A separations and purification process for improving yields and meeting fuel contaminant specifications for high-octane gasoline produced from dimethyl-ether over a Cu/BEA catalyst

We have been developing a three-step conversion of biomass-derived syngas to methanol to dimethyl-ether (DME) to non-aromatic hydrocarbons for use as high-octane gasoline and sustainable aviation fuel. This process produces branched alkanes from DME using a Cu/BEA catalyst and is a promising alternative to other syngas conversion processes such as Fischer-Tropsch to linear alkanes and traditional ZSM-5 catalyzed methanol to aromatic gasoline. In this short article we describe some advances in our understanding related to separation and purification via the use of more detailed experimental speciation in an updated process model involving multiple-phase equilibrium-based separation steps. The primary modeled reactor outlet constituents (and wt%) are: C3 and lighter hydrocarbon gases (11.1%), C4s (54.5%), H-2 (1.2%), CO2 (2.9%), water (5.0%), unreacted DME (16.5%), methanol (2.3%) and C5+ hydrocarbons (6.4%). Dimethyl-ether (the primary reactant) and H-2 recycling and reuse are important for the overall process efficiency, and the recycling of C4s is important to increase the C5+ yield via reactivation and homologation. Thus H-2, C4s and DME are targeted for recycling, while methanol and water need to be removed from the product to conform with fuel specifications. Model predictions from Aspen Plus using the NRTL-RK property method indicate a fuel composition with a C5+ content of 97.1 wt%, with the minor constituents 2.4 wt% C4s, 0.3 wt% methanol, 0.1 wt% DME, 0.03 wt% water and 0.01 wt% C3s. These ranges of minor components conform with fuel quality requirements, and the modeled product is amenable for unconstrained blending to boost gasoline octane ratings. (c) 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

Automated summary

The study discusses the conversion of biomass-derived syngas to non-aromatic hydrocarbons for fuel, highlighting the importance of experimental analysis and modeling predictions.