A comparison of machine learning algorithms for estimation of higher heating values of biomass and fossil fuels from ultimate analysis
•HHV of fuels are estimated from ultimate analysis on a dry, ash-free basis.•Performance of several machine learning algorithms are evaluated.•Van Krevelen diagram of several classes of fuels was plotted with a large dataset.•The proposed RFR and DTR models yielded the best results for HHV estimatio...
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| Veröffentlicht in: | Fuel (Guildford) Jg. 320; S. 123971 |
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| Format: | Journal Article |
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Elsevier Ltd
15.07.2022
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| ISSN: | 0016-2361, 1873-7153 |
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| Abstract | •HHV of fuels are estimated from ultimate analysis on a dry, ash-free basis.•Performance of several machine learning algorithms are evaluated.•Van Krevelen diagram of several classes of fuels was plotted with a large dataset.•The proposed RFR and DTR models yielded the best results for HHV estimation.
Higher heating value (HHV) is one of the most important parameters to consider while obtaining energy efficiently from fuels. It provides means to estimate the quality of the fuel. However, measuring the HHV of fuels requires advanced devices, is expensive and time consuming. Thus, sufficient estimation of the HHV is crucial for development of fuel technologies and numerous studies have been performed about this subject. In this study, several machine learning algorithms (DTR, SVR, GPR, RFR, Multi-Linear Regressions, and Polynomial Regression) were utilized to construct models for estimation of the HHV from the largest open dataset of ultimate analysis of fuels on a dry, ash-free basis. The mathematical analysis is conducted for numerous different solid, liquid, and gaseous fuels such as biomass, biochar, municipal solid waste, kerosene, gasoline, fuel oil, algae, natural gas etc. 10-fold cross validation method was used to assess the validity of the constructed models in an unbiased manner. The R2, adjusted R2, RMSE, N-RMSE, and AAE values of each model were computed for performance evaluations. Finally, the results were compared with the literature, and advantages and disadvantages of each method were discussed in terms of both computational complexity and prediction accuracy. The RFR and DTR models performed exceptionally well in estimation of HHV of all classes of fuels with R2 values of 0.9814 and 0.9664, respectively. In addition, the statistical values of the ultimate analysis for each constructed class of fuel are investigated for each class of fuel and an extensive Krevelen diagram was produced from the largest dataset to date, which illustrated the relationships between the atomic O/C ratio and the atomic H/C ratio of the fuels. |
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| AbstractList | •HHV of fuels are estimated from ultimate analysis on a dry, ash-free basis.•Performance of several machine learning algorithms are evaluated.•Van Krevelen diagram of several classes of fuels was plotted with a large dataset.•The proposed RFR and DTR models yielded the best results for HHV estimation.
Higher heating value (HHV) is one of the most important parameters to consider while obtaining energy efficiently from fuels. It provides means to estimate the quality of the fuel. However, measuring the HHV of fuels requires advanced devices, is expensive and time consuming. Thus, sufficient estimation of the HHV is crucial for development of fuel technologies and numerous studies have been performed about this subject. In this study, several machine learning algorithms (DTR, SVR, GPR, RFR, Multi-Linear Regressions, and Polynomial Regression) were utilized to construct models for estimation of the HHV from the largest open dataset of ultimate analysis of fuels on a dry, ash-free basis. The mathematical analysis is conducted for numerous different solid, liquid, and gaseous fuels such as biomass, biochar, municipal solid waste, kerosene, gasoline, fuel oil, algae, natural gas etc. 10-fold cross validation method was used to assess the validity of the constructed models in an unbiased manner. The R2, adjusted R2, RMSE, N-RMSE, and AAE values of each model were computed for performance evaluations. Finally, the results were compared with the literature, and advantages and disadvantages of each method were discussed in terms of both computational complexity and prediction accuracy. The RFR and DTR models performed exceptionally well in estimation of HHV of all classes of fuels with R2 values of 0.9814 and 0.9664, respectively. In addition, the statistical values of the ultimate analysis for each constructed class of fuel are investigated for each class of fuel and an extensive Krevelen diagram was produced from the largest dataset to date, which illustrated the relationships between the atomic O/C ratio and the atomic H/C ratio of the fuels. Higher heating value (HHV) is one of the most important parameters to consider while obtaining energy efficiently from fuels. It provides means to estimate the quality of the fuel. However, measuring the HHV of fuels requires advanced devices, is expensive and time consuming. Thus, sufficient estimation of the HHV is crucial for development of fuel technologies and numerous studies have been performed about this subject. In this study, several machine learning algorithms (DTR, SVR, GPR, RFR, Multi-Linear Regressions, and Polynomial Regression) were utilized to construct models for estimation of the HHV from the largest open dataset of ultimate analysis of fuels on a dry, ash-free basis. The mathematical analysis is conducted for numerous different solid, liquid, and gaseous fuels such as biomass, biochar, municipal solid waste, kerosene, gasoline, fuel oil, algae, natural gas etc. 10-fold cross validation method was used to assess the validity of the constructed models in an unbiased manner. The R2, adjusted R2, RMSE, N-RMSE, and AAE values of each model were computed for performance evaluations. Finally, the results were compared with the literature, and advantages and disadvantages of each method were discussed in terms of both computational complexity and prediction accuracy. The RFR and DTR models performed exceptionally well in estimation of HHV of all classes of fuels with R2 values of 0.9814 and 0.9664, respectively. In addition, the statistical values of the ultimate analysis for each constructed class of fuel are investigated for each class of fuel and an extensive Krevelen diagram was produced from the largest dataset to date, which illustrated the relationships between the atomic O/C ratio and the atomic H/C ratio of the fuels. |
| ArticleNumber | 123971 |
| Author | Yaka, Havva Insel, Mert Akin Sadikoglu, Hasan Yucel, Ozgun |
| Author_xml | – sequence: 1 givenname: Havva surname: Yaka fullname: Yaka, Havva organization: Yildiz Technical University, Department of Chemical Engineering, 34210 Esenler/İstanbul, Turkey – sequence: 2 givenname: Mert Akin surname: Insel fullname: Insel, Mert Akin organization: Yildiz Technical University, Department of Chemical Engineering, 34210 Esenler/İstanbul, Turkey – sequence: 3 givenname: Ozgun surname: Yucel fullname: Yucel, Ozgun email: yozgun@gtu.edu.tr organization: Gebze Technical University, Department of Chemical Engineering, 41400 Gebze/Kocaeli, Turkey – sequence: 4 givenname: Hasan surname: Sadikoglu fullname: Sadikoglu, Hasan organization: Yildiz Technical University, Department of Chemical Engineering, 34210 Esenler/İstanbul, Turkey |
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| Snippet | •HHV of fuels are estimated from ultimate analysis on a dry, ash-free basis.•Performance of several machine learning algorithms are evaluated.•Van Krevelen... Higher heating value (HHV) is one of the most important parameters to consider while obtaining energy efficiently from fuels. It provides means to estimate the... |
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| SubjectTerms | Algae Algorithms Biomass Calorific value Charcoal Computer applications Datasets Fossil fuels Fuel oils Fuel technology Gaseous fuels Gasoline Heating Higher heating value Learning algorithms Machine learning Mathematical models Municipal solid waste Municipal waste management Natural gas Performance evaluation Polynomials Regression Regression analysis Solid waste management Statistical analysis |
| Title | A comparison of machine learning algorithms for estimation of higher heating values of biomass and fossil fuels from ultimate analysis |
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