Journal of Natural Gas Science and Engineering. 2016; 34:34
-54. https://doi.org/10.1016/j.jngse.2016.06.030.
[4]
Perera F. Pollution from Fossil-fuel Combustion is the Leading Environmental Threat to Global Pediatric Health and Equity: Solutions Exist. Int. J. Environ. Res. Public Health. 2018; 15(1):16. https://doi.org/10.3390/ijerph15010016.
[5]
Dale S. BP Statistical Review of World Energy. United Kingdom: British Petroleum Company; 2022.
[6]
Mokhatab S, Poe, WA and Speight J. Natural gas compression. Handbook of natural gas transmission and processing. Burlington: Gulf Professional Pub.; 2006.
[7]
Siirola, JJ. Natural gas as a chemical industry fuel and feedstock: past, present, future (and Far Future). Eastman Chemical Company. Retrieved from http://egon.cheme.cmu.edu/esi/docs/pdf/SiirolaNaturalGas.pdf.
[8]
Veza I, Irianto I, Panchal H, Paristiawan PA, Idris M, Fattah IMR, Putra NR, Silambarasan R. Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms. Results in Engineering. 2022; 16:1-6. https://doi.org/10.1016/j.rineng.2022.100688.
[9]
Sheng C and Azevedo JLT. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy, 2005; 28 (5):499-404. https://doi.org/10.1016/j.biombioe.2004.11.008.
[10]
Elmaz1 F, Yücel, Ö and Mutlu, AY. Machine learning based approach for predicting of higher heating values of solid fuels using proximity and ultimate analysis. Int. J. Adv. Eng. Pure Sci. 2020; 32(2): 145-151. https://doi.org/10.7240/jeps.558378.
[11]
Afolabi IC, Epelle IE, Gunes B, Okolie JA. Data-driven machine learning approach for predicting the higher heating value of different biomass classes. Clean Technologies. 2022: 1227-1241. https://doi.org/10.3390/cleantechnol4040075.
[12] Yu W and Chen, C. Predicting the heating value of rice husk with neural network. Advances in Intelligent Systems and Computing. 2014; 279. https://doi.org/10.1007/978-3-642-54927-4_84.
[13]
Ewing L. Fundamentals of Gas Chromatography, Gas Quality and Troubleshooting. Indiana Avenue, Oklahoma: Chandler Engineering Company LLC; 2001.
[14]
Ayaburi J and Bazilian M. Economic benefits of natural gas production: The case of Ghana’s Sankofa Gas Project. Energy for Growth Hub. 2020:1-2.
[15]
Steinar F, Reidar S and Victor H. Online Gas Chromatograph: A Technical and Historical Overview – Design and Maintenance Advice to Achieve an accurate End Results. 28th International North Sea Flow Measurement Workshop. 2010:1-15.
[16]
Xing J, Luo K, Wang H, Gao Z, Fan J. A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning Approaches. Energy. 2019; 188: 116077. https://doi.org/10.1016/j.energy.2019.116077
[17]
Taki M and Rohani, A. Machine learning models for prediction of the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy Evaluation, Case Stud. Therm. Eng. 2022; 31: 101823. https://doi.org/10.1016/j.csite.2022.101823.
[18]
Birgen C, Magnanelli E, Carlsson P, Skreiberg O, Mosby J and Becidan, M. Machine learning based Modelling for Lower heating value prediction of municipal solid waste. Fuel. 2021; 283:1-8. https://doi.org/10.1016/j.fuel.2020.118906
[19]
Taki M and H. Farhadi H. Application of Artificial Neural Network Models (MLP and RBF) and Support Vector Machine (SVM) to Estimate the Shadow in Flat-plate Solar collectors in Iran, Iran Biosyst. Eng. 2021; 52 (2):197–209.
[20]
Qian X, Lee S, Soto A and Chen G. Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis. Resources. 2018; 7(3):1-14.
https://doi.org/10.3390/resources7030039.