Makine Öğrenmesi ile Kısa ve Elemental Analiz Kullanarak Katı Yakıtların Üst Isı Değerinin Tahmin Edilmesi
Abstract
Kısa ve elemental analiz
kullanılarak yüksek ısı değerinin (HHV) öngörülmesi, bir yakıtın karakteristik
niteliğini anlamak için önemli bir prosedürdür. Araştırmalar, HHV değeri ile bu
analizler arasındaki ilişkiyi açıklamak için denklemler oluşturma çabasını
ortaya koymuştur. Ancak uygulanan yöntemler genellikle sadece basit istatistiksel
analizleri içermektedir, bu nedenle kısmen başarılı sayılabilir. Bu makalede,
bu tahmin sorununa makine öğrenme perspektifinden yaklaşılmaktadır, farklı
türdeki malzemelerin kısa ve elemental analizini kullanarak HHV'yi tahmin etmek
için dört makine öğrenme yöntemi, yani doğrusal regresyon, polinom regresyonu,
karar ağacı regresyonu ve destek vektör regresyonunu kullanılmıştır. Kullanılan
veri seti literatürdeki farklı kaynaklardan temin edilerek, kategorilere
ayrılmış; sonuçta elde edilen kategoriler, mümkün olduğunca doğru tahmin
modelleri oluşturmak için makine öğrenme modellerine beslenecek girdiler olarak
kullanılmıştır. Önerilen yöntemlerin performansları k-katlı çapraz doğrulama
tekniğiyle değerlendirilerek, her yöntemin performans değerleri hem tahmin
doğruluğu hem de hesaplama karmaşıklığı açısından tartışılmıştır.
References
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- Strache H, L. R., “Kohlenchemie,” Akad. Verlagsgesellschaft, p. 476, 1924.
- Hosokai, S., Matsuoka, K., Kuramoto, K., and Suzuki, Y., “Modification of Dulong’s formula to estimate heating value of gas, liquid and solid fuels,” Fuel Process. Technol., vol. 152, pp. 399–405, 2016.
- Rd, M. and Md, H., “Mass-fraction of oxygen as a predictor of HHV of gaseous, liquid and solid fuels,” Energy Procedia, vol. 142, pp. 4124–4130, 2017.
- Matin, S. S. and Chelgani, S. C., “Estimation of coal gross calorific value based on various analyses by random forest method,” Fuel, vol. 177, pp. 274–278, 2016.
- Channiwala SA and Parikh PP., “A unified correlation for estimating HHV of solid, liquid and gaseous fuels.,” Fuel, vol. 81, pp. 1051–63, 2002.
- Ng, A. Y., “Preventing Overfitting of Cross-Validation Data,” in ICML ’97 Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp. 245–253.
- Podgorelec, V. and Zorman, M., “Decision Tree Learning,” Encycl. Complex. Syst. Sci., pp. 1–28, 2015.
- Bertsimas, D. and Dunn, J., “Optimal classification trees,” Mach. Learn., vol. 106, no. 7, pp. 1039–1082, 2017.
- Chih-Wei Hsu, Chih-Chung Chang, and C.-J. L., Chih-Wei Hsu, Chih-Chung Chang, Lin, C.-J., Chih-Wei Hsu, Chih-Chung Chang, and C.-J. L., Chih-Wei Hsu, Chih-Chung Chang, and Lin, C.-J., “A Practical Guide to Support Vector Classification,” BJU Int., vol. 101, no. 1, pp. 1396–1400, 2008.
- Noble, W. S., “What is a support vector machine?,” Nat. Biotechnol., vol. 24, no. 12, pp. 1565–1567, 2006.
- Elmaz, F., Yücel, Ö., and Mutlu, A. Y., “Evaluating the Effect of Blending Ratio on the Co-Gasification of High Ash Coal and Biomass in a Fluidized Bed Gasifier Using Machine Learning,” Mugla J. Sci. Technol., vol. 5, no. 1, pp. 1–15, Jun. 2019.
- Mutlu, A. Y. and Yucel, O., “An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification,” Energy, vol. 165, pp. 895–901, Dec. 2018.
