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A machine learning ...
A machine learning approach for biomass characterization
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- Ahmed, Mobyen Uddin, Dr, 1976- (author)
- Mälardalens universitet,Inbyggda system
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- Andersson, Peter (author)
- Mälardalen University, Sweden,Mälardalens högskola, Inbyggda system
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- Andersson, Tim (author)
- Mälardalens universitet,Inbyggda system
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- Tomas Aparicio, Elena, 1976- (author)
- Mälardalens universitet,Framtidens energi,Mälarenergi AB, Sweden
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- Baaz, Hampus (author)
- Mälardalen University, Sweden,Mälardalens högskola, Inbyggda system
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- Barua, Shaibal (author)
- Mälardalens universitet,RISE,SICS,Mälardalen University, Sweden,Inbyggda system,RISE SICS Västerås, Sweden
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- Bergström, Albert (author)
- Mälardalen University, Sweden,Mälardalens högskola, Inbyggda system
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- Bengtsson, Daniel (author)
- Mälardalen University, Sweden,Mälardalens högskola, Inbyggda system
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- Orisio, Daniele (author)
- State Institute of Higher Education "Guglielmo Marconi", Italy,State Inst Higher Educ Guglielmo Marconi, Dalmine, Italy.
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- Skvaril, Jan, 1982- (author)
- Mälardalens universitet,Framtidens energi
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- Zambrano, Jesus (author)
- Mälardalens universitet,Framtidens energi
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(creator_code:org_t)
- Elsevier Ltd, 2019
- 2019
- English.
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In: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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- By the author/editor
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Ahmed, Mobyen Ud ...
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Andersson, Peter
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Andersson, Tim
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Tomas Aparicio, ...
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Baaz, Hampus
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Barua, Shaibal
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Bergström, Alber ...
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Bengtsson, Danie ...
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Orisio, Daniele
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Skvaril, Jan, 19 ...
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Zambrano, Jesus
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- About the subject
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Mechanical Engin ...
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and Energy Engineeri ...
- Articles in the publication
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Energy Procedia
- By the university
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RISE
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Mälardalen University