Quantitative Determination of Biomass-Derived Renewable Carbon in Fuels from Coprocessing of Bio-Oils in Refinery Using a Stable Carbon Isotopic Approach
2020
Li, Zheng-Hua | Wang, Huamin | Magrini, Kimberly A. | Lee, James Edward | Geeza, Thomas | Maltsev, Oleg Vitalivich | Helper, Jacob Pierce
Increasing renewable carbon incorporation into conventional fuels through coprocessing with vacuum gas oil (VGO, a petroleum refining feedstock) is a critical step in biofuel development, scaling-up, adoption, and associated GHG reduction. Optimization of the coprocessing parameters maximizes incorporation of the renewable carbon in the fuel products. Quantitative determination of the renewable carbon content in the coprocessed products provides direct evaluation of these parameters. The coprocessing bio-oil with VGO through hydrocracking (HC) or fluid catalytic cracking (FCC) system resulted in carbon isotopic fractionation that prevented the direct use of the isotope-mixing model for quantifying the renewable carbon. Here, we report an algorithm of using a stable carbon isotope approach to quantify the renewable carbon content in coprocessing biofuel products through high-precision δ¹³C analysis. A controlled experiment carried out by blending a fossil diesel (−29.013‰) with a biodiesel (−30.099‰) at various blending levels up to 98.0/2.0 wt % is presented and has demonstrated the applicability of this approach. The carbon isotope fractionation factors for the bio-oil coprocessing were obtained by using a ¹⁴C-derived isotope-mixing model. The δ¹³C method was tested by coprocessing ¹³C-labeled biocrude and natural woody biomass-derived fast pyrolysis (FP) and catalytic fast pyrolysis (CFP) bio-oils with VGO. The results were verified by ¹⁴C accelerator mass spectrometry (AMS) method (ASTM-D6866) and compared with the yield mass balance method. Strong agreement between δ¹³C and ¹⁴C AMS methods demonstrated the applicability of the δ¹³C method to quantify renewable carbon content in coprocessing fuel products and guide the coprocessing optimization.
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