- Xu, M., Watanachaturaporn, P., Varshney, P. K., and Arora, M. K., “Decision tree regression for soft classification of remote sensing data,” Remote Sens. Environ., vol. 97, no. 3, pp. 322–336, 2005.
Machine learning based approach for predicting of higher heating values of solid fuels using proximity and ultimate analysis
Abstract
Prediction of higher heating value (HHV) using
proximity and ultimate analysis is an important procedure for understanding the
characteristic attribute of a fuel.
Researches put effort to come up with equations to explain the
relationship between the HHV value and those analyses. But conducted methods
usually included only simple statistical analysis, thus they were partially
effective to use in a practical manner. In this paper we approach this
prediction problem from the machine learning perspective, we employ four
machine learning methods, i.e. linear regression, polynomial regression,
decision tree regression and support vector regression to predict HHV using
proximity and ultimate analysis of different type of materials. Data set used
is collected from literature and is categorized, where the resulting categories
are used as features to be fed to the machine learning models to create
prediction models as accurate as possible. Performances of the proposed methods
are evaluated with k-fold cross-validation technique and each method’s pros and
cons are discussed for both prediction accuracy and computational complexity.
References
- Selvig WA, G. I., “Calorific value of coal,” Chem. coal Util., vol. 1, p. 139, 1945.
- Strache H, L. R., “Kohlenchemie,” Akad. Verlagsgesellschaft, p. 476, 1924.
- Hosokai, S., Matsuoka, K., Kuramoto, K., and Suzuki, Y., “Modification of Dulong’s formula to estimate heating value of gas, liquid and solid fuels,” Fuel Process. Technol., vol. 152, pp. 399–405, 2016.
- Rd, M. and Md, H., “Mass-fraction of oxygen as a predictor of HHV of gaseous, liquid and solid fuels,” Energy Procedia, vol. 142, pp. 4124–4130, 2017.
- Matin, S. S. and Chelgani, S. C., “Estimation of coal gross calorific value based on various analyses by random forest method,” Fuel, vol. 177, pp. 274–278, 2016.
- Channiwala SA and Parikh PP., “A unified correlation for estimating HHV of solid, liquid and gaseous fuels.,” Fuel, vol. 81, pp. 1051–63, 2002.
- Ng, A. Y., “Preventing Overfitting of Cross-Validation Data,” in ICML ’97 Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp. 245–253.
- Podgorelec, V. and Zorman, M., “Decision Tree Learning,” Encycl. Complex. Syst. Sci., pp. 1–28, 2015.
- Bertsimas, D. and Dunn, J., “Optimal classification trees,” Mach. Learn., vol. 106, no. 7, pp. 1039–1082, 2017.
- Chih-Wei Hsu, Chih-Chung Chang, and C.-J. L., Chih-Wei Hsu, Chih-Chung Chang, Lin, C.-J., Chih-Wei Hsu, Chih-Chung Chang, and C.-J. L., Chih-Wei Hsu, Chih-Chung Chang, and Lin, C.-J., “A Practical Guide to Support Vector Classification,” BJU Int., vol. 101, no. 1, pp. 1396–1400, 2008.
- Noble, W. S., “What is a support vector machine?,” Nat. Biotechnol., vol. 24, no. 12, pp. 1565–1567, 2006.
- Elmaz, F., Yücel, Ö., and Mutlu, A. Y., “Evaluating the Effect of Blending Ratio on the Co-Gasification of High Ash Coal and Biomass in a Fluidized Bed Gasifier Using Machine Learning,” Mugla J. Sci. Technol., vol. 5, no. 1, pp. 1–15, Jun. 2019.
- Mutlu, A. Y. and Yucel, O., “An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification,” Energy, vol. 165, pp. 895–901, Dec. 2018.
- Xu, M., Watanachaturaporn, P., Varshney, P. K., and Arora, M. K., “Decision tree regression for soft classification of remote sensing data,” Remote Sens. Environ., vol. 97, no. 3, pp. 322–336, 2005.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 30, 2020 |
| Published in Issue | Year 2020 |