Start-up, shutdown, and transition timescale analysis in biomass reactor operations

Kung, Kevin S.; Thengane, Sonal K.; Ghoniem, Ahmed F.; Lim, C. Jim; Cao, Yankai; Sokhansanj, S.

doi:10.1016/j.energy.2022.123448

Cited by 3 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In lieu of directly measuring a given reactor's temperature profile in detail, another potentially viable approach is to examine the distribution of key features of the output product as a proxy for the degree of inhomogeneity inside the reaction chamber. In the case of biomass torrefaction, for example, we could randomly sample torrefied biomass particles from the output stream and understand how they vary from one another in key thermal and chemical parameters as an indication of the spread in the temperature severity of the treatment within the reaction chamber 16–19,70 . Thompson et al 5 .…”

Section: Introductionmentioning

confidence: 99%

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

Watts

Potter²,

Mohan³

et al. 2023

Biofuels Bioprod Bioref

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%