Multiscale Equation-Oriented Optimization Decreases the Carbon Intensity of Shale Gas to Liquid Fuel Processes

Shale gas is revolutionizing the U.S. energy and chemical
commodity
landscape and can ease the transition to a sustainable decarbonized
economy. This work develops an equation-oriented (EO) multiscale modeling
framework using the open-source IDAES-PSE platform that tractably
incorporates microkinetic detail in process design via reduced-order
kinetic (ROK) models. Using multiobjective optimization with embedded
heat integration and life-cycle analysis, we simultaneously minimize
the minimum selling price of liquid hydrocarbons (e.g., liquid fuels/additives
from shale gas) and process emissions (via a CO2 tax).
Optimization reduces greenhouse gas emissions per MJ of fuel produced
by over 35% compared to the literature and achieves a carbon efficiency
of 87%. The optimizer changes the recycling rate, temperatures, and
pressures to mitigate the effect of ROK model-form uncertainty on
product portfolio predictions. Moreover, we show that the optimal
process design is insensitive to changing CO2 tax rates.
Finally, the EO framework enables a fast sensitivity analysis of shale
gas composition variability across 12 regions of the Eagle Ford basin.
These results highlight the benefits of the open-source EO framework:
fast, scalable, customized, and reproducible system analysis and optimization
for sustainable energy technologies beyond shale utilization.