(365e) Computer-Aided Design of Products Derived from Biomass Pyrolysis

The use of biomass to produce fuels and chemicals required for the modern world has emerged as a promising alternative to reduce our dependency on the fossil fuels which are used as the main feedstocks for the petrochemical industry. Among chemical biomass-to-fuel conversion processes, pyrolysis is considered as the cheapest conversion pathway towards liquid biofuels and it is gaining increased attention as a sustainable option for the production of bio-oil and biochemicals (Galadima and Muraza, 2015). In addition to its significant economic potential, pyrolysis technology has a high degree of flexibility as any type of biomass can be potentially used as a feedstock. Despite its advantages, the use of biomass pyrolysis to produce renewable fuels poses several challenges. The difficult characterization of highly diverse biomass resources, reaction intermediates and final products makes the commercialization of the process quite challenging. Although extensive work has been done on different pretreatment technologies (Hosseini et al., 2010) and reaction pathways of several feedstocks (Papari and Hawboldt, 2015), the upgrading of pyrolysis products into a viable product mix remains a challenge that requires further investigation.

In a recent study, Sharifzadeh et al. (2017) addressed the challenges of modeling the hydrothermal upgrading of pyrolysis oil in order to deoxygenate it and make it usable in the current energy infrastructure. In this work we develop a computer-based methodology for identifying an optimal product mix given information on the effluent from a pyrolyzer. A set of possible products is specified based on their properties and prices, and we formulate an optimization problem to identify the most valuable product portfolio. The proposed methodology builds on a general computer-aided model for designing mixtures and product blends, in which the number, identity and composition of different chemicals in the final product are optimized simultaneously using advanced optimization techniques (Jonuzaj et al., 2016, 2018). The modeling approach is demonstrated though the determination of optimal product blends from predefined feedstock.

References

Galadima, A., Muraza, O., 2015. In situ fast pyrolysis of biomass with zeolite catalysts for bioaromatics/gasoline production: A review. Energy Conversion and Management 105, 338–354.

Hosseini, S.A., Lambert, R., Kucherenko, S., Shah, N., 2010. Multiscale modeling of hydrothermal pretreatment: from hemicellulose hydrolysis to biomass size optimization. Energy Fuels 24, 4673–4380.

Jonuzaj, S., Akula, P.T., Kleniati, P.M., Adjiman, C.S., 2016. The formulation of optimal mixtures with Generalized Disjunctive Programming: A solvent design case study. AIChE Journal 62, 1616–1633.

Jonuzaj, S., Gupta, A., Adjiman, C.S., 2018. The design of optimal mixtures from atom groups using Generalized Disjunctive Programming. Computers & Chemical Engineering. URL: https://doi.org/10.1016/j.compchemeng.2018.01.016.

Papari, S., Hawboldt, K., 2015. A review on the pyrolysis of woody biomass to bio-oil: Focus on kinetic models. Renewable and Sustainable Energy Reviews 52, 1580–1595.

Sharifzadeh, M., Richard, C.J., Shah, N., 2017. Modelling the kinetics of pyrolysis oil hydrothermal upgrading based on the connectivity of oxygen atoms, quantified by ³¹P-NMR. Biomass and Bioenergy 98, 272–290.