A prediction model for heat exchanger fouling factor based on stacking model
Keywords:
Fouling Factor Prediction, Heat Exchanger Fouling, Stacking ModelAbstract
Given the pressing demand for energy conservation, the petrochemical sector faces increasingly stringent energy-saving mandates. Heat exchangers, essential to this sector, suffer efficiency losses and increased energy consumption due to fouling. To ensure optimal operation of heat exchange systems, regular assessment of solid deposits and the implementation of cleaning schedules are imperative. However, the multitude of influencing factors renders traditional estimation methods unreliable. Consequently, we developed a stacking model to predict the fouling factor of heat exchangers. Specifically, we first constructed fouling factor prediction models using various machine learning techniques, then selected the best-performing models—random forest, extreme gradient boosting , and light gradient boosting machine—for integration. Finally, the predictions from these three models were fed into a linear regression layer to form the final stacking model. The results indicate that the constructed stacking model significantly enhances the accuracy of fouling factor prediction. This model not only surpasses traditional multilayer perceptron neural network methods but also outperforms the well-performing gaussian process regression. This achievement not only validates the effectiveness of our model but also provides robust support for future research and applications in related fields.
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J. Berce, M. Zupanciˇ c, M. Može, and I. Golobi ˇ c, “A review of crystal- ˇ lization fouling in heat exchangers,” Processes, vol. 9, no. 8, p. 1356, 2021. doi:10.3390/pr9081356.
W. F. Alfwzan, G. A. Alomani, L. A. Alessa, and M. M. Selim, “Sensitivity analysis and design optimization of nanofluid heat transfer in a shell-and-tube heat exchanger for solar thermal energy systems: A statistical approach,” Arabian Journal for Science and Engineering, pp. 1–17, 2023. doi:10.1007/s13369-023-08568-0.
Y. Cao, A. Taghvaie Nakhjiri, S. M. Sarkar, and M. Ghadiri, “Integration of ann and nsga-ii for optimization of nusselt number and pressure drop in a coiled heat exchanger via water-based nanofluid containing alumina and ag nanoparticles,” Arabian Journal for Science and Engineering, vol. 48, no. 7, pp. 8861–8869, 2023. doi:10.1007/s13369-022-07480-3.
E. Sandrin, A. Hissanaga, J. Barbosa Jr, and A. da Silva, “On the relation between maximal thermal performance and minimal fouling deposition rate in heat exchanger-like devices,” Applied Thermal Engineering, vol. 243, p. 122518, 2024. doi:10.1016/j.applthermaleng.2024.122518.
R. Ranjan and S. Kumar, “An efficient cascaded effect based parallel flow heat exchanger using nonlinear model predictive controller based fuzzy optimization technique,” Arabian Journal for Science and Engineering, vol. 48, no. 3, pp. 3227–3239, 2023. doi:10.1007/s13369-02207120-w.
M. Hiba, A. F. Ibrahim, S. Elkatatny, and A. Ali, “Application of machine learning to predict the failure parameters from conventional well logs,” Arabian Journal for Science and Engineering, vol. 47, no. 9, pp. 11709–11719, 2022. doi:10.1007/s13369-021-06461-2.
S. Sundar, M. C. Rajagopal, H. Zhao, G. Kuntumalla, Y. Meng, H. C.
Chang, C. Shao, P. Ferreira, N. Miljkovic, S. Sinha, et al, “Fouling modeling and prediction approach for heat exchangers using deep learning,” International Journal of Heat and Mass Transfer, vol. 159, p. 120112, 2020. doi:10.1016/j.ijheatmasstransfer.2020.120112.
S.-Z. Tang, M.-J. Li, F.-L. Wang, Y.-L. He, and W.-Q. Tao, “Fouling potential prediction and multi-objective optimization of a flue gas heat exchanger using neural networks and genetic algorithms,” International Journal of Heat and Mass Transfer, vol. 152, p. 119488, 2020.doi:10.1016/j.ijheatmasstransfer.2020.119488.
Y. K. Dossumbekov, N. Zhakiyev, M. A. Nazari, M. Salem, and B. Abdikadyr, “Sensitivity analysis and performance prediction of a micro plate heat exchanger by use of intelligent approaches,” International Journal of Thermofluids, p. 100601, 2024.doi:10.1016/j.ijft.2024.100601.
