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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| Vydáno v: | Fuel (Guildford) Ročník 320; s. 123971 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Kidlington
Elsevier Ltd
15.07.2022
Elsevier BV |
| Témata: | |
| 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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| Cites_doi | 10.1007/s13399-019-00386-5 10.1016/j.wasman.2015.09.036 10.1016/j.fuel.2008.04.008 10.1080/10485252.2017.1404598 10.1016/j.fuel.2018.02.126 10.1016/j.jngse.2020.103760 10.1016/j.fuel.2010.11.031 10.1007/978-3-642-21551-3_33 10.1016/j.fuel.2020.118906 10.1016/j.fuel.2016.12.044 10.1016/j.renene.2017.02.008 10.3390/en9010028 10.1016/j.scitotenv.2014.01.001 10.1016/j.csda.2007.05.017 10.1016/j.fuel.2012.04.015 10.1016/j.patcog.2016.12.018 10.1016/j.fuel.2019.115931 10.17798/bitlisfen.315118 10.1371/journal.pone.0194889 10.1016/j.fuel.2020.117066 10.1016/j.csda.2013.09.007 10.1016/j.tjem.2018.08.001 10.1016/j.fuel.2019.116925 10.1088/1757-899X/324/1/012049 10.1016/j.fuel.2004.10.010 10.1016/S0016-2361(01)00034-5 10.1016/j.energy.2019.116077 10.1016/j.ejpe.2015.06.001 10.1016/j.fuel.2006.12.029 10.1016/S0016-2361(01)00131-4 10.1016/0144-4565(84)90003-9 10.1016/j.asr.2007.07.020 10.1016/j.joei.2016.03.002 10.1007/BF00116251 10.1016/j.cj.2016.01.008 10.1016/j.rser.2020.110237 10.1016/j.energy.2010.10.032 10.1016/j.wasman.2019.09.035 10.1016/j.commatsci.2019.109203 10.1016/j.fuel.2009.10.022 |
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| References | Anca-couce, Hochenauer, Scharler (b0020) 2021; 135 Quinlan (b0155) 1986; 1 Yi, Feng, Qin, Li (b0225) 2017; 193 Candela, Rasmussen (b0165) 2005; 6 Elmaz, Büyükçakır, Yücel, Mutlu (b0185) 2020; 266 Xing, Luo, Wang, Gao, Fan (b0045) 2019; 188 Yang, Zhang, Fan, Sun (b0190) 2021; 87 MathWorks. fspecial 2021. Majumder, Jain, Banerjee, Barnwal (b0030) 2008; 87 Birgen, Magnanelli, Carlsson, Skreiberg, Mosby, Becidan (b0180) 2021; 283 Vassilev, Baxter, Andersen, Vassileva (b0260) 2010; 89 Voyant, Muselli, Paoli, Nivet (b0090) 2011; 36 Ricci, Martínez (b0080) 2008; 52 Song, Quinton, Peng, Zhao, Ladommatos (b0255) 2016; 9 Parikh, Channiwala, Ghosal (b0065) 2007; 86 Rokach, Maimon (b0145) 2010 Shi, Mahinpey, Aqsha, Silbermann (b0245) 2016; 48 Rasmussen, Williams (b0170) 2006 Hosseinpour, Aghbashlo, Tabatabaei (b0055) 2018; 222 Elmaz, Yucel, Mutlu (b0100) 2020; 32 . Wong (b0210) 2017; 65 Rodriguez-Galiano, Mendes, Garcia-Soldado, Chica-Olmo, Ribeiro (b0205) 2014; 476–477 Jung (b0220) 2018; 30 Nhuchhen, Abdul (b0050) 2012; 99 Wang, Lu (b0085) 2018; 324 Yin (b0240) 2010; 90 Toor, Rosendahl, Hoffmann, Pedersen, Nielsen, Søgaard (b0025) 2014 Phyllis 2. Database for biomass and waste n.d. ecn.nl/phyllis2. Makridakis, Spiliotis, Assimakopoulos, Hernandez Montoya (b0160) 2018; 13 Xiong, Cui, Liu, Zhao, Hu, Hu (b0215) 2020; 171 Özkan, Işık, Günkaya, Özkan, Banar (b0140) 2019; 100 Cordero, Marquez, Rodriguez-Mirasol, Rodriguez (b0035) 2001; 80 Zhao, Zhang (b0150) 2008; 41 Zhang, Zhang, Wang (b0105) 2014; 70 Parikh, Channiwala, Ghosal (b0060) 2005; 84 Mathworks. Gaussian Process Regression Models 2021. Boumanchar, Charafeddine, Chhiti, M’hamdi Alaoui, Sahibed-dine, Bentiss (b0235) 2019; 9 Channiwala, Parikh (b0040) 2002; 81 Şenol H, Elibol EA, Açıkel Ü, Şenol M. Türkiye’de Biyogaz Üretimi İçin Başlıca Biyokütle Kaynakları. Bitlis Eren Üniversitesi Fen Bilim Derg 2017;6:81–92. 10.17798/bitlisfen.315118. Bühlmann P. Bagging, Boosting and Ensemble Methods. In: Gentle JE, Härdle WK, Mori Y, editors. Handb. Comput. Stat. Concepts Methods, Berlin, Heidelberg: Springer Berlin Heidelberg; 2012, p. 985–1022. 