A two-phase substrate model for enzymatic hydrolysis of lignocellulose: application to batch and continuous reactors.

BACKGROUND: Enzymatic hydrolysis continues to have a significant projected production cost for the biological conversion of biomass to fuels and chemicals, motivating research into improved enzyme and reactor technologies in order to reduce enzyme usage and equipment costs. However, technology development is stymied by a lack of accurate and computationally accessible enzymatic-hydrolysis reaction models. Enzymatic deconstruction of cellulosic materials is an exceedingly complex physico-chemical process. Models which elucidate specific mechanisms of deconstruction are often too computationally intensive to be accessible in process or multi-physics simulations, and empirical models are often too inflexible to be effectively applied outside of their batch contexts. In this paper, we employ a phenomenological modeling approach to represent rate slowdown due to substrate structure (implemented as two substrate phases) and feedback inhibition, and apply the model to a continuous reactor system. RESULTS: A phenomenological model was developed in order to predict glucose and solids concentrations in batch and continuous enzymatic-hydrolysis reactors from which liquor is continuously removed by ultrafiltration. A series of batch experiments were performed, varying initial conditions (solids, enzyme, and sugar concentrations), and best-fit model parameters were determined using constrained nonlinear least-squares methods. The model achieved a good fit for overall sugar yield and insoluble solids concentration, as well as for the reduced rate of sugar production over time. Additionally, without refitting model coefficients, good quantitative agreement was observed between results from continuous enzymatic-hydrolysis experiments and model predictions. Finally, the sensitivity of the model to its parameters is explored and discussed. CONCLUSIONS: Although the phenomena represented by the model correspond to behaviors that emerge from clusters of mechanisms, and hence a set of model coefficients are unique to the substrate and the enzyme system, the model is efficient to solve and may be applied to novel reactor schema and implemented in computational fluid dynamics (CFD) simulations. Hence, this modeling approach finds the right balance between model complexity and computational efficiency. These capabilities have broad application to reactor design, scale-up, and process optimization.

Assessing pretreatment reactor scaling through empirical analysis.

BACKGROUND: Pretreatment is a critical step in the biochemical conversion of lignocellulosic biomass to fuels and chemicals. Due to the complexity of the physicochemical transformations involved, predictively scaling up technology from bench- to pilot-scale is difficult. This study examines how pretreatment effectiveness under nominally similar reaction conditions is influenced by pretreatment reactor design and scale using four different pretreatment reaction systems ranging from a 3 g batch reactor to a 10 dry-ton/days continuous reactor. The reactor systems examined were an automated solvent extractor (ASE), steam explosion reactor (SER), ZipperClave®Reactor (ZCR), and large continuous horizontal screw reactor (LHR). To our knowledge, this is the first such study performed on pretreatment reactors across a range of reaction conditions and at different reactor scales. RESULTS: The comparative pretreatment performance results obtained for each reactor system were used to develop response surface models for total xylose yield after pretreatment and total sugar yield after pretreatment followed by enzymatic hydrolysis. Near- and very-near-optimal regions were defined as the set of conditions that the model identified as producing yields within one and two standard deviations of the optimum yield. Optimal conditions identified in the smallest scale system (the ASE) were within the near-optimal region of the largest scale reactor system evaluated. The maximum total sugar yields for the ASE and LHR were [Formula: see text], while [Formula: see text] was the optimum observed in the ZipperClave. CONCLUSIONS: The optimum condition identified using the automated and less costly to operate ASE system was within the very-near-optimal space for the total xylose yield of both the ZCR and the LHR, and was within the near-optimal space for total sugar yield for the LHR. This indicates that the ASE is a good tool for cost effectively finding near-optimal conditions for operating pilot-scale systems. Additionally, using a severity factor approach to optimization was found to be inadequate compared to a multivariate optimization method. Finally, the ASE and the LHR were able to enable significantly higher total sugar yields after enzymatic hydrolysis relative to the ZCR, despite having similar optimal conditions and total xylose yields. This underscores the importance of mechanical disruption during pretreatment to improvement of enzymatic digestibility.

Parameter determination and validation for a mechanistic model of the enzymatic saccharification of cellulose-Iß.

