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- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A machine learning approach for biomass characterization
- 2019
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Sevcik, Martin, et al.
(författare)
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Applications of hyperspectral imaging and machine learning methods for real-time classification of waste stream components
- 2019
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Konferensbidrag (refereegranskat)abstract
- Near-infrared (NIR) hyperspectral imaging (HSI) was applied together with machine learning methods to enable classification of typical municipal solid waste (MSW) components such as paper, biomass, food residues, plastics, textile and incombustibles. Classification models were developed using partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and radial-basis neural network (RBNN). The overall accuracy of SVM model calculated from classification sensitivity was 85% in prediction pixel by pixel for external sample set. The model outperformed other models in identifying incombustible material but it had higher computational time requirements. The accuracy of RBNN model reached 85% in prediction while being approx. 10 times faster. Minimum computational time was required by PLS-DA model reaching lower accuracy of 81% in prediction. The result indicate that developed models can be successfully used for real-time MSW component classification. NIR hyperspectral imaging coupled with machine learning methods has a large potential to be used for on-line material identification at waste sorting facilities or for pre-sorting at waste-to-energy powerplants.
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