10.1007/978-3-642-21551-3_33. Patle, Chouhan (b0120) 2013 Toklu (b0010) 2017; 107 Dashti, Noushabadi, Raji, Razmi, Ceylan, Mohammadi (b0115) 2019; 257 Wang, Zhou, Zhu, Dong, Guo (b0200) 2016; 4 MathWorks. Understanding Support Vector Machine Regression 2021. Demirbas (b0230) 2016; 38 Qian, Li, Zhang, Wang, Hu, Cao (b0250) 2020; 265 Ghugare, Tambe (b0070) 2017; 90 (b0135) 2000 Akoglu (b0075) 2018; 18 Trimble, Van Hook, Gray (b0015) 1984; 6 Gendy, El-Shiekh, Zakhary (b0110) 2015; 24 Channiwala (10.1016/j.fuel.2022.123971_b0040) 2002; 81 Yang (10.1016/j.fuel.2022.123971_b0190) 2021; 87 Xing (10.1016/j.fuel.2022.123971_b0045) 2019; 188 Rasmussen (10.1016/j.fuel.2022.123971_b0170) 2006 Rokach (10.1016/j.fuel.2022.123971_b0145) 2010 Vassilev (10.1016/j.fuel.2022.123971_b0260) 2010; 89 Toor (10.1016/j.fuel.2022.123971_b0025) 2014 Voyant (10.1016/j.fuel.2022.123971_b0090) 2011; 36 10.1016/j.fuel.2022.123971_b0195 Shi (10.1016/j.fuel.2022.123971_b0245) 2016; 48 Majumder (10.1016/j.fuel.2022.123971_b0030) 2008; 87 Nhuchhen (10.1016/j.fuel.2022.123971_b0050) 2012; 99 Dashti (10.1016/j.fuel.2022.123971_b0115) 2019; 257 Zhao (10.1016/j.fuel.2022.123971_b0150) 2008; 41 Cordero (10.1016/j.fuel.2022.123971_b0035) 2001; 80 Akoglu (10.1016/j.fuel.2022.123971_b0075) 2018; 18 Song (10.1016/j.fuel.2022.123971_b0255) 2016; 9 Parikh (10.1016/j.fuel.2022.123971_b0065) 2007; 86 Yi (10.1016/j.fuel.2022.123971_b0225) 2017; 193 Anca-couce (10.1016/j.fuel.2022.123971_b0020) 2021; 135 Parikh (10.1016/j.fuel.2022.123971_b0060) 2005; 84 Trimble (10.1016/j.fuel.2022.123971_b0015) 1984; 6 Rodriguez-Galiano (10.1016/j.fuel.2022.123971_b0205) 2014; 476–477 Elmaz (10.1016/j.fuel.2022.123971_b0185) 2020; 266 Wang (10.1016/j.fuel.2022.123971_b0200) 2016; 4 Elmaz (10.1016/j.fuel.2022.123971_b0100) 2020; 32 Ghugare (10.1016/j.fuel.2022.123971_b0070) 2017; 90 Hosseinpour (10.1016/j.fuel.2022.123971_b0055) 2018; 222 Demirbas (10.1016/j.fuel.2022.123971_b0230) 2016; 38 10.1016/j.fuel.2022.123971_b0005 10.1016/j.fuel.2022.123971_b0125 Quinlan (10.1016/j.fuel.2022.123971_b0155) 1986; 1 Xiong (10.1016/j.fuel.2022.123971_b0215) 2020; 171 Toklu (10.1016/j.fuel.2022.123971_b0010) 2017; 107 Patle (10.1016/j.fuel.2022.123971_b0120) 2013 10.1016/j.fuel.2022.123971_b0095 10.1016/j.fuel.2022.123971_b0175 10.1016/j.fuel.2022.123971_b0130 Jung (10.1016/j.fuel.2022.123971_b0220) 2018; 30 Wong (10.1016/j.fuel.2022.123971_b0210) 2017; 65 Yin (10.1016/j.fuel.2022.123971_b0240) 2010; 90 Boumanchar (10.1016/j.fuel.2022.123971_b0235) 2019; 9 Zhang (10.1016/j.fuel.2022.123971_b0105) 2014; 70 Candela (10.1016/j.fuel.2022.123971_b0165) 2005; 6 Wang (10.1016/j.fuel.2022.123971_b0085) 2018; 324 (10.1016/j.fuel.2022.123971_b0135) 2000 Özkan (10.1016/j.fuel.2022.123971_b0140) 2019; 100 Birgen (10.1016/j.fuel.2022.123971_b0180) 2021; 283 Makridakis (10.1016/j.fuel.2022.123971_b0160) 2018; 13 Gendy (10.1016/j.fuel.2022.123971_b0110) 2015; 24 Qian (10.1016/j.fuel.2022.123971_b0250) 2020; 265 Ricci (10.1016/j.fuel.2022.123971_b0080) 2008; 52 |
| References_xml | – start-page: 189 year: 2014 end-page: 217 ident: b0025 article-title: Application of Hydrothermal Reactions to Biomass Conversion publication-title: Hydrothermal Liq Biomass – volume: 89 start-page: 913 year: 2010 end-page: 933 ident: b0260 article-title: An overview of the chemical composition of biomass publication-title: Fuel – reference: Şenol H, Elibol EA, Açıkel Ü, Şenol M. Türkiye’de Biyogaz Üretimi İçin Başlıca Biyokütle Kaynakları. Bitlis Eren Üniversitesi Fen Bilim Derg 2017;6:81–92. 