E. M. El-Said, M. Abd Elaziz, and A. H. Elsheikh, “Machine learning algorithms for improving the prediction of air injection effect on the thermohydraulic performance of shell and tube heat exchanger,” Applied Thermal Engineering, vol. 185, p. 116471, 2021.doi:10.1016/j.applthermaleng.2020.116471.
Z. Karimi Shoar, H. Pourpasha, S. Zeinali Heris, S. B. Mousavi, and M. Mohammadpourfard, “The effect of heat transfer characteristics of macromolecule fouling on heat exchanger surface: A dynamic simulation study,” The Canadian Journal of Chemical Engineering, vol. 101, no. 10, pp. 5802–5817, 2023. doi:10.1002/cjce.24832.
E. Reynoso-Jardón, A. Tlatelpa-Becerro, R. Rico-Martínez, M. Calderón-Ramírez, and G. Urquiza, “Artificial neural networks (ann) to predict overall heat transfer coefficient and pressure drop on a simulated heat exchanger,” International Journal of Applied Engineering Research, vol. 14, no. 13, pp. 3097–3103, 2019. doi:.
A. K. Gupta, P. Kumar, R. K. Sahoo, A. K. Sahu, and S. K. Sarangi, “Performance measurement of plate fin heat exchanger by exploration: Ann, anfis, ga, and sa,” Journal of Computational Design and Engineering, vol. 4, no. 1, pp. 60–68, 2017. doi:10.1016/j.jcde.2016.07.002.
T. N. Verma, P. Nashine, D. V. Singh, T. S. Singh, and D. Panwar, “Ann: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes,” Applied Thermal Engineering, vol. 120, pp. 219–227, 2017.doi:10.1016/j.applthermaleng.2017.03.126.
L. M. Romeo and R. Gareta, “Fouling control in biomass boilers,” Biomass and bioenergy, vol. 33, no. 5, pp. 854–861, 2009.doi:10.1016/j.biombioe.2009.01.008.
A. Boloorchi and M. Jafari Nasr, “A model for fouling of plate-andframe heat exchangers in food industry,” Asia-Pacific Journal of Chemical Engineering, vol. 7, no. 3, pp. 427–433, 2012. doi:10.1002/apj.585.
J. Aminian and S. Shahhosseini, “Evaluation of ann modeling for prediction of crude oil fouling behavior,” Applied thermal engineering, vol. 28, no. 7, pp. 668–674, 2008. doi:10.1016/j.applthermaleng.2007.06.022.
J. Aminian and S. Shahhosseini, “Neuro-based formulation to predict fouling threshold in crude preheaters,” International Communications in Heat and Mass Transfer, vol. 36, no. 5, pp. 525–531, 2009.doi:10.1016/j.icheatmasstransfer.2009.01.020.
R. F. Garcia, “Improving heat exchanger supervision using neural networks and rule based techniques,” Expert Systems with Applications, vol. 39, no. 3, pp. 3012–3021, 2012. doi:10.1016/j.eswa.2011.08.163.
M. N. Kashani, J. Aminian, S. Shahhosseini, and M. Farrokhi, “Dynamic crude oil fouling prediction in industrial preheaters using optimized ann based moving window technique,” Chemical Engineering Research and Design, vol. 90, no. 7, pp. 938–949, 2012.doi:10.1016/j.cherd.2011.10.013.
D. K. Mohanty and P. M. Singru, “Fouling analysis of a shell and tube heat exchanger using local linear wavelet neural network,” International journal of heat and mass transfer, vol. 77, pp. 946–955, 2014.doi:10.1016/j.ijheatmasstransfer.2014.06.007 .
L. Goliatt, C. Saporetti, L. Oliveira, and E. Pereira, “Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields,” Petroleum, vol. 10, no. 1, pp. 150–164, 2024. doi:10.1016/j.petlm.2023.05.005.
R. M. Adnan, R. R. Mostafa, H.-L. Dai, S. Heddam, A. Kuriqi, and O. Kisi, “Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data,” Engineering Applications of Computational Fluid Mechanics, vol. 17, no. 1, p. 2192258, 2023. doi:10.1080/19942060.2023.2192258.
S. Hosseini, A. Khandakar, M. E. Chowdhury, M. A. Ayari, T. Rahman, M. H. Chowdhury, and B. Vaferi, “Novel and robust machine learning approach for estimating the fouling factor in heat exchangers,” Energy Reports, vol. 8, pp. 8767–8776, 2022. doi:10.1016/j.egyr.2022.06.123.
