Modeling the beating degree of wheat straw biochemical mechanical pulp using multifactorial equations

Area of Science:

• Pulp and Paper Science
• Biochemical Engineering
• Process Optimization

Background:

• Traditional pulp beating relies on inefficient "produce-test-adjust" cycles, leading to high energy and chemical usage.
• Optimizing wheat straw biochemical mechanical pulping is crucial for sustainable papermaking.

Purpose of the Study:

• To identify key factors affecting wheat straw pulp beating degree.
• To develop a predictive model for optimizing the bio-pulping process.

Main Methods:

• Plackett-Burman (PB) design to screen ten potential factors.
• Box-Behnken Design (BBD) response surface methodology (RSM) to build a predictive model.
• Identification of refiner gap, KOH dosage, and enzyme dosage as critical parameters.

Main Results:

• A quadratic polynomial model with R² of 0.9899, adjusted R² of 0.9768, and predicted R² of 0.8723 was established.
• The model demonstrated high reliability, practicality, and predictive ability (signal-to-noise ratio of 29.2395).
• BBD application avoids extreme conditions, with all experimental points within safe operating ranges.

Conclusions:

• The developed BBD-RSM model provides a predictable and controllable optimization tool for wheat straw bio-pulping.
• This approach enhances efficiency and reduces resource consumption compared to traditional methods.
• The study offers a novel application of BBD for optimizing pulp beating degree.