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Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach
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. Mälarenergi AB, Sweden.ORCID-id: 0000-0002-9508-1733
Vise andre og tillknytning
2019 (engelsk)Inngår i: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2019, Vol. 158, 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 (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.

sted, utgiver, år, opplag, sider
2019. Vol. 158, s. 1279-1287
Serie
Energy Procedia, ISSN 1876-6102
Emneord [en]
Artificial Neural Netwrok; Chemometrics; Gaussian Process Regression; Multiplicative Scatter Correction; Standard Normal Variate; Support Vector Regression; Partial Least Squares; Savitzky-Golay derivatives
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
URN: urn:nbn:se:mdh:diva-40396DOI: 10.1016/j.egypro.2019.01.316ISI: 471031701100Scopus ID: 2-s2.0-85063865772OAI: oai:DiVA.org:mdh-40396DiVA, id: diva2:1240381
Konferanse
International Conference on Applied Energy, 2018
Prosjekter
FUDIPO
Forskningsfinansiär
EU, Horizon 2020, 723523Tilgjengelig fra: 2018-08-21 Laget: 2018-08-21 Sist oppdatert: 2020-03-16bibliografisk kontrollert

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Ahmed, Mobyen UddinTomas Aparicio, ElenaBarua, ShaibalSkvaril, JanZambrano, Jesus

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