Application of LSSVM-PSO algorithm as a novel tool to predict standard molar chemical exergy for organic materials

References

• Ahmadi, M. A., and A. Bahadori. 2016a. Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine. International Journal of Ambient Energy 37:486–94. doi:10.1080/01430750.2015.1004105.
   Web of Science ®Google Scholar
• Ahmadi, M. A., and A. Bahadori. 2016b. A simple approach for screening enhanced oil recovery methods: Application of artificial intelligence. Petroleum Science and Technology 34:1887–93. doi:10.1080/10916466.2016.1233247.
   Web of Science ®Google Scholar
• Ahmadi, P., M. A. Rosen, and I. Dincer. 2012. Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm. Energy 46:21–31. doi:10.1016/j.energy.2012.02.005.
   Web of Science ®Google Scholar
• Baghban, A., M. A. Ahmadi, and B. H. Shahraki. 2015. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. The Journal of Supercritical Fluids 98:50–64. doi:10.1016/j.supflu.2015.01.002.
   Web of Science ®Google Scholar
• Baghban, A., M. Bahadori, J. Rozyn, M. Lee, A. Abbas, A. Bahadori, and A. Rahimali. 2016. Estimation of air dew point temperature using computational intelligence schemes. Applied Thermal Engineering 93:1043–52. doi:10.1016/j.applthermaleng.2015.10.056.
   Web of Science ®Google Scholar
• Baghban, A., M. Kahani, M. A. Nazari, M. H. Ahmadi, and W.-M. Yan. 2019. Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. International Journal of Heat and Mass Transfer 128:825–35. doi:10.1016/j.ijheatmasstransfer.2018.09.041.
   Web of Science ®Google Scholar
• Baghban, A., F. Pourfayaz, M. H. Ahmadi, A. Kasaeian, S. M. Pourkiaei, and G. Lorenzini. 2018. Connectionist intelligent model estimates of convective heat transfer coefficient of nanofluids in circular cross-sectional channels. Journal of Thermal Analysis and Calorimetry 132:1213–39. doi:10.1007/s10973-017-6886-z.
   Web of Science ®Google Scholar
• Baghban, A., J. Sasanipour, P. Haratipour, M. Alizad, and M. V. Ayouri. 2017. ANFIS modeling of rhamnolipid breakthrough curves on activated carbon. Chemical Engineering Research and Design 126:67–75. doi:10.1016/j.cherd.2017.08.007.
   Web of Science ®Google Scholar
• Bahadori, A., A. Baghban, M. Bahadori, M. Lee, Z. Ahmad, M. Zare, and E. Abdollahi. 2016. Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems. Applied Thermal Engineering 102:432–46. doi:10.1016/j.applthermaleng.2016.04.005.
   Web of Science ®Google Scholar
• Bilgen, S., and K. Kaygusuz. 2008. The calculation of the chemical exergies of coal-based fuels by using the higher heating values. Applied Energy 85:776–85. doi:10.1016/j.apenergy.2008.02.001.
   Web of Science ®Google Scholar
• Cristianini, N., and J. Shawe-Taylor. 2000. An introduction to support vector machines and other kernel-based learning methods. Cambridge university, UK: Cambridge university press.
   Google Scholar
• Dimopoulos, G. G., I. C. Stefanatos, and N. M. P. Kakalis. 2013. Exergy analysis and optimisation of a steam methane pre-reforming system. Energy 58:17–27. doi:10.1016/j.energy.2012.11.027.
   Web of Science ®Google Scholar
• Dincer,I., and M. A. Rosen. 2012. Exergy: energy, environment and sustainable development. Elsevier: Newnes.
   Google Scholar
• Gharagheizi, F., and M. Mehrpooya. 2007. Prediction of standard chemical exergy by a three descriptors QSPR model. Energy Conversion and Management 48:2453–60. doi:10.1016/j.enconman.2007.04.005.
   Web of Science ®Google Scholar
• Haratipour, P., A. Baghban, A. H. Mohammadi, S. H. H. Nazhad, and A. Bahadori. 2017. On the estimation of viscosities and densities of CO2-loaded MDEA, MDEA+ AMP, MDEA+ DIPA, MDEA+ MEA, and MDEA+ DEA aqueous solutions. Journal of Molecular Liquids 242:146–59. doi:10.1016/j.molliq.2017.06.123.
