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A Machine Learning Approach for Biomass Characterization
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-3802-4721
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. Malardalen Univ, Future Energy Ctr, Sch Business Soc & Engn, SE-72123 Vasteras, Sweden.;Malarenergi AB, Sjohagsvagen 3, S-72103 Vasteras, Sweden..ORCID-id: 0000-0002-9508-1733
Vise andre og tillknytning
2019 (engelsk)Inngår i: INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS / [ed] Yan, J Yang, HX Li, H Chen, X, ELSEVIER SCIENCE BV , 2019, s. 1279-1287Konferansepaper, Publicerat paper (Fagfellevurdert)
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 (SGi) 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.

sted, utgiver, år, opplag, sider
ELSEVIER SCIENCE BV , 2019. s. 1279-1287
Serie
Energy Procedia, ISSN 1876-6102 ; 158
Emneord [en]
Artificial Neural Network, Chemometrics, Gaussian Process Regression, Near Infrared Spectroscopy, Multiplicative Scatter Correction, Standard Normal Variate, Support Vector Regression, Partial Least Squares, Savitzky-Golay derivatives
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Identifikatorer
URN: urn:nbn:se:mdh:diva-44835DOI: 10.1016/j.egypro.2019.01.316ISI: 000471031701100Scopus ID: 2-s2.0-85063865772OAI: oai:DiVA.org:mdh-44835DiVA, id: diva2:1336956
Konferanse
10th International Conference on Applied Energy (ICAE), AUG 22-25, 2018, Hong Kong, HONG KONG
Tilgjengelig fra: 2019-07-11 Laget: 2019-07-11 Sist oppdatert: 2019-10-14bibliografisk kontrollert

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