(763a) Design and Operation of Synthetic Fuels Production from Fossil and Renewable Resources

The transport sector, including road, air, and waterborne transport, contributes to slightly more than 20% of the global emissions of greenhouse gases (GHG). In 2017, about 92% of the total U.S. transportation sector needs were accounted by petroleum products [1]. According to 2018 data, the U.S. consumed an average of value of 20.5 million barrels of petroleum per day [2]. With growing concerns over expensive crude oil prices and increased scrutiny over high levels of greenhouse gas (GHG) emissions, the U.S. transportation sector faces major challenges that must be addressed through the investigation of novel processes to produce liquid fuels. While governments all over the world aim to reduce the GHG emissions from transport drastically by mid century, in the absence of vital measures, emissions are expected to continue increasing due to the increasing demand for transport [3].

One way to bring down the GHG emissions without totally replacing the transportation infrastructure is to produce low-emission fuels require using renewable energy sources such as biomass, solar, and wind energy, as well as captured carbon. While, previous multi-scale engineering work by Floudas and coworkers focused on process synthesis of transportation fuels production from hybrid feedstock routes including coal, biomass, and municipal solid waste gasification, and natural gas reforming, their efforts mainly focused on optimal steady-state designs [4-7]. Integrating other renewable resources like wind and solar to that picture is challenging since steady-state analysis lacks the scheduling aspect of dynamic operation resulting from the intermittency of wind and solar availability [8,9].

In this work, we will consider synthetic fuels production from a variety of fossil and renewable resources including natural gas, biomass, captured carbon dioxide together with wind and solar energy. The hourly, daily, and seasonal fluctuations in solar irradiation and wind speed are modeled by using an hourly discretized multi-period formulation. The formulation allows us to find the optimal process design along with the optimal operating strategy for an annual time horizon. The resulting optimization model is a mixed-integer linear programming (MILP) problem. Case studies will be presented to investigate the design and operation of process networks that (i) synthesize low-emission fuels, (ii) produce fuels that are price competitive with fossil-based counterparts, and (iii) find the optimal renewable penetration level for the investment made.

References

1. EIA, “Energy Use for Transportation”, retrieved on April 9, 2019. https://www.eia.gov/energyexplained/?page=us_energy_transportation
2. EIA, “How much oil is consumed in the United States”, retrieved on April 9, 2019. https://www.eia.gov/tools/faqs/faq.php?id=33&t=6
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