Cost-effective production of fuels and chemicals from lignocellulosic biomass often involves enzymatic saccharification, which has been the subject of intense research and development. Recently, a mechanistic model for the enzymatic saccharification of cellulose has been developed that accounts for distribution of cellulose chain lengths, the accessibility of insoluble cellulose to enzymes, and the distinct modes of action of the component cellulases [Griggs et al. (2012) Biotechnol. Bioeng., 109(3):665-675; Griggs et al. (2012) Biotechnol. Bioeng., 109(3):676-685]. However, determining appropriate values for the adsorption, inhibition, and rate parameters required further experimental investigation. In this work, we performed several sets of experiments to aid in parameter estimation and to quantitatively validate the model. Cellulosic materials differing in degrees of polymerization and crystallinity (α-cellulose-Iß and highly crystalline cellulose-Iß ) were digested by component enzymes (EGI/CBHI/ßG) and by mixtures of these enzymes. Based on information from the literature and the results from these experiments, a single set of model parameters was determined, and the model simulation results using this set of parameters were compared with the experimental data of total glucan conversion, chain-length distribution, and crystallinity. Model simulations show significant agreement with the experimentally derived glucan conversion and chain-length distribution curves and provide interesting insights into multiple complex and interacting physico-chemical phenomena involved in enzymatic hydrolysis, including enzyme synergism, substrate accessibility, cellulose chain length distribution and crystallinity, and inhibition of cellulases by soluble sugars.

A low-cost solid-liquid separation process for enzymatically hydrolyzed corn stover slurries.

Solid-liquid separation of intermediate process slurries is required in some process configurations for the conversion of lignocellulosic biomass to transportation fuels. Thermochemically pretreated and enzymatically hydrolyzed corn stover slurries have proven difficult to filter due to formation of very low permeability cakes that are rich in lignin. Treatment of two different slurries with polyelectrolyte flocculant was demonstrated to increase mean particle size and filterability. Filtration flux was greatly improved, and thus scaled filter unit capacity was increased approximately 40-fold compared with unflocculated slurry. Although additional costs were accrued using polyelectrolyte, techno-economic analysis revealed that the increase in filter capacity significantly reduced overall production costs. Fuel production cost at 95% sugar recovery was reduced by $1.35 US per gallon gasoline equivalent for dilute-acid pretreated and enzymatically hydrolyzed slurries and $3.40 for slurries produced using an additional alkaline de-acetylation preprocessing step that is even more difficult to natively filter.

A mechanistic model for enzymatic saccharification of cellulose using continuous distribution kinetics II: cooperative enzyme action, solution kinetics, and product inhibition.

The projected cost for the enzymatic hydrolysis of cellulosic biomass continues to be a barrier for the commercial production of liquid transportation fuels from renewable feedstocks. Predictive models for the kinetics of the enzymatic reactions will enable an improved understanding of current limitations, such as the slow-down of the overall conversion rate, and may point the way for more efficient utilization of the enzymes in order to achieve higher conversion yields. A mechanistically based kinetic model for the enzymatic hydrolysis of cellulose was recently reported in Griggs et al. (2011) (Part I). In this article (Part II), the enzyme system is expanded to include solution-phase kinetics, particularly cellobiose-to-glucose conversion by ß-glucosidase (ßG), and novel adsorption and product inhibition schemes have been incorporated, based on current structural knowledge of the component enzymes. Model results show cases of cooperative and non-cooperative hydrolysis for an enzyme system consisting of EG(I) and CBH(I). The model is used to explore various potential rate-limiting phenomena, such as substrate accessibility, product inhibition, sterically hindered enzyme adsorption, and the molecular weight of the cellulose substrate.

A mechanistic model for enzymatic saccharification of cellulose using continuous distribution kinetics I: depolymerization by EGI and CBHI.

A mechanistically based kinetic model for the enzymatic hydrolysis of cellulosic biomass has been developed that incorporates the distinct modes of action of cellulases on insoluble cellulose polymer chains. Cellulose depolymerization by an endoglucanase (endoglucanase I, EG(I) ) and an exoglucanase (cellobiohydrolase I, CBH(I)) is modeled using population-balance equations, which provide a kinetic description of the evolution of a polydisperse distribution of chain lengths. The cellulose substrate is assumed to have enzyme-accessible chains and inaccessible interior chains. EG(I) is assumed to randomly cleave insoluble cellulose chains. For CBH(I), distinct steps for adsorption, complexation, processive hydrolysis, and desorption are included in the mechanistic description. Population-balance models that employ continuous distributions track the evolution of the spectrum of chain lengths, and do not require solving equations for all chemical species present in the reacting mixture, resulting in computationally efficient simulations. The theoretical and mathematical development needed to describe the hydrolysis of insoluble cellulose chains embedded in a solid particle by EG(I) and CBH(I) is given in this article (Part I). Results for the time evolution of the distribution of chain sizes are provided for independent and combined enzyme hydrolysis. A companion article (Part II) incorporates this modeling framework to study cellulose conversion processes, specifically, solution kinetics, enzyme inhibition, and cooperative enzymatic action.