10.17798/bitlisfen.315118. – volume: 171 year: 2020 ident: b0215 article-title: Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation publication-title: Comput Mater Sci – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: b0155 article-title: Induction of decision trees publication-title: Mach Learn – volume: 9 start-page: 499 year: 2019 end-page: 509 ident: b0235 article-title: Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming publication-title: Biomass Convers Biorefinery – volume: 48 start-page: 34 year: 2016 end-page: 47 ident: b0245 article-title: Characterization, thermochemical conversion studies, and heating value modeling of municipal solid waste publication-title: Waste Manag – volume: 283 year: 2021 ident: b0180 article-title: Machine learning based modelling for lower heating value prediction of municipal solid waste publication-title: Fuel – volume: 90 start-page: 1128 year: 2010 end-page: 1132 ident: b0240 article-title: Prediction of higher heating values of biomass from proximate and ultimate analyses publication-title: Fuel – reference: Mathworks. Gaussian Process Regression Models 2021. – reference: Bühlmann P. Bagging, Boosting and Ensemble Methods. In: Gentle JE, Härdle WK, Mori Y, editors. Handb. Comput. Stat. Concepts Methods, Berlin, Heidelberg: Springer Berlin Heidelberg; 2012, p. 985–1022. 10.1007/978-3-642-21551-3_33. – volume: 84 start-page: 487 year: 2005 end-page: 494 ident: b0060 article-title: A correlation for calculating HHV from proximate analysis of solid fuels publication-title: Fuel – reference: Phyllis 2. Database for biomass and waste n.d. ecn.nl/phyllis2. – volume: 324 start-page: 012049 year: 2018 ident: b0085 article-title: Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model publication-title: IOP Conf Ser: Mater Sci Eng – volume: 87 start-page: 3077 year: 2008 end-page: 3081 ident: b0030 article-title: Development of a new proximate analysis based correlation to predict calorific value of coal publication-title: Fuel – volume: 265 year: 2020 ident: b0250 article-title: Prediction of higher heating values of biochar from proximate and ultimate analysis publication-title: Fuel – volume: 107 start-page: 235 year: 2017 end-page: 244 ident: b0010 article-title: Biomass energy potential and utilization in Turkey publication-title: Renew Energy – volume: 38 start-page: 2693 year: 2016 end-page: 2697 ident: b0230 article-title: Calculation of higher heating values of fatty acids publication-title: Energy Sources, Part A Recover Util Environ Eff – volume: 9 start-page: 28 year: 2016 ident: b0255 article-title: Effects of Oxygen Content of Fuels on Combustion and Emissions of Diesel Engines publication-title: Energies – volume: 86 start-page: 1710 year: 2007 end-page: 1719 ident: b0065 article-title: A correlation for calculating elemental composition from proximate analysis of biomass materials publication-title: Fuel – start-page: 149 year: 2010 end-page: 174 ident: b0145 article-title: Classification Trees publication-title: Data Min. Knowl. Discov. Handb. – volume: 81 start-page: 1051 year: 2002 end-page: 1063 ident: b0040 article-title: A unified correlation for estimating HHV of solid, liquid and gaseous fuels publication-title: Fuel – volume: 90 start-page: 476 year: 2017 end-page: 484 ident: b0070 article-title: Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies publication-title: J Energy Inst – volume: 65 start-page: 97 year: 2017 end-page: 107 ident: b0210 article-title: Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets publication-title: Pattern Recogn – volume: 222 start-page: 1 year: 2018 end-page: 10 ident: b0055 article-title: Biomass higher heating value (HHV) modeling on the basis of proximate analysis using iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS) publication-title: