methods:

• Modification of an existing semi-mechanistic model.
• Fitting the model to experimental data with varying solid loadings (10-25% w/w) and different feedstock conditions (pretreatment liquor, washed solids, inhibitors).
• Statistical analysis to identify parameters with reliable confidence intervals.

Main Results:

• The modified model demonstrated a statistically significant improvement in fit compared to existing models.
• A subset of 8 out of 17 parameters exhibited sufficiently tight confidence intervals.
• These parameters are suitable for uncertainty propagation and model analysis without expert truncation.

Conclusions:

• The proposed semi-mechanistic model provides a more accurate and reliable representation of corn stover enzymatic saccharification.
• The identified parameters with tight confidence intervals enhance the model's utility for practical applications and decision-making.
• This work addresses the critical need for quantifiable uncertainty in biomass conversion models.