Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Watts, Jordan; Potter, Adam; Mohan, Vidyut; Kumari, Pratima; Thengane, Sonal K.; Sokhansanj, Shahabaddine; Cao, Yankai; Kung, Kevin S.

doi:10.1002/bbb.2504

Title: Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Journal Article · 09 May 2023 · Biofuels, Bioproducts & Biorefining

DOI: https://doi.org/10.1002/bbb.2504 · OSTI ID:1973303

^[1]; Potter, Adam ^[2]; Mohan, Vidyut ^[2]; Kumari, Pratima ^[3];

^[4]; Sokhansanj, Shahabaddine ^[5]; Cao, Yankai ^[5];

^[6]

Takachar Limited Boston MA USA, Department of Electrical Engineering Brown University Providence RI USA
Takachar Limited Boston MA USA
University of California San Francisco San Francisco CA USA
Department of Hydro and Renewable Energy Indian Institute of Technology Roorkee Roorkee India
Department of Chemical and Biological Engineering The University of British Columbia Vancouver BC Canada
Takachar Limited Boston MA USA, Department of Chemical and Biological Engineering The University of British Columbia Vancouver BC Canada

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

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Research Organization:: Takachar Limited, Boston, MA (United States)

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

Grant/Contract Number:: SC0021836

OSTI ID:: 1973303

Journal Information:: Biofuels, Bioproducts & Biorefining, Journal Name: Biofuels, Bioproducts & Biorefining Journal Issue: 5 Vol. 17; ISSN 1932-104X

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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Title: Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

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