Fuel – volume: 24 start-page: 445 year: 2015 end-page: 453 ident: b0110 article-title: A polynomial regression model for stabilized turbulent confined jet diffusion flames using bluff body burners publication-title: Egypt J Pet – year: 2000 ident: b0135 publication-title: The Nature of Statistical Learning Theory – reference: MathWorks. Understanding Support Vector Machine Regression 2021. – volume: 52 start-page: 1650 year: 2008 end-page: 1660 ident: b0080 article-title: Adjusted R2-type measures for Tweedie models publication-title: Comput Stat Data Anal – volume: 4 start-page: 212 year: 2016 end-page: 219 ident: b0200 article-title: Estimation of biomass in wheat using random forest regression algorithm and remote sensing data publication-title: Crop J – volume: 193 start-page: 315 year: 2017 end-page: 321 ident: b0225 article-title: Prediction of elemental composition of coal using proximate analysis publication-title: Fuel – volume: 6 start-page: 1939 year: 2005 end-page: 1959 ident: b0165 article-title: A Unifying View of Sparse Approximate Gaussian Process Regression publication-title: J Mach Learn Res – volume: 135 year: 2021 ident: b0020 article-title: Bioenergy technologies, uses, market and future trends with Austria as a case study publication-title: Renew Sustain Energy Rev – volume: 30 start-page: 197 year: 2018 end-page: 215 ident: b0220 article-title: Multiple predicting K-fold cross-validation for model selection publication-title: J Nonparametr Stat – volume: 266 start-page: 117066 year: 2020 ident: b0185 article-title: Classification of solid fuels with machine learning publication-title: Fuel – volume: 80 start-page: 1567 year: 2001 end-page: 1571 ident: b0035 article-title: Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis publication-title: Fuel – volume: 36 start-page: 348 year: 2011 end-page: 359 ident: b0090 article-title: Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation publication-title: Energy – volume: 18 start-page: 91 year: 2018 end-page: 93 ident: b0075 article-title: User’s guide to correlation coefficients publication-title: Turkish J Emerg Med – volume: 87 year: 2021 ident: b0190 article-title: Experimental study on erosion behavior of fracturing pipeline involving tensile stress and erosion prediction using random forest regression publication-title: J Nat Gas Sci Eng – start-page: 1 year: 2013 end-page: 9 ident: b0120 article-title: SVM kernel functions for classification publication-title: 2013 Int. Conf. Adv. Technol. Eng. – volume: 41 start-page: 1955 year: 2008 end-page: 1959 ident: b0150 article-title: Comparison of decision tree methods for finding active objects publication-title: Adv Sp Res – volume: 100 start-page: 327 year: 2019 end-page: 335 ident: b0140 article-title: A heating value estimation of refuse derived fuel using the genetic programming model publication-title: Waste Manage. – year: 2006 ident: b0170 article-title: Gaussian Processes for Machine Learning – reference: MathWorks. fspecial 2021. – volume: 13 start-page: e0194889 year: 2018 ident: b0160 article-title: Statistical and Machine Learning forecasting methods: Concerns and ways forward publication-title: PLoS One – volume: 6 start-page: 3 year: 1984 end-page: 13 ident: b0015 article-title: Biomass for energy: the environmental issues publication-title: Biomass – volume: 99 start-page: 55 year: 2012 end-page: 63 ident: b0050 article-title: Estimation of higher heating value of biomass from proximate analysis: A new approach publication-title: Fuel – volume: 188 year: 2019 ident: b0045 article-title: A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches