Water electrolysis using renewable energy inputs is being actively pursued as a green route for hydrogen production. However, it is limited by the high energy consumption due to the sluggish...
This study investigates the economic and environmental benefits of integrating hydroprocessing, fermentation, and anaerobic digestion into a pyrolysis refinery. Two scenarios were developed for upgrading and/or utilizing the primary products of pyrolysis (bio-oil, gas, and char). The first (hydroprocessing) scenario hydroprocesses whole bio-oil into gasoline and diesel. The second (fractionation) scenario fractionates bio-oil into sugars for fermentation to cellulosic ethanol and residual phenolic oil as the primary product. Both scenarios use the gaseous product of pyrolysis for process heat in the plant and employ biochar to enhance anaerobic digestion of manure for power generation. The fast pyrolysis plant processes 2000 ton/day of corn stover while the anaerobic digester employs 430 ton/day of manure to generate power. The hydroprocessing scenario produces gasoline at a minimum fuel-selling price (MFSP) of $2.77 per gallons of gasoline while the fractionation scenario produces ethanol and phenolic oils (diesel) as a transportation fuel for $1.2 per gallon ($1.41 per GGE). Sensitivity analysis indicates that the MFSP for both scenarios is highly sensitive to the fixed capital cost. Fixed capital costs for the hydroprocessing and fractionation scenarios were estimated to be $643 and $288 million, respectively. Fuel production rates for the hydroprocessing and fractionation scenarios are 60.5 and 16 million GGE per year, respectively. Life cycle greenhouse gas emissions were calculated as −9.6 and −16.6 g CO2,eq per MJ for the hydroprocessing and fractionation scenarios, respectively. LCA emissions are sensitive to byproduct credits derived from biochar sequestration and power generation. This study shows that both systems produce transportation fuels at competitive market prices with an additional reduction in atmospheric CO2 levels compared to fossil fuel sources.
Carbon-negative energy removes carbon dioxide from the atmosphere while providing energy to society. The pyrolysis-biochar platform achieves carbon-negative energy by producing bio-oil as an energy product and biochar as a...
Feedstock properties impact the economic feasibility and sustainability of biorefinery systems. Scientists have developed pyrolysis kinetics, process, and assessment models that estimate the costs and greenhouse gas (GHG) emissions of various biorefineries. Previous studies demonstrate that feedstock properties have a significant influence on product costs and lifecycle emissions. However, feedstock variability remains a challenge due to a large number of possible feedstock property combinations and limited public availability of feedstock composition data. Here, we demonstrate the use of machine learning (ML) models to generate large feedstock sample data from a smaller sample set for sustainability assessment of biorefinery systems. This study predicts the impact of feedstock properties on the profitability and sustainability of a lignocellulosic biomass autothermal pyrolysis (ATP) biorefinery producing sugar, phenolic oil, and biochar. Generative Adversarial Networks and Kernel Density Estimation machine learning models are used to generate 3,000 feedstock samples of diverse biochemical compositions. Techno-economic and lifecycle assessments estimated that the ATP minimum sugar selling price (MSSP) ranges between $66/metric ton (MT) and $280/MT, and the greenhouse gas (GHG) range from a net negative GHG emission(s) of −0.56 to −0.74 kg CO2e/kg lignocellulosic biomass processed. These results show the potential of ML to enhance sustainability analyses by replacing Monte Carlo-type approaches to generate large feedstock composition datasets that are representative of empirical data.
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