Limited Time Offer

Claim a 25% discount on all eLearning courses (including credentials) with code ELEARN25.

Offer is valid from March 10-31. Public courses excluded from promo. 

(444e) Development of a 3D Transport-Reaction MODEL to Understand the Pretreatment Processes in Plant Using RAMAN Spectroscopy

Ramanna, S. - Presenter, University of Minnesota
Ramarao, B. V., State Univ of New York
Xu, F., Beijing Forestry University
Ramaswamy, S., University of Minnesota
The renewable nature of plant biomass makes it an ideal raw material for the production of a wide variety of bio-based products that include pulp and paper, bioplastics, biofuels, wood plastic composites etc. In order to carry out biomass conversion to other products, the recalcitrant structure of biomass is first disrupted through a pretreatment process. This step also increases the efficiency of the subsequent hydrolysis step. Pretreatment can be carried out by mechanical, chemical, thermochemical or biological methods. During this process, one or more of the cell wall components are degraded and the cell wall structure is significantly altered. These changes in structure and topochemistry of the plant cell wall affect the transport and reaction during the pretreatment process. Hence, it is extremely important to develop a fundamental understanding of the underlying process in order to develop effective pretreatment methods.

In the current work, the structural and topochemical changes in lignin during the pretreatment of the biomass structure is obtained using Raman spectroscopy. In addition, a stochastic dynamic model using 3D structure of plant biomass is presented to evaluate the transport and reaction behavior of delignification during pretreatment processes. The lignin distribution map of the initial biomass structure obtained from Raman imaging is used as a starting point for this model. The transport-reaction model is based on a hybrid random walk process for diffusion of the pretreatment reagent followed by reaction with the cell wall components based on a certain reaction probability. The reagent walkers diffuse through the lumen and pore spaces of the cell wall and follow a random walk path until they encounter the cell wall interface. At the interface, based on the probability of reaction and the ratio of diffusivities between the pore space and cell wall, they either react with the lignin in the cell wall or diffuse further into the cell wall. The probability of reaction is based on a Thiele modulus which is a function of both diffusion and reaction. The degradation of lignin is modeled as a pseudo first order reaction where lignin is the limiting reactant. This process is continued with a sufficient number of reagent particles or walkers to simulate the entire course of the reaction and the results are compared with experimental data. This stochastic dynamic approach keeps track of the bulk concentration and spatial distribution of both lignin and the reagent used for pretreatment in real time. The results obtained from the model were compared with experimental data obtained from Raman spectroscopy. In addition, the variation of different diffusive and reactive parameters on the transport-reaction process were also studied. An effective rate constant Keff based on the overall transport-reaction was determined for the process which was either time dependent or independent of time based on the time required for diffusion and reaction. Additionally, an overall transport rate coefficient KT based on the survival time of the tracers was also determined.

Two cases of pretreatment corresponding to different types of biomass species and reagent were studied. These include alkali pretreatment of poplar and hot water pretreatment of Acer. The concentration profiles of lignin were obtained in each case and compared to the experimental data. The corresponding rate expressions were also determined. The results from the two cases were compared to study the effect of biomass species and the reagent on the pretreatment process. Thus, this model enables us to determine the effect of 3D structure as well as the effective diffusivity and local reaction kinetics on the overall pretreatment process and structural evolution. This will provide additional fundamental insights on biomass pretreatment and conversion process and develop effective biomass conversion strategies. Even more broadly, this can provide additional opportunities for better understanding the role of structure and reaction kinetics in porous materials such as catalyst pellets and their wide-ranging applications.