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Hemicelluloses consume alkali during kraft pulping and dissolve in the black liquor as a low energy fuel. Acidic pre-hydrolysis of softwood chips removes hemicelluloses but preserves cellulose content prior to pulping. This study compared mild pre-hydrolysis (140 °C) kraft pulping with conventional kraft pulping of commercial softwood chips at two H-factors for wood chips with thickness ranging from less than 2 mm to over 6 mm. The chip thickness less than 2 mm increased hemicelluloses oligomer yield and showed little influence on pulp fiber yield. However, the kraft pulp fiber length decreased 5.6%. Kappa number and fiber reject increased dramatically when chip thickness was greater than 6 mm. The detailed compositional analysis of kraft pulp and fiber quality analysis indicate that pre-hydrolysis followed by kraft pulping enhanced delignification with limited reduction of fiber length and width, and increased kinks. Strategic considerations for the integration of pre-hydrolysis into kraft pulping for future biorefineries were outlined.

Hemicelluloses consume alkali during kraft pulping and dissolve in the black liquor as a low energy fuel. Acidic pre-hydrolysis of softwood chips removes hemicelluloses but preserves cellulose content prior to pulping. This study compared mild pre-hydrolysis (140 °C) kraft pulping with conventional kraft pulping of commercial softwood chips at two H-factors for wood chips with thickness ranging from less than 2 mm to over 6 mm. The chip thickness less than 2 mm increased hemicelluloses oligomer yield and showed little influence on pulp fiber yield. However, the kraft pulp fiber length decreased 5.6%. Kappa number and fiber reject increased dramatically when chip thickness was greater than 6 mm. The detailed compositional analysis of kraft pulp and fiber quality analysis indicate that pre-hydrolysis followed by kraft pulping enhanced delignification with limited reduction of fiber length and width, and increased kinks. Strategic considerations for the integration of pre-hydrolysis into kraft pulping for future biorefineries were outlined.

Hemicelluloses are amorphous polymers of sugar molecules that make up a major fraction of lignocellulosic biomasses. They have applications in the bioenergy, textile, mining, cosmetic, and pharmaceutical industries. Industrial use of hemicellulose often requires that the polymer be hydrolyzed into constituent oligomers and monomers. Traditional models of hemicellulose degradation are kinetic, and usually only appropriate for limited operating regimes and specific species. The study of hemicellulose hydrolysis has yielded substantial data in the literature, enabling a diverse data set to be collected for general and widely applicable machine learning models. In this paper, a dataset containing 1955 experimental data points on batch hemicellulose hydrolysis of hardwood was collected from 71 published papers dated from 1985 to 2019. Three machine learning models (ridge regression, support vector regression and artificial neural networks) are assessed on their ability to predict xylose yield and compared to a kinetic model. Although the performance of ridge regression was unsatisfactory, both support vector regression and artificial neural networks outperformed the simple kinetic model. The artificial neural network outperformed support vector regression, reducing the mean absolute error in predicting soluble xylose yield of test data to 6.18%. The results suggest that machine learning models trained on historical data may be used to supplement experimental data, reducing the number of experiments needed.

The cost of enzymes makes enzymatic hydrolysis one of the most expensive steps in the production of lignocellulosic ethanol. Diverse studies have used commercial enzyme cocktails assuming that change in total protein concentration during hydrolysis was solely due to adsorption of endo- and exoglucanases onto the substrate. Given the sensitivity of enzymes and proteins to media conditions this assumption was tested by evaluating and modeling the protein concentration of commercial cocktails at hydrolysis conditions. In the absence of solid substrate, the total protein concentration of a mixture of Celluclast 1.5 L and Novozyme 188 decreased by as much as 45% at 50 °C after 4 days. The individual cocktails as well as a mixture of both were stable at 20 °C. At 50 °C, the protein concentration of Celluclast 1.5 was relatively constant but Novozyme 188 decreased by as much as 77%. It was hypothesized that Novozyme 188 proteins suffer a structural change at 50 °C which leads to protein aggregation and precipitation. Lyophilized β-glucosidase (P-β-glucosidase) at 50 °C exhibited an aggregation rate which was successfully modeled using first order kinetics (R2 = 0.97). By incorporating the possible presence of chaperone proteins in Novozyme 188, the protein aggregation observed for this cocktail was successfully modeled (R2 = 0.96). To accurately model the increasing protein stability observed at high cocktail loadings, the model was modified to include the presence of additives in the cocktail (R2 = 0.98). By combining the measurement of total protein concentration with the proposed Novozyme 188 protein aggregation model, the endo- and exoglucanases concentration in the solid and liquid phases during hydrolysis can be more accurately determined. This methodology can be applied to various systems leading to optimization of enzyme loading by minimizing the excess of endo- and exoglucanases. In addition, the monitoring of endo- and exoglucanases concentrations can be used to build mass balances of enzyme recycling processes and to techno-economically evaluate the viability of enzyme recycling.