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  • Ahmed, Mobyen Uddin,Dr,1976-Mälardalens universitet,Inbyggda system (author)

A machine learning approach for biomass characterization

  • Article/chapterEnglish2019

Publisher, publication year, extent ...

  • Elsevier Ltd,2019
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:ri-38495
  • https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-38495URI
  • https://doi.org/10.1016/j.egypro.2019.01.316DOI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44835URI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:kon swepub-publicationtype

Notes

  • 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 and genre

  • TEKNIK OCH TEKNOLOGIER Maskinteknik Energiteknik hsv//swe
  • ENGINEERING AND TECHNOLOGY Mechanical Engineering Energy Engineering hsv//eng
  • 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

Added entries (persons, corporate bodies, meetings, titles ...)

  • Andersson, PeterMälardalen University, Sweden,Mälardalens högskola, Inbyggda system(Swepub:mdh)i.u. (author)
  • Andersson, TimMälardalens universitet,Inbyggda system(Swepub:mdh)tan08 (author)
  • Tomas Aparicio, Elena,1976-Mälardalens universitet,Framtidens energi,Mälarenergi AB, Sweden(Swepub:mdh)eto01 (author)
  • Baaz, HampusMälardalen University, Sweden,Mälardalens högskola, Inbyggda system(Swepub:mdh)i.u. (author)
  • Barua, ShaibalMälardalens universitet,RISE,SICS,Mälardalen University, Sweden,Inbyggda system,RISE SICS Västerås, Sweden(Swepub:mdh)sba01 (author)
  • Bergström, AlbertMälardalen University, Sweden,Mälardalens högskola, Inbyggda system(Swepub:mdh)i.u. (author)
  • Bengtsson, DanielMälardalen University, Sweden,Mälardalens högskola, Inbyggda system(Swepub:mdh)i.u. (author)
  • Orisio, DanieleState Institute of Higher Education "Guglielmo Marconi", Italy,State Inst Higher Educ Guglielmo Marconi, Dalmine, Italy. (author)
  • Skvaril, Jan,1982-Mälardalens universitet,Framtidens energi(Swepub:mdh)jsl02 (author)
  • Zambrano, JesusMälardalens universitet,Framtidens energi(Swepub:mdh)jzo01 (author)
  • Mälardalens universitetInbyggda system (creator_code:org_t)

Related titles

  • In:Energy Procedia: Elsevier Ltd, s. 1279-12871876-6102

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