(393f) Optimal Biomass Conversion Technology Investments Considering Uncertainty and Environmental Policy
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Computing and Systems Technology Division
Sustainable Process Synthesis, Design, and Intensification
Wednesday, November 8, 2023 - 5:00pm to 5:18pm
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 âhere-and-nowâ decisions, decisions made prior to the realization of uncertainty, such as investments towards process units and their capacities, and second stage âwait and seeâ 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 deviation8, 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
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10. Aspen Plus V.12; Aspen Technology, Inc.: 2020.