Integration of the Intelligent Optimisation Algorithms with the Artificial Neural Networks to predict the performance of a counter flow wet cooling tower with rotational packing

References

• Bonabeau, E., M. Dorigo, D. de R. D. F. Marco, G. Theraulaz, and G. Théraulaz. 1999. Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press.
   Google Scholar
• Brits, R., A. P. Engelbrecht, and F. van den Bergh. 2007. “Locating Multiple Optima Using Particle Swarm Optimization.” Applied Mathematics and Computation 189 (2): 1859–1883. doi:10.1016/j.amc.2006.12.066.
   Web of Science ®Google Scholar
• Engelbrecht, A. P. 2005. Computational Swarm Intelligence. Wiley.
   Google Scholar
• Gao, M., Y. Shi, N. Wang, Y. Zhao, and F. Sun. 2013. “Artificial Neural Network Model Research on Effects of Cross-Wind to Performance Parameters of wet Cooling Tower Based on Level Froude Number.” Applied Thermal Engineering 51 (1–2): 1226–1234. doi:10.1016/j.applthermaleng.2012.06.053.
   Web of Science ®Google Scholar
• Gao, M., F. Sun, S. Zhou, Y. Shi, Y. Zhao, and N. Wang. 2009. “Performance Prediction of wet Cooling Tower Using Artificial Neural Network Under Cross-Wind Conditions.” International Journal of Thermal Sciences 48 (3): 583–589. doi:10.1016/j.ijthermalsci.2008.03.012.
   Web of Science ®Google Scholar
• Grefenstette, J. J. 1989. “How Genetic Algorithms Work: A Critical Look at Implicit Parallelism.” Genetic Algorithm and Their Applications: Proceedings of third International Conference of Genetic Algorithm.
   Google Scholar
• Guo, Q., X. Qi, Z. Wei, Q. Yin, P. Sun, P. Guo, and J. Liu. 2019. “Modeling and Characteristic Analysis of Fouling in a Wet Cooling Tower Based on Wavelet Neural Networks.” Applied Thermal Engineering 152 (February): 907–916. doi:10.1016/j.applthermaleng.2019.02.041.
   Web of Science ®Google Scholar
• Hajidavalloo, E., R. Shakeri, and M. A. Mehrabian. 2010. “Thermal Performance of Cross Flow Cooling Towers in Variable Wet Bulb Temperature.” Energy Conversion and Management 51 (6): 1298–1303. doi:10.1016/j.enconman.2010.01.005.
   Web of Science ®Google Scholar
• Holland, J. H. 1992. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press.
   Google Scholar
• Hosoz, M., H. M. Ertunc, and H. Bulgurcu. 2007. “Performance Prediction of a Cooling Tower Using Artificial Neural Network.” Energy Conversion and Management 48 (4): 1349–1359. doi:10.1016/j.enconman.2006.06.024.
   Web of Science ®Google Scholar
• Jafarinejad, T., A. Erfani, A. Fathi, and M. B. Shafii. 2019. “Bi-level Energy-Efficient Occupancy Profile Optimization Integrated with Demand-Driven Control Strategy: University Building Energy Saving.” Sustainable Cities and Society 48: 101539. doi:10.1016/j.scs.2019.101539.
   Web of Science ®Google Scholar
• Kennedy, J., and R. Eberhart. 1995. “Particle Swarm Optimization, Proc. Neural Networks 1995.” Proceedings IEEE International Conference on 2, 1942–1948.
   Google Scholar
• Llano-Restrepo, M., and R. Monsalve-Reyes. 2017. “Modeling and Simulation of Counterflow Wet-Cooling Towers and the Accurate Calculation and Correlation of Mass Transfer Coefficients for Thermal Performance Prediction.” International Journal of Refrigeration 74: 47–72. doi:10.1016/j.ijrefrig.2016.10.018.
   Web of Science ®Google Scholar
• Michalewicz, Z. 2013. Genetic Algorithms + Data Structures = Evolution Programs. New York: Springer Science & Business Media.
   Google Scholar
• Pan, T., S. Shieh, S. Jang, W. Tseng, C. Wu, and J. Ou. 2011. “Statistical Multi-Model Approach for Performance Assessment of Cooling Tower.” Energy Conversion and Management 52 (2): 1377–1385. doi:10.1016/j.enconman.2010.09.036.
   Web of Science ®Google Scholar
• Pham, D. T., A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, and M. Zaidi. 2005. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University.
   Google Scholar
• Pham, D. T., A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim, and M. Zaidi. 2006. “The Bees Algorithm – A Novel Tool for Complex Optimisation Problems.” In Intelligent Production Machines and Systems, edited by D. T. Pham, E. E. Eldukhri, and A. J. Soroka, 454–459. Cardiff: Elsevier.
   Google Scholar
• Pham, D. T., and X. Liu. 1995. Neural Networks for Identification, Prediction and Control. London: Springer.
   Google Scholar
• Qi, X., Z. Liu, and D. Li. 2008. “Prediction of the Performance of a Shower Cooling Tower Based on Projection Pursuit Regression.” Applied Thermal Engineering 28 (8–9): 1031–1038. doi:10.1016/j.applthermaleng.2007.06.029.
   Web of Science ®Google Scholar
• Teimoori, K., and A. M. Sadegh. 2014, November. “Experimental Studies of Rotation Efficiency of Packing on Characteristics of Counter Flow Wet Cooling Tower.” In ASME International Mechanical Engineering congress and exposition, V06BT07A041. American Society of Mechanical Engineers.
   Google Scholar
• Von Frisch, K. 2014. Bees: Their Vision, Chemical Senses, and Language. Ithaca, NY: Cornell University Press.
   Google Scholar
• Yang, B., X. Liu, Z. Zhao, J. Song, and C. Chen. 2018, July. “The Analysis of the Influence of Packing and Total Pressure on Cooling Performance of the Cooling Tower.” IOP Conference Series: Earth and Environmental Science 170 (3): 032020.
   Google Scholar