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A machine learning approach for biomass characterization
Mälardalen University, Sweden.
Mälardalen University, Sweden.
Mälardalen University, Sweden.
Mälardalen University, Sweden; Mälarenergi AB, Sweden.
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2019 (English)In: Energy Procedia, Elsevier Ltd , 2019, p. 1279-1287Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.

Place, publisher, year, edition, pages
Elsevier Ltd , 2019. p. 1279-1287
Keywords [en]
Artificial Neural Network, Chemometrics, Gaussian Process Regression, Multiplicative Scatter Correction, Near Infrared Spectroscopy, Partial Least Squares, Savitzky-Golay derivatives, Standard Normal Variate, Support Vector Regression, Data handling, Gaussian distribution, Gaussian noise (electronic), Genetic algorithms, Infrared devices, Iterative methods, Learning algorithms, Least squares approximations, Light scattering, Machine learning, Moisture, Moisture determination, Neural networks, Partial least square (PLS), Savitzky-Golay, Standard normal variates, Support vector regression (SVR), Regression analysis
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-38495DOI: 10.1016/j.egypro.2019.01.316Scopus ID: 2-s2.0-85063865772OAI: oai:DiVA.org:ri-38495DiVA, id: diva2:1314806
Conference
10th International Conference on Applied Energy, ICAE 2018, 22 August 2018 through 25 August 2018
Available from: 2019-05-09 Created: 2019-05-09 Last updated: 2019-05-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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