Machine Learning Reduced Order Model for Cost and Emission Assessment of a Pyrolysis System

Olafasakin, Olumide; Chang, Yahan; Passalacqua, Alberto; Subramaniam, Shankar; Brown, Robert C.; Mba Wright, Mark

doi:10.1021/acs.energyfuels.1c00490

Title: Machine Learning Reduced Order Model for Cost and Emission Assessment of a Pyrolysis System

Journal Article · 01 June 2021 · Energy and Fuels

DOI: https://doi.org/10.1021/acs.energyfuels.1c00490 · OSTI ID:1855239

Olafasakin, Olumide ^[1]; Chang, Yahan ^[2]; Passalacqua, Alberto ^[2];

^[2];

^[2]

Iowa State Univ., Ames, IA (United States); Iowa State University
Iowa State Univ., Ames, IA (United States)

Biomass pyrolysis is a promising approach for producing economic and environmentally-friendly fuels and bioproducts. Biomass pyrolysis experiments show that feedstock properties have a significant impact on product yields and composition. Scientists are developing detailed chemical reaction mechanisms to capture the relationships between biomass composition and pyrolysis yields. These mechanisms can be computationally intensive. In this study, we investigate the use of a machine learning reduced order model (ROM) for assessing the costs and emissions of a pyrolysis biorefinery. Here, we developed a Kriging-based ROM to predict pyrolysis yields of 314 feedstock samples based on the results of a detailed chemical kinetic pyrolysis mechanism. The ROM is integrated into a chemical process model for calculating mass and energy yields in a commercial-scale (2000 tonne/day) biorefinery. The ROM estimated biofuel yields of 65 to 130 gallons per ton of dry biomass. This results in biofuel minimum fuel-selling prices of $2.62 to $5.43 per gallon and emissions of -13.62 to 145 kg of CO₂ per MJ. The ROM achieved an average mean square error of 1.8e-20 and a mean absolute error of 0.53%. These results suggest that ROMs can facilitate rapid feedstock screening for biorefinery systems.

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Research Organization:: Iowa State Univ., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0008326

OSTI ID:: 1855239

Journal Information:: Energy and Fuels, Journal Name: Energy and Fuels Journal Issue: 12 Vol. 35; ISSN 0887-0624

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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