Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value
The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (...
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| Published in | Biofuels, bioproducts and biorefining Vol. 14; no. 6; pp. 1286 - 1295 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.11.2020
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-104X 1932-1031 |
| DOI | 10.1002/bbb.2140 |
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| Abstract | The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (SGD) in a machine‐learning environment were used as novel methods to predict the HHV of biomass. The basis of the model was 78 lines of combined proximate and ultimate analysis data. The LRA model was observed to be more accurate. The testing for both models was done by stratified cross‐validation, stratified shuffle splits, and no sampling tests. The root mean square error (RMSE) of the LRA and SGD models was 8.151 and 21.65 kJ kg−1 and the mean absolute error (MAE) was 6.823 and 13.87 for the stratified shuffle split (ten random samples with 75% data). The coefficient of determination for both models was >0.999 in all cases. The study observed that LRA and SGD are among the most accurate artificial intelligence models for the prediction of biomass HHV. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd |
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| AbstractList | The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (SGD) in a machine‐learning environment were used as novel methods to predict the HHV of biomass. The basis of the model was 78 lines of combined proximate and ultimate analysis data. The LRA model was observed to be more accurate. The testing for both models was done by stratified cross‐validation, stratified shuffle splits, and no sampling tests. The root mean square error (RMSE) of the LRA and SGD models was 8.151 and 21.65 kJ kg −1 and the mean absolute error (MAE) was 6.823 and 13.87 for the stratified shuffle split (ten random samples with 75% data). The coefficient of determination for both models was >0.999 in all cases. The study observed that LRA and SGD are among the most accurate artificial intelligence models for the prediction of biomass HHV. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (SGD) in a machine‐learning environment were used as novel methods to predict the HHV of biomass. The basis of the model was 78 lines of combined proximate and ultimate analysis data. The LRA model was observed to be more accurate. The testing for both models was done by stratified cross‐validation, stratified shuffle splits, and no sampling tests. The root mean square error (RMSE) of the LRA and SGD models was 8.151 and 21.65 kJ kg−1 and the mean absolute error (MAE) was 6.823 and 13.87 for the stratified shuffle split (ten random samples with 75% data). The coefficient of determination for both models was >0.999 in all cases. The study observed that LRA and SGD are among the most accurate artificial intelligence models for the prediction of biomass HHV. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (SGD) in a machine‐learning environment were used as novel methods to predict the HHV of biomass. The basis of the model was 78 lines of combined proximate and ultimate analysis data. The LRA model was observed to be more accurate. The testing for both models was done by stratified cross‐validation, stratified shuffle splits, and no sampling tests. The root mean square error (RMSE) of the LRA and SGD models was 8.151 and 21.65 kJ kg−1 and the mean absolute error (MAE) was 6.823 and 13.87 for the stratified shuffle split (ten random samples with 75% data). The coefficient of determination for both models was >0.999 in all cases. The study observed that LRA and SGD are among the most accurate artificial intelligence models for the prediction of biomass HHV. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd |
| Author | Marques, Gonçalo Ighalo, Joshua O. Adeniyi, Adewale George |
| Author_xml | – sequence: 1 givenname: Joshua O. orcidid: 0000-0002-8709-100X surname: Ighalo fullname: Ighalo, Joshua O. email: oshea.ighalo@yahoo.com organization: Nnamdi Azikiwe University – sequence: 2 givenname: Adewale George orcidid: 0000-0001-6615-5361 surname: Adeniyi fullname: Adeniyi, Adewale George email: adeniyi.ag@unilorin.edu.ng organization: University of Ilorin – sequence: 3 givenname: Gonçalo surname: Marques fullname: Marques, Gonçalo email: goncalosantosmarques@gmail.com organization: University of Beira Interior |
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| Snippet | The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed... |
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| SubjectTerms | Algorithms Artificial intelligence Biomass Calorific value Data analysis Heating HHV Learning algorithms linear regression Machine learning Regression analysis Root-mean-square errors School environment stochastic gradient descent |
| Title | Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value |
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