Model development for the optimization of operational conditions of the pretreatment of wheat straw

The underlying study presents models for the optimization of operational conditions of lignocellulosic biomass pretreatment to facilitate the conceptual process design of biorefineries. Experiments for hydrothermal and dilute acid pretreatment are performed and analyzed. The highest xylose monomer yield obtained for dilute acid pretreatment is YXyl = 98% at a temperature of T = 195 degrees C , a reaction time of t = 18min and a dilute acid concentration of Cacid = 1.25wt% . The data is used to fit a response surface model (RSM), a Gaussian process regression model (GPR), and a mechanistic model based on thermodynamic principles and first-order rate equations. All models are validated respectively with a coefficient of determination of R2RSM = 0.914 , R2GPR = 0.999 and R2mech = 0.988 . Each model is used in an optimization problem to predict the optimal operational conditions that maximize the xylose yield. The conditions found by the mechanistic model (T = 191.6 degrees C, t = 18min, Cac = 1.13wt%) with CXyl,mech = 23.47wt% and the GPR (T = 195 degrees C, t = 18min, Cac = 1.25wt%) with CXyl,GPR = 23.23wt% are in agreement and stand out compared to the RSM metamodeling approach (T = 182. 4 degrees C, t = 26.2min, Cac = 1.25wt%) , which yields CXyl,RSM = 25.72wt% . Considering the scenario of uncertainty in the feedstock composition, the optimization under this uncertainty with the mechanistic model yields slightly different conditions (T = 182.6 degrees C, t = 18min, Cac = 0.84wt%) and CXyl,mech,uc = 20.88wt% . Given the underlying phenomena in the biomass pretreatment, all models have shortcomings; however, the mechanistic model is validated best overall and is thus recommended for further engineering purposes as, e.g., the conceptual process design of biorefineries.

Automated summary

The study focuses on optimizing operational conditions for lignocellulosic biomass pretreatment to aid in the design of biorefineries. Different models are used to predict optimal conditions for maximizing xylose yield, with the mechanistic model performing the best in validation and recommended for further engineering purposes.