publication-title: Energy – volume: 476–477 start-page: 189 year: 2014 end-page: 206 ident: b0205 article-title: Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain) publication-title: Sci Total Environ – reference: . – volume: 257 year: 2019 ident: b0115 article-title: Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation publication-title: Fuel – volume: 70 start-page: 183 year: 2014 end-page: 197 ident: b0105 article-title: Model detection for functional polynomial regression publication-title: Comput Stat Data Anal – volume: 32 start-page: 145 year: 2020 end-page: 151 ident: b0100 article-title: Makine Öğrenmesi ile Kısa ve Elemental Analiz Kullanarak Katı Yakıtların Üst Isı Değerinin Tahmin Edilmesi publication-title: Int J Adv Eng Pure Sci – volume: 9 start-page: 499 issue: 3 year: 2019 ident: 10.1016/j.fuel.2022.123971_b0235 article-title: Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming publication-title: Biomass Convers Biorefinery doi: 10.1007/s13399-019-00386-5 – volume: 48 start-page: 34 year: 2016 ident: 10.1016/j.fuel.2022.123971_b0245 article-title: Characterization, thermochemical conversion studies, and heating value modeling of municipal solid waste publication-title: Waste Manag doi: 10.1016/j.wasman.2015.09.036 – volume: 87 start-page: 3077 year: 2008 ident: 10.1016/j.fuel.2022.123971_b0030 article-title: Development of a new proximate analysis based correlation to predict calorific value of coal publication-title: Fuel doi: 10.1016/j.fuel.2008.04.008 – volume: 32 start-page: 145 year: 2020 ident: 10.1016/j.fuel.2022.123971_b0100 article-title: Makine Öğrenmesi ile Kısa ve Elemental Analiz Kullanarak Katı Yakıtların Üst Isı Değerinin Tahmin Edilmesi publication-title: Int J Adv Eng Pure Sci – volume: 30 start-page: 197 year: 2018 ident: 10.1016/j.fuel.2022.123971_b0220 article-title: Multiple predicting K-fold cross-validation for model selection publication-title: J Nonparametr Stat doi: 10.1080/10485252.2017.1404598 – ident: 10.1016/j.fuel.2022.123971_b0125 – volume: 38 start-page: 2693 year: 2016 ident: 10.1016/j.fuel.2022.123971_b0230 article-title: Calculation of higher heating values of fatty acids publication-title: Energy Sources, Part A Recover Util Environ Eff – volume: 222 start-page: 1 year: 2018 ident: 10.1016/j.fuel.2022.123971_b0055 article-title: Biomass higher heating value (HHV) modeling on the basis of proximate analysis using iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS) publication-title: Fuel doi: 10.1016/j.fuel.2018.02.126 – volume: 87 year: 2021 ident: 10.1016/j.fuel.2022.123971_b0190 article-title: Experimental study on erosion behavior of fracturing pipeline involving tensile stress and erosion prediction using random forest regression publication-title: J Nat Gas Sci Eng doi: 10.1016/j.jngse.2020.103760 – volume: 90 start-page: 1128 year: 2010 ident: 10.1016/j.fuel.2022.123971_b0240 article-title: Prediction of higher heating values of biomass from proximate and ultimate analyses publication-title: Fuel doi: 10.1016/j.fuel.2010.11.031 – ident: 10.1016/j.fuel.2022.123971_b0195 doi: 10.1007/978-3-642-21551-3_33 – volume: 283 year: 2021 ident: 10.1016/j.fuel.2022.123971_b0180 article-title: Machine learning based modelling for lower heating value prediction of municipal solid waste publication-title: Fuel doi: 10.1016/j.fuel.2020.118906 – volume: 193 start-page: 315 year: 2017 ident: 10.1016/j.fuel.2022.123971_b0225 article-title: Prediction of elemental composition of coal using proximate analysis publication-title: Fuel doi: 10.1016/j.fuel.2016.12.044 – volume: 107 start-page: 235 year: 2017 ident: 10.1016/j.fuel.2022.123971_b0010 