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A Machine Learning Approach for Biomass Characterization
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Mälarenergi AB, Sweden.ORCID iD: 0000-0002-9508-1733
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2019 (English)In: Energy Procedia, ISSN 1876-6102, p. 1279-1287Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2019. p. 1279-1287
Keywords [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
National Category
Energy Engineering
Identifiers
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
Conference
10th International Conference on Applied Energy (ICAE), AUG 22-25, 2018, Hong Kong, HONG KONG
Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2023-08-28Bibliographically approved

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Ahmed, Mobyen UddinAndersson, TimTomas Aparicio, ElenaBarua, ShaibalSkvaril, JanZambrano, Jesus

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Ahmed, Mobyen UddinAndersson, PeterAndersson, TimTomas Aparicio, ElenaBaaz, HampusBarua, ShaibalBergström, AlbertBengtsson, DanielSkvaril, JanZambrano, Jesus
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