(393f) Optimal Biomass Conversion Technology Investments Considering Uncertainty and Environmental Policy

The replacement of petrochemical feedstocks with biobased chemicals is a significant area of research aimed at reducing greenhouse gas emissions and creating a sustainable economy¹. Despite extensive research on converting biomass into drop-in or alternative chemicals for modern-day use, such as bioethanol or polyethylene furanoate (PEF) replacing polyethylene terephthalate (PET), commercialization of biomass-based processes often fails^2-4. The lack of commercialization is partly due to the risk associated with unproven technologies and unknown market conditions for these new feedstock streams and product demands^{4, 5}. A significant challenge that must be overcome is the economic hurdle of competing with the well-established petrochemical industry. Policymakers can play a role in facilitating this transition toward a green economy by establishing carbon emissions policies. Certain countries have already established such policies, and states such as California have begun implementing these policies to reduce emissions and incentivize greener processes⁶.

This work aimed to analyze the changes in technological investment decisions made across various carbon policies: carbon tax, cap and trade, cap, and offset⁷. For each scenario, a Pareto-optimal curve was generated considering economic and environmental metrics, such as profit and Global Warming Potential. To mitigate uncertainty, risk metrics were considered to understand the trade-offs between risk, profit, and emissions. This framework aims to assist decision-makers in assessing an extensive range of technologies for making investments while considering policy and uncertainty.

In this work, a superstructure consisting of biomass transformation pathways obtained from literature as well as separations characterized by shortcut methods and surrogate models was developed. This superstructure was utilized in a two-stage mixed integer linear stochastic programming problem. The two-stage formulation allows us to capture the first stage “here-and-now” decisions, decisions made prior to the realization of uncertainty, such as investments towards process units and their capacities, and second stage “wait and see” decisions, operating and production levels under the face of different realizations of uncertainty. Uncertainties in biomass feedstock supply and cost, product demand and price, and environmental emissions were considered. The finance literature has developed many risk metrics, such as conditional value at risk (CVaR), downside risk, variance, and mean absolute deviation^{8, 9}. These risk metrics capture one aspect of the cost distribution, such as tail-end behavior for CVaR and the width of the distribution of mean absolute deviation. Risk metrics can be combined to obtain better control over multiple attributes of the profit distributions and manage uncertainty. Finally, rigorous process simulation in Aspen Plus V12 was used to validate the objective functions and model for accuracy¹⁰.

References

1. Dahiya, S.; Katakojwala, R.; Ramakrishna, S.; Mohan, S. V. Biobased Products and Life Cycle Assessment in the Context of Circular Economy and Sustainability. Materials Circular Economy 2020, 2 (1). DOI: 10.1007/s42824-020-00007-x.
2. Zhao, Y.; Damgaard, A.; Christensen, T. H. Bioethanol from corn stover – a review and technical assessment of alternative biotechnologies. Progress in Energy and Combustion Science 2018, 67, 275-291. DOI: 10.1016/j.pecs.2018.03.004
3. de Jong, E.; Visser, H. A.; Dias, A. S.; Harvey, C.; Gruter, G.-J. M. The Road to Bring FDCA and PEF to the Market. Polymers 2022, 14 (5), 943. DOI: 10.3390/polym14050943
4. Saini, R.; Osorio-Gonzalez, C. S.; Hegde, K.; Brar, S. K.; Magdouli, S.; Vezina, P.; Avalos-Ramirez, A. Lignocellulosic Biomass-Based Biorefinery: an Insight into Commercialization and Economic Standout. Current Sustainable/Renewable Energy Reports 2020, 7 (4), 122-136. DOI: 10.1007/s40518-020-00157-1.
5. Buchner, G. A.; Zimmermann, A. W.; Hohgräve, A. E.; Schomäcker, R. Techno-economic Assessment Framework for the Chemical Industry—Based on Technology Readiness Levels. Ind Eng Chem Res 2018, 57 (25), 8502-8517. DOI: 10.1021/acs.iecr.8b01248
6. Sumner, J.; Bird, L.; Dobos, H. Carbon taxes: a review of experience and policy design considerations. Climate Policy 2011, 11 (2), 922-943. DOI: 10.3763/cpol.2010.0093.
7. Marufuzzaman, M.; Eksioglu, S. D.; Huang, Y. Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment. Computers & Operations Research 2014, 49, 1-17. DOI: 10.1016/j.cor.2014.03.010.
8. You, F.; Wassick, J. M.; Grossmann, I. E. Risk management for a global supply chain planning under uncertainty: Models and algorithms. AIChE Journal 2009, 55 (4), 931-946. DOI: 10.1002/aic.11721
9. Kapsos, M.; Zymler, S.; Christofides, N.; Rustem, B. Optimizing the Omega ratio using linear programming. Journal of Computational Finance 2014, 17 (4), 49-57.
10. Aspen Plus V.12; Aspen Technology, Inc.: 2020.