article-title: Biomass energy potential and utilization in Turkey publication-title: Renew Energy doi: 10.1016/j.renene.2017.02.008 – volume: 9 start-page: 28 issue: 1 year: 2016 ident: 10.1016/j.fuel.2022.123971_b0255 article-title: Effects of Oxygen Content of Fuels on Combustion and Emissions of Diesel Engines publication-title: Energies doi: 10.3390/en9010028 – volume: 476–477 start-page: 189 year: 2014 ident: 10.1016/j.fuel.2022.123971_b0205 article-title: Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain) publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2014.01.001 – volume: 52 start-page: 1650 year: 2008 ident: 10.1016/j.fuel.2022.123971_b0080 article-title: Adjusted R2-type measures for Tweedie models publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2007.05.017 – volume: 99 start-page: 55 year: 2012 ident: 10.1016/j.fuel.2022.123971_b0050 article-title: Estimation of higher heating value of biomass from proximate analysis: A new approach publication-title: Fuel doi: 10.1016/j.fuel.2012.04.015 – volume: 65 start-page: 97 year: 2017 ident: 10.1016/j.fuel.2022.123971_b0210 article-title: Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets publication-title: Pattern Recogn doi: 10.1016/j.patcog.2016.12.018 – start-page: 189 year: 2014 ident: 10.1016/j.fuel.2022.123971_b0025 article-title: Application of Hydrothermal Reactions to Biomass Conversion publication-title: Hydrothermal Liq Biomass – volume: 257 year: 2019 ident: 10.1016/j.fuel.2022.123971_b0115 article-title: Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation publication-title: Fuel doi: 10.1016/j.fuel.2019.115931 – ident: 10.1016/j.fuel.2022.123971_b0005 doi: 10.17798/bitlisfen.315118 – ident: 10.1016/j.fuel.2022.123971_b0130 – volume: 13 start-page: e0194889 issue: 3 year: 2018 ident: 10.1016/j.fuel.2022.123971_b0160 article-title: Statistical and Machine Learning forecasting methods: Concerns and ways forward publication-title: PLoS One doi: 10.1371/journal.pone.0194889 – ident: 10.1016/j.fuel.2022.123971_b0095 – volume: 266 start-page: 117066 year: 2020 ident: 10.1016/j.fuel.2022.123971_b0185 article-title: Classification of solid fuels with machine learning publication-title: Fuel doi: 10.1016/j.fuel.2020.117066 – volume: 70 start-page: 183 year: 2014 ident: 10.1016/j.fuel.2022.123971_b0105 article-title: Model detection for functional polynomial regression publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2013.09.007 – volume: 18 start-page: 91 year: 2018 ident: 10.1016/j.fuel.2022.123971_b0075 article-title: User’s guide to correlation coefficients publication-title: Turkish J Emerg Med doi: 10.1016/j.tjem.2018.08.001 – volume: 265 year: 2020 ident: 10.1016/j.fuel.2022.123971_b0250 article-title: Prediction of higher heating values of biochar from proximate and ultimate analysis publication-title: Fuel doi: 10.1016/j.fuel.2019.116925 – volume: 324 start-page: 012049 year: 2018 ident: 10.1016/j.fuel.2022.123971_b0085 article-title: Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model publication-title: IOP Conf Ser: Mater Sci Eng doi: 10.1088/1757-899X/324/1/012049 – volume: 84 start-page: 487 year: 2005 ident: 10.1016/j.fuel.2022.123971_b0060 article-title: A correlation for calculating HHV from proximate analysis of solid fuels publication-title: Fuel doi: 10.1016/j.fuel.2004.10.010 – volume: 80 start-page: 1567 year: 2001 ident: 10.1016/j.fuel.2022.123971_b0035 article-title: Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis publication-title: Fuel doi: 10.1016/S0016-2361(01)00034-5 – volume: 188 year: 2019 ident: 10.1016/j.fuel.2022.123971_b0045 article-title: