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Data-driven acoustic measurement of moisture content in flowing biomass
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.
Interdisciplinary strategies to enable data-driven plant breeding in a changing climate
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Data-driven evaluations of Euclidean windows to scrutinize hadronic vacuum polarization
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Uncertainty quantification of fluidized beds using a data-driven framework
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A Bibliometric Analysis of Physics-Based and Data-Driven Hybrid Modeling
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Determination of the single droplet post-impingement pattern on a dry wall: A data-driven approach
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Data-driven estimates of undocumented orphan oil and gas wells with implications to the environment
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Data-driven model for solar irradiation based on satellite observations
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New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities
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Coarse Graining Turbulence: Modeling and Data-Driven Approaches and their Applications
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