Investigating the Impacts of Feedstock Variability on a Carbon-Negative Autothermal Pyrolysis System Using Machine Learning

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2022-05-09
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Ganguly, Arna
Wright, Mark Mba
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Frontiers Media S. A.
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Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
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Bioeconomy Institute
The Bioeconomy Institute at Iowa State University leads the nation and world in establishing the bioeconomy, where society obtains renewable fuel, energy, chemicals, and materials from agricultural sources. The institute seeks to advance the use of biorenewable resources for the production of fuels, energy, chemicals, and materials. The Institute will assure Iowa’s prominence in the revolution that is changing the way society obtains its essential sources of energy and carbon. This revolution will dramatically reduce our dependence on petroleum. Instead of fossil sources of carbon and energy, the bioeconomy will use biomass (including lignocellulose, starches, oils and proteins) as a renewable resource to sustain economic growth and prosperity. Agriculture will supply renewable energy and carbon to the bioeconomy while engineering will transform these resources into transportation fuels, commodity chemicals, and electric power. This transformation, however, must be done in a manner that meets our present needs without compromising those of future generations.
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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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This article is published as Ganguly A, Brown RC and Wright MM (2022) Investigating the Impacts of Feedstock Variability on a Carbon-Negative Autothermal Pyrolysis System Using Machine Learning. Front. Clim. 4:842650. DOI: 10.3389/fclim.2022.842650. Copyright 2022 Ganguly, Brown, and Wright. Attribution 4.0 International (CC BY 4.0). Posted with permission.
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