Data-driven acoustic measurement of moisture content in flowing biomass

Greenhall, J.; Pantea, C.; Vakhlamov, P.; Davis, E. S.; Semelsberger, T.

doi:10.1016/j.mlwa.2023.100476

Title: Data-driven acoustic measurement of moisture content in flowing biomass

Journal Article · 08 June 2023 · Machine Learning with Applications

DOI: https://doi.org/10.1016/j.mlwa.2023.100476 · OSTI ID:2005842

^[1]; Pantea, C. ^[1]; Vakhlamov, P. ^[1]; Davis, E. S. ^[1]; Semelsberger, T. ^[1]

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001; EE0034978

OSTI ID:: 2005842

Report Number(s):: LA-UR-23-21176

Journal Information:: Machine Learning with Applications, Vol. 13; ISSN 2666-8270

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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