   Web of Science ®Google Scholar
• Hosseinzadeh, M., and A. Hemmati-Sarapardeh. 2014. Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids. Journal of Molecular Liquids 200:340–48. doi:10.1016/j.molliq.2014.10.033.
   Web of Science ®Google Scholar
• Ikumi, S., C. D. Luo, and C. Y. Wen. 1982. A method of estimating entropies of coals and coal liquids. The Canadian Journal of Chemical Engineering 60:551–55. doi:10.1002/cjce.v60:4.
   Web of Science ®Google Scholar
• Kalogirou, S. A. 2000. Applications of artificial neural-networks for energy systems.’ in, energy systems. Elsevier.
   Google Scholar
• Keybondorian, E., A. Taherpour, A. Bemani, and T. Hamule. 2017a. Application of novel ANFIS-PSO approach to predict asphaltene precipitation in different operational conditions. Petroleum Science and Technology. 36:1–6.
   Web of Science ®Google Scholar
• Keybondorian, E., H. Zanbouri, A. Bemani, and T. Hamule. 2017b. Estimation of the higher heating value of biomass using proximate analysis. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 39:2025–30. doi:10.1080/15567036.2017.1400609.
   Web of Science ®Google Scholar
• Kotas, T. J. 2013. The exergy method of thermal plant analysis. Butterworths, UK.
   Google Scholar
• Kotas, T. J. 1985. The exergy method of thermal power analysis. Butterworth, UK.
   Google Scholar
• Rant, Z. 1961. Zur Bestimmung der spezifischen Exergie von Brennstoffen. Allgemeine Warmetech 10:S172.
   Google Scholar
• Razavi, R., A. Bemani, A. Baghban, A. H. Mohammadi, and S. Habibzadeh. 2019a. An insight into the estimation of fatty acid methyl ester based biodiesel properties using a LSSVM model. Fuel 243:133–41. doi:10.1016/j.fuel.2019.01.077.
   Web of Science ®Google Scholar
• Razavi, R., A. Sabaghmoghadam, A. Bemani, A. Baghban, K.-W. Chau, and E. Salwana. 2019b. Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids. Engineering Applications of Computational Fluid Mechanics 13:560–78. doi:10.1080/19942060.2019.1620130.
   Web of Science ®Google Scholar
• Sargut, J., D. R. Morris, and F. R. Steward. 1988. Exergy analysis of thermal, chemical, and metallurgical processes, 8. USA: Hemisphere Pub’, Co.
   Google Scholar
• Shieh, J. H., and L. T. Fan. 1982. Estimation of energy (enthalpy) and exergy (availability) contents in structurally complicated materials. Energy Sources 6:1–46. doi:10.1080/00908318208946020.
   Web of Science ®Google Scholar
• Song, G., J. Xiao, H. Zhao, and L. Shen. 2012. A unified correlation for estimating specific chemical exergy of solid and liquid fuels. Energy 40:164–73. doi:10.1016/j.energy.2012.02.016.
   Web of Science ®Google Scholar
• Stepanov, V. S. 1995. Chemical energies and exergies of fuels. Energy 20:235–42. doi:10.1016/0360-5442(94)00067-D.
   Web of Science ®Google Scholar
• Styrylska, T., and J. SZARGUT. 1964. Approximate determination of fuel exergy(approximate determination of chemical exergy of fuels with known absolute entropy). Brennstoff-Wärme-Kraft 16:589–96.
   Google Scholar
• Suykens, J. A. K., and J. Vandewalle. 1999. Least squares support vector machine classifiers. Neural Processing Letters 9:293–300. doi:10.1023/A:1018628609742.
   Web of Science ®Google Scholar
• Suykens, J. A. K., and J. Vandewalle. 2000. Recurrent least squares support vector machines. IEEE Transactions on Circuits and Systems Part 1 Fundamental Theory and Applications 47:1109–14. doi:10.1109/81.855471.
   Google Scholar
• Taherpour, A., A. C. Sefidi, A. Bemani, and T. Hamule. 2018. Application of fuzzy c-means algorithm for the estimation of asphaltene precipitation. Petroleum Science and Technology 36:239–43. doi:10.1080/10916466.2017.1416632.
   Web of Science ®Google Scholar
• Zarei, F., and A. Baghban. 2017. Phase behavior modelling of asphaltene precipitation utilizing MLP-ANN approach. Petroleum Science and Technology 35:2009–15. doi:10.1080/10916466.2017.1377233.
   Web of Science ®Google Scholar