Hill Publishing Group | contact@hillpublisher.com

Hill Publishing Group

Location:Home / Journals / Journal of Electrical Power & Energy Systems /

DOI:http://dx.doi.org/10.26855/jepes.2022.01.003

Application of Gene Expression Programming for Performance Analysis of a Regenerative Organic Rankine Cycle with Low-Temperature Heat Source

Date: January 25,2022 |Hits: 1025 Download PDF How to cite this paper

Arzu Şencan Şahin*, Erkan Dikmen

Mechanical Engineering Department, Technology Faculty, Isparta University of Applied Sciences, Isparta, Turkey.

*Corresponding author: Arzu Şencan Şahin

Abstract

In this study, the performance analysis of the Regenerative Organic Rankine Cycle (RORC) by using the Gene Expression Programming (GEP) was carried out. Working fluids R-123 and R-134a have been used in the RORC. GEP model was developed to predict thermodynamic performances of the RORC depending on the steam generator, condenser, subcooling, and superheating temperature. To investigate the accuracy of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) was employed. For R123, the optimal values of RMSE, MAPE, and R2 are 0.00004228, 0.01377, and 0.9532, respectively. For R-134a, the optimal values of RMSE, MAPE, and R2 are 0.00002413, 0.01226, and 0.9613, respectively. The results showed that the GEP model results and actual values are in fairly well agreement. The formulas obtained from the GEP model are relatively short, simple and reliable. So, these formulas will assist engineers to very accurately and quickly estimate the thermal efficiency of the regenerative organic Rankine cycle.

References

[1] Roy, J. P., Mishra, M. K., Misra, A. (2011). Parametric optimization and performance analysis of a regenerative Organic Rankine Cycle using low-grade waste heat for power generation. Int. J. Green Energy, 2011, 8: 173-196. https://doi.org/10.1080/15435075.2010.550017.

[2] Kumar, U., Karimi, M. N., Asjad, M. (2016). Parametric optimisation of the organic Rankine cycle for power generation from low-grade waste heat. Int. J. Sustain. Energy, 2016, 35: 774-792. https://doi.org/10.1080/14786451.2014.950962.

[3] Huang, R., Luo, X., Yang, Z., Chen, Y. (2017). Thermodynamic analysis and optimization of a novel zeotropic organic Rankine Cycle. Energy Procedia, 2017, 142: 1346-1352. https://doi.org/10.1016 /j.egypro.2017.12.518.

[4] Le, V. L., Feidt, M., Kheiri, A., Pelloux-Prayer, S. (2014). Performance optimization of low-temperature power generation by supercritical ORCs (organic Rankine cycles) using low GWP (global warming potential) working fluids. Energy, 2014, 67: 513-526. https://doi.org/10.1016/j.energy.2013.12.027.

[5] Li, J., Liu, Q., Ge, Z., Duan, Y., Yang, Z. (2017). Thermodynamic performance analyses and optimization of subcritical and transcritical organic Rankine cycles using R1234ze(E) for 100-200°C heat sources. Energy Convers. Manag., 2017, 149: 140-154. https://doi.org/10.1016/j.enconman.2017.06.060.

[6] Zhu, Y., Li, W., Sun, G., Li, H. (2018). Thermo-economic analysis based on objective functions of an organic Rankine cycle for waste heat recovery from marine diesel engine. Energy, 2018, 158: 343-356. https://doi.org/10.1016/j.energy.2018.06.047.

[7] Yuksel, Y. (2020). Thermodynamic and performance evaluation of an integrated geothermal energy based multigeneration plant. El-Cezeri, 2020, 7(2): 381-401.

[8] Akbay, O., Yılmaz, F. (2021). Flaş İkili Jeotermal Güç Üretim Santralinin Termodinamik Analizi ve Performans Karşılaştırması, El-Cezeri, 2021, 8(1): 445-461.

[9] Şencan, Ş. A., Kovacı, T., Dikmen, E. (2021). A GEP-Based Model Approach for Estimating Thermodynamic Properties of R513A Refrigerant. El-Cezeri, 2021, 8(1): 376-388.

[10] Leon, L. P., Gay, D. (2019). Gene expression programming for evaluation of aggregate angularity effects on permanent deformation of asphalt mixtures, Constr. Build. Mater., 2019, 211: 470-478. https://doi.org/10.1016/j.conbuildmat.2019.03.225.

[11] Mattar, M. A. (2018). Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt. Agric. Water Manag., 2018, 198: 28-38. https://doi.org/10.1016/j.agwat.2017.12.017.

[12] Kaboli, S. H. A., Fallahpour, A., Selvaraj, J., Rahim, N. A. (2017). Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy, 2017, 126: 144-164. https://doi.org/10.1016/j.energy.2017.03.009.

[13] Dikmen, E., Ayaz, M., Gül, D., Şahin, A. Ş. (2017). Gene expression programming approach for the estimation of moisture ratio in herbal plants drying with vacuum heat pump dryer. Heat Mass Transf., 2017, 53: 2419-2424. https://doi.org/10.1007/s00231-017-1998-3.

[14] Şahin, A. Ş., Dikmen, E., Şentürk, S. (2019). A gene expression programming approach for thermodynamic properties of working fluids used on Organic Rankine Cycle. Neural Comput. Appl., 2019, 31: 3947-3955. https://doi.org/10.1007/s00521-018-3349-9.

[15] Abraham, A., De Baets, B., Köppen, M., Nickolay, B. (Eds.). (2006). “Applied soft computing technologies: the challenge of complexity”, 34, Springer Science & Business Media. (2006).

[16] Ferreira, C. (2001). Gene Expression Programming: a New Adaptive Algorithm for Solving Problems, 2001. http://arxiv.org/abs/cs/0102027 (accessed November 5, 2020).

[17] Ferreira, C. (2002). Combinatorial Optimization by Gene Expression Programming: Inversion Revisited, 2002. http://www.gene-expression-programming.com/webpapers/ferreira-ASAI02.pdf (accessed November 5, 2020).

[18] Ferreira, C. (2006). Gene Expression Programming Mathematical Modeling by an Artificial Intelligence, Springer. 2006. https://link.springer.com/book/10.1007%2F3-540-32849-1 (accessed November 5, 2020).

[19] Solvay, Solkane Refrigerant Software. (2012). www.solvaychemicals.com/EN /products/Fluor/Software.aspx.

[20] GeneXproTools, APS v2 (Limited version), Automatic Problem Solver Software. http://www.gepsoft.com.

How to cite this paper

Application of Gene Expression Programming for Performance Analysis of a Regenerative Organic Rankine Cycle with Low-Temperature Heat Source

How to cite this paper: Arzu Şencan Şahin, Erkan Dikmen. (2022) Application of Gene Expression Programming for Performance Analysis of a Regenerative Organic Rankine Cycle with Low-Temperature Heat Source. Journal of Electrical Power & Energy Systems6(1), 24-33.

DOI: http://dx.doi.org/10.26855/jepes.2022.01.003

Volumes & Issues

Free HPG Newsletters

Add your e-mail address to receive free newsletters from Hill Publishing Group.

Contact us

Hill Publishing Group

8825 53rd Ave

Elmhurst, NY 11373, USA

E-mail: contact@hillpublisher.com

Copyright © 2019 Hill Publishing Group Inc. All Rights Reserved.