A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches publication-title: Energy doi: 10.1016/j.energy.2019.116077 – volume: 24 start-page: 445 year: 2015 ident: 10.1016/j.fuel.2022.123971_b0110 article-title: A polynomial regression model for stabilized turbulent confined jet diffusion flames using bluff body burners publication-title: Egypt J Pet doi: 10.1016/j.ejpe.2015.06.001 – volume: 86 start-page: 1710 year: 2007 ident: 10.1016/j.fuel.2022.123971_b0065 article-title: A correlation for calculating elemental composition from proximate analysis of biomass materials publication-title: Fuel doi: 10.1016/j.fuel.2006.12.029 – ident: 10.1016/j.fuel.2022.123971_b0175 – volume: 81 start-page: 1051 year: 2002 ident: 10.1016/j.fuel.2022.123971_b0040 article-title: A unified correlation for estimating HHV of solid, liquid and gaseous fuels publication-title: Fuel doi: 10.1016/S0016-2361(01)00131-4 – volume: 6 start-page: 3 year: 1984 ident: 10.1016/j.fuel.2022.123971_b0015 article-title: Biomass for energy: the environmental issues publication-title: Biomass doi: 10.1016/0144-4565(84)90003-9 – volume: 41 start-page: 1955 year: 2008 ident: 10.1016/j.fuel.2022.123971_b0150 article-title: Comparison of decision tree methods for finding active objects publication-title: Adv Sp Res doi: 10.1016/j.asr.2007.07.020 – volume: 90 start-page: 476 year: 2017 ident: 10.1016/j.fuel.2022.123971_b0070 article-title: Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies publication-title: J Energy Inst doi: 10.1016/j.joei.2016.03.002 – volume: 1 start-page: 81 year: 1986 ident: 10.1016/j.fuel.2022.123971_b0155 article-title: Induction of decision trees publication-title: Mach Learn doi: 10.1007/BF00116251 – start-page: 149 year: 2010 ident: 10.1016/j.fuel.2022.123971_b0145 article-title: Classification Trees – volume: 4 start-page: 212 year: 2016 ident: 10.1016/j.fuel.2022.123971_b0200 article-title: Estimation of biomass in wheat using random forest regression algorithm and remote sensing data publication-title: Crop J doi: 10.1016/j.cj.2016.01.008 – volume: 6 start-page: 1939 year: 2005 ident: 10.1016/j.fuel.2022.123971_b0165 article-title: A Unifying View of Sparse Approximate Gaussian Process Regression publication-title: J Mach Learn Res – volume: 135 year: 2021 ident: 10.1016/j.fuel.2022.123971_b0020 article-title: Bioenergy technologies, uses, market and future trends with Austria as a case study publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2020.110237 – year: 2006 ident: 10.1016/j.fuel.2022.123971_b0170 – volume: 36 start-page: 348 year: 2011 ident: 10.1016/j.fuel.2022.123971_b0090 article-title: Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation publication-title: Energy doi: 10.1016/j.energy.2010.10.032 – year: 2000 ident: 10.1016/j.fuel.2022.123971_b0135 – volume: 100 start-page: 327 year: 2019 ident: 10.1016/j.fuel.2022.123971_b0140 article-title: A heating value estimation of refuse derived fuel using the genetic programming model publication-title: Waste Manage. doi: 10.1016/j.wasman.2019.09.035 – volume: 171 year: 2020 ident: 10.1016/j.fuel.2022.123971_b0215 article-title: Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2019.109203 – start-page: 1 year: 2013 ident: 10.1016/j.fuel.2022.123971_b0120 article-title: SVM kernel functions for classification – volume: 89 start-page: 913 year: 2010 ident: 10.1016/j.fuel.2022.123971_b0260 article-title: An overview of the chemical composition of biomass publication-title: Fuel doi: 10.1016/j.fuel.2009.10.022 |
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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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