K. M. Ting and I. H. Witten, “Issues in stacked generalization,” Journal of artificial intelligence research, vol. 10, pp. 271–289, 1999.doi:10.1613/jair.594.
E. Davoudi and B. Vaferi, “Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers,” Chemical Engineering Research and Design, vol. 130, pp. 138–153, 2018.doi:10.1016/j.cherd.2017.12.017.
M.-L. Zhang and Z.-H. Zhou, “Ml-knn: A lazy learning approach to multi-label learning,” Pattern recognition, vol. 40, no. 7, pp. 2038–2048, 2007. doi:10.1016/j.patcog.2006.12.019.
S. J. Rigatti, “Random forest,” Journal of Insurance Medicine, vol. 47, no. 1, pp. 31–39, 2017. doi:.
N. Altman and M. Krzywinski, “Ensemble methods: bagging and random forests,” Nature Methods, vol. 14, no. 10, pp. 933–935, 2017.doi:.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794, 2016.doi:10.1145/2939672.2939785.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.Y. Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” Advances in neural information processing systems, vol. 30, 2017. doi:.
E. Schulz, M. Speekenbrink, and A. Krause, “A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions,” Journal of Mathematical Psychology, vol. 85, pp. 1–16, 2018.doi:10.1016/j.jmp.2018.03.001.
X. Wu, V. Kumar, J. Ross Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J.
McLachlan, A. Ng, B. Liu, P. S. Yu, et al, “Top 10 algorithms in data mining,” Knowledge and information systems, vol. 14, pp. 1–37, 2008.doi:10.1007/s10115-007-0114-2.
D. Cheng, S. Zhang, Z. Deng, Y. Zhu, and M. Zong, “knn algorithm with data-driven k value,” in Advanced Data Mining and Applications: 10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014. Proceedings 10, pp. 499–512, Springer, 2014. doi:10.1007/9783-319-14717-8_39.
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, vol. 2. Springer, 2009. doi:10.1007/978-0-387-21606-5.
L. Breiman, “Bagging predictors,” Machine learning, vol. 24, pp. 123– 140, 1996. doi:10.1007/BF00058655.
S. Ramraj, N. Uzir, R. Sunil, and S. Banerjee, “Experimenting xgboost algorithm for prediction and classification of different datasets,” International Journal of Control Theory and Applications, vol. 9, no. 40, pp. 651–662, 2016. doi:.
C. Rasmussen and Z. Ghahramani, “Infinite mixtures of gaussian process experts,” Advances in neural information processing systems, vol. 14, 2001. doi:.
Y. Ju, G. Sun, Q. Chen, M. Zhang, H. Zhu, and M. U. Rehman, “A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting,” Ieee Access, vol. 7, pp. 28309– 28318, 2019. doi:10.1109/ACCESS.2019.2901920.
X. Guo, Y. Gao, D. Zheng, Y. Ning, and Q. Zhao, “Study on short-term photovoltaic power prediction model based on the stacking ensemble learning,” Energy Reports, vol. 6, pp. 1424–1431, 2020.doi:10.1016/j.egyr.2020.11.006.
N. Ke, G. Shi, and Y. Zhou, “Stacking model for optimizing subjective well-being predictions based on the cgss database,” Sustainability, vol. 13, no. 21, p. 11833, 2021. doi:10.3390/su132111833.
J. Han, M. Kamber, and J. Pei, “Data mining: Concepts and,” Techniques, Waltham: Morgan Kaufmann Publishers, 2012.
D. Valencia, R. E. Lillo, and J. Romo, “A kendall correlation coefficient between functional data,” Advances in Data Analysis and Classification, vol. 13, pp. 1083–1103, 2019.
C. Croux and C. Dehon, “Influence functions of the spearman and kendall correlation measures,” Statistical methods & applications, vol. 19, pp. 497–515, 2010.
Y. Cao, E. Kamrani, S. Mirzaei, A. Khandakar, and B. Vaferi, “Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm,” Energy Reports, vol. 8, pp. 24–36, 2022.doi:10.1016/j.egyr.2021.11.252.
W. Qiao, Y. Wang, J. Zhang, W. Tian, Y. Tian, and Q. Yang, “An innovative coupled model in view of wavelet transform for predicting short-term pm10 concentration,” Journal of Environmental Management, vol. 289, p. 112438, 2021. doi:10.1016/j.jenvman.2021.112438 .
