Data mining model to prediction thermal efficiency in ORC

Erkan Dikmen, Arzu Şencan Şahin

Article ID: 6126
Vol 7, Issue 1, 2024

VIEWS - 1517 (Abstract)

Abstract


The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R2) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.


Keywords


ORC; data mining; machine learning; zeotropik working fluids

Full Text:

PDF


References


Paris IEA. Global Energy Review: CO2 Emissions in 2021. International Energy Agency; 2022. Xu W, Zhao R, Deng S, et al. Is zeotropic working fluid a promising option for organic Rankine cycle: A quantitative evaluation based on literature data. Renewable and Sustainable Energy Reviews. 2021; 148: 111267. doi: 10.1016/j.rser.2021.111267 Donti PL, Kolter JZ. Machine Learning for Sustainable Energy Systems. Annual Review of Environment and Resources. 2021; 46(1): 719-747. doi: 10.1146/annurev-environ-020220-061831 Arslan O, Yetik O. ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study. Applied Thermal Engineering. 2011; 31(17-18): 3922-3928. doi: 10.1016/j.applthermaleng.2011.07.041 Yılmaz F, Selbaş R, Şahin AŞ. Efficiency analysis of organic Rankine cycle with internal heat exchanger using neural network. Heat and Mass Transfer. 2015; 52(2): 351-359. doi: 10.1007/s00231-015-1564-9 Rashidi MM, Galanis N, Nazari F, et al. Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network. Energy. 2011; 36(9): 5728-5740. doi: 10.1016/j.energy.2011.06.036 Kovacı T, Şencan Şahin A, Dikmen E, et al. Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics. International Journal Of Engineering & Applied Sciences. 2017; 9(3): 1-10. doi: 10.24107/ijeas.297737 Massimiani A, Palagi L, Sciubba E, et al. Neural networks for small scale ORC optimization. Energy Procedia. 2017; 129: 34-41. doi: 10.1016/j.egypro.2017.09.174 Yang F, Cho H, Zhang H, et al. Artificial neural network (ANN) based prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery. Energy Conversion and Management. 2018; 164: 15-26. doi: 10.1016/j.enconman.2018.02.062 Bilgiç HH, Yağlı H, Koç A, et al. Power estimation using artificial neural networks (ANN) in an experimental organic rankine cycle (Turkish). Selcuk University Journal of Engineering, Science and Technology. 2016; 4(1): 7-7. doi: 10.15317/scitech.2016116091 Dong S, Zhang Y, He Z, et al. Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system. Energy. 2018; 144: 851-864. doi: 10.1016/j.energy.2017.12.094 Kılıç B, Arabacı E. Alternative approach in performance analysis of organic rankine cycle (ORC). Environmental Progress & Sustainable Energy. 2018; 38(1): 254-259. doi: 10.1002/ep.12901 Luo X, Wang Y, Liang J, et al. Improved correlations for working fluid properties prediction and their application in performance evaluation of sub-critical Organic Rankine Cycle. Energy. 2019; 174: 122-137. doi: 10.1016/j.energy.2019.02.124 Palagi L, Pesyridis A, Sciubba E, et al. Machine Learning for the prediction of the dynamic behavior of a small scale ORC system. Energy. 2019; 166: 72-82. doi: 10.1016/j.energy.2018.10.059 Huster WR, Schweidtmann AM, Mitsos A. Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation. Optimization and Engineering. 2019; 21(2): 517-536. doi: 10.1007/s11081-019-09454-1 Wang W, Deng S, Zhao D, et al. Application of machine learning into organic Rankine cycle for prediction and optimization of thermal and exergy efficiency. Energy Conversion and Management. 2020; 210: 112700. doi: 10.1016/j.enconman.2020.112700 Peng Y, Lin X, Liu J, et al. Machine learning prediction of ORC performance based on properties of working fluid. Applied Thermal Engineering. 2021; 195: 117184. doi: 10.1016/j.applthermaleng.2021.117184 Tartière T, Astolfi M. A World Overview of the Organic Rankine Cycle Market. Energy Procedia. 2017; 129: 2-9. doi: 10.1016/j.egypro.2017.09.159 Cengel YA, Boles MA, Kanoğlu M. Thermodynamics: an engineering approach. McGraw-hill New York; 2011. Kong R, Deethayat T, Asanakham A, et al. Thermodynamic performance analysis of a R245fa organic Rankine cycle (ORC) with different kinds of heat sources at evaporator. Case Studies in Thermal Engineering. 2019; 13: 100385. doi: 10.1016/j.csite.2018.100385 Siraj F, Ali M. Mining Enrollment Data Using Descriptive and Predictive Approaches. Knowledge-Oriented Applications in Data Mining. Published online January 21, 2011. doi: 10.5772/14210 Becerra-Fernandez I, Zanakis SH, Walczak S. Knowledge discovery techniques for predicting country investment risk. Comput Ind Eng. 2002; 43: 787-800. doi: 10.1016/S0360-8352(02)00140-7 Kaluža B. Machine Learning in Java. UK Packt Publ Ltd; 2016. Chapman P, Clinton J, Kerber R, et al. CRISP-DM 1.0: Step-by-step data mining guide. SPSS inc. 2000; 9: 1-73. Şencan A. Modeling of thermodynamic properties of refrigerant/absorbent couples using data mining process. Energy Conversion and Management. 2007; 48(2): 470-480. doi: 10.1016/j.enconman.2006.06.018 Witten IH, Frank E, Hall MA. Implementations. Data Mining: Practical Machine Learning Tools and Techniques. Published online 2011: 191-304. doi: 10.1016/b978-0-12-374856-0.00006-7 Shaghaghi A, Omidifar R, Zahedi R, et al. Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers. Thermal Science and Engineering. 2023; 6(2): 2087. doi: 10.24294/tse.v6i2.2087 Niranjan A, Prakash A, Veena N, et al. EBJRV: An Ensemble of Bagging, J48 and Random Committee by Voting for Efficient Classification of Intrusions. 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). Published online December 2017. doi: 10.1109/wiecon-ece.2017.8468876 Şencan A, Kızılkan Ö, Bezir NÇ, et al. Different methods for modeling absorption heat transformer powered by solar pond. Energy Conversion and Management. 2007; 48(3): 724-735. doi: 10.1016/j.enconman.2006.09.013 Kaboli SHrA, Fallahpour A, Selvaraj J, et al. Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy. 2017; 126: 144-164. doi: 10.1016/j.energy.2017.03.009



DOI: https://doi.org/10.24294/tse.v7i1.6126

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Erkan Dikmen, Arzu Şencan Şahin

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.