Uncertainties in feedstock composition and kinetic parameters impact the performance of enzymatic hydrolysis of lignocellulosic biomass. Operating strategies need to be accordingly modified to achieve higher yield of glucose. Therefore, stochastic optimization and optimal control problems were formulated to maximize glucose concentration and minimize variance. The optimal temperature for maximization of glucose concentration reduced in the presence of uncertainties. For uncertainties in kinetic parameters, the optimal temperature reduced from 323.34 K to 317.07 K for a batch time of 12 h, and the glucose concentration increased by 7.7% when the stochastic optimal temperature was used instead of deterministic temperature. Similarly, for the objective of minimization of variance in glucose concentration at the end of the batch, stochastic optimization reduced the variance by 89% as compared to deterministic optimization. Stochastic optimal control resulted in up to 60% improvement in mean glucose concentration in comparison to the deterministic optimal control.

原料组成和动力学参数的不确定性影响木质纤维素生物质的酶促水解性能。需要相应地修改操作策略以获得更高的葡萄糖产量。因此，制定了随机优化和最优控制问题，以最大程度地提高葡萄糖浓度并最小化方差。在存在不确定性的情况下，用于最大化葡萄糖浓度的最佳温度降低了。由于动力学参数的不确定性，最佳温度从323.34 K降低到317.07 K，分批处理12小时，当使用随机最佳温度代替确定性温度时，葡萄糖浓度增加了7.7％。同样，为了使批次结束时的葡萄糖浓度差异最小化，与确定性优化相比，随机优化将方差减少了89％。与确定性最佳控制相比，随机最佳控制可将平均葡萄糖浓度提高多达60％。