Multiple Decision Expert Systems for Performance Analysis of a Boiler System

REFERENCES

• Adam, E. J., and J. L. Marchetti. 1999. Dynamic simulation of large boilers with natural recirculation. Computers & Chemical Engineering 23:1031–40. doi:10.1016/S0098-1354(99)00269-0.
   Web of Science ®Google Scholar
• Afgan, N., M. G. Carvalho, and P. Coelho. 1996. Concept of expert system for boiler fouling assessment. Applied Thermal Engineering 16:835–44. doi:10.1016/1359-4311(95)00082-8.
   Web of Science ®Google Scholar
• Behera, S. K., E. R. Rene, M. C. Kim, and H. S. Park. 2014. Performance prediction of a RPF-fired boiler using artificial neural networks. International Journal of Energy Research 38(8): 995–1007. doi:10.1002/er.3108.
   Web of Science ®Google Scholar
• Bhambre, K., S. K. Mitra, and U. N. Gaitunde. 2007. Modeling of a coal-fired natural circulation boiler. Journal of Energy Resources — ASME 129:159–67. doi:10.1115/1.2719209.
   Web of Science ®Google Scholar
• De, S., M. Kaiadi, M. Fast, and M. Assadi. 2007. Development of an artificial neural network model for the steam process of a coal biomass co-fired combined heat and power (CHP) plant in Sweden. Energy 32:2099–109. doi:10.1016/j.energy.2007.04.008.
   Web of Science ®Google Scholar
• Du, P., J. Xia, W. Zhang, K. Tan, Y. Liu, and S. Liu. 2012. Multiple classifier system for remote sensing image classification: A review. Sensors 12:4764–92. doi:10.3390/s120404764.
   PubMed Web of Science ®Google Scholar
• Elias, A., G. Ibarra–Berastegi, R. Arias, and A. Barona. 2006. Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide. Bioprocess and Biosystems Engineering 29:129–36. doi:10.1007/s00449-006-0062-3.
   PubMed Web of Science ®Google Scholar
• El-Melegy, M. T., and S. M. Ahmed. 2007. Neural networks in multiple classifier systems for remote-sensing image classification. In Soft computing in image processing: Studies in fuzziness and soft computing, ed. M. Nachtegael, D. Vin der Weken, E. E. Kerre, and W. Philips, Vol. 210, 65–96. Berlin, Heidelberg: Springer-Verlag.
   Google Scholar
• Ghosh, A., B. Uma Shankar, L. Bruzzone, and S. K. Meher. 2010. Neuro-fuzzy-combiner: An effective multiple classifier system. International Journal of Knowledge Engineering and Soft Data Paradigms 2:107–29. doi:10.1504/IJKESDP.2010.034678.
   Google Scholar
• Haykin, S. 2009. Neural networks and learning machines (3rd ed.). Upper Saddle River, NJ: Pearson Education, Inc.
   Google Scholar
• Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2:359–66. doi:10.1016/0893-6080(89)90020-8.
   Web of Science ®Google Scholar
• Huang, G. B., Q. Y. Zhu, and C. K. Siew. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70:489–501. doi:10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Khoshjavan, S., B. Rezai, and M. Heidary. 2011. Evaluation of effect of coal chemical properties on coal swelling index using artificial neural networks. Expert Systems with Applications 38:12906–12. doi:10.1016/j.eswa.2011.04.084.
   Web of Science ®Google Scholar
• Kittler, J. 1998. Combining classifiers: A theoretical framework. Pattern Analysis and Applications 1:18–27. doi:10.1007/BF01238023.
   Web of Science ®Google Scholar
• Kljajić, M., D. Gvozdenac, and S. Vukmirović. 2012. Use of neural networks for modeling and predicting boiler’s operating performance. Energy 45:304–11. doi:10.1016/j.energy.2012.02.067.
   Web of Science ®Google Scholar
• Kuncheva, L. I. 2004. Combining pattern classifiers: Methods and algorithms. New York, NY: John Wiley & Sons.
   Google Scholar
• Kusiak, A., M. Li, and Z. Zhang. 2010. A data-driven approach for steam load prediction in buildings. Applied Energy 87:925–33. doi:10.1016/j.apenergy.2009.09.004.
   Web of Science ®Google Scholar
• Kwan, H. W., and J. H. Anderson. 1970. A mathematical model of a 200 MW boiler†. International Journal of Control 12:977–98. doi:10.1080/00207177008931911.
   Web of Science ®Google Scholar
• Lalot, S., and S. Lecoeuche. 2003. Online fouling detection in electrical circulation heaters using neural networks. International Journal of Heat and Mass Transfer 46:2445–57. doi:10.1016/S0017-9310(02)00545-8.
   Web of Science ®Google Scholar
• Lu, S. 1999. Dynamic modeling and simulation of power plant systems. Proceedings of the Institution of Mechanical Engineers Part A: Journal of Power and Energy 213:7–22.
   Web of Science ®Google Scholar
• Maier, H. R., and G. C. Dandy. 2001. Neural network based modelling of environmental variables: A systematic approach. Mathematical and Computer Modelling 33:669–82. doi:10.1016/S0895-7177(00)00271-5.
   Web of Science ®Google Scholar
• MOE (Ministry of Environment). 2013. South Korea. http://eng.me.go.kr/content.do?method=moveContent&menuCode=pol_rec_pol_rec_energy [accessed January 4, 2013].
   Google Scholar
• Monedero, I., F. Biscarri, C. León, J. I. Guerrero, R. González, and L. Pérez-Lombard. 2012. Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant. Expert Systems with Applications 39:9860–67. doi:10.1016/j.eswa.2012.02.165.
   Web of Science ®Google Scholar
• Polikar, R. 2006. Ensemble based systems in decision making. IEEE Transactions on Circuits and Systems 6:21–45.
   Google Scholar
• Qin, R., D. Yan, X. Zhou, and Y. Jiang. 2012. Research on a dynamic simulation method of atrium thermal environment based on neural network. Building and Environment 50:214–20. doi:10.1016/j.buildenv.2011.11.001.
   Web of Science ®Google Scholar
• Riedmiller, M., and H. Braun. 1993. A direct adaptive method for faster back propagation learning: The Rprop algorithm. In Proceedings of the IEEE international conference on neural networks, 586–91. IEEE Press.
   Google Scholar
• Romeo, L. M., and R. Gareta. 2006. Neural network for evaluating boiler behaviour. Applied Thermal Engineering 26:1530–36. doi:10.1016/j.applthermaleng.2005.12.006.
   Web of Science ®Google Scholar
• Rummelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning representations by back-propagation errors. Nature 323:533–36. doi:10.1038/323533a0.
   Web of Science ®Google Scholar
• Silva, R. G., L. M. Pinotti, A. J. G. Cruz, R. L. C. Giordano, and R. C. Giordano. 2008. Artificial neural networks to infer biomass and product concentration during the production of penicillin G acylase from Bacillus megaterium. . Journal of Chemical Technology and Biotechnology 83:739–49. doi:10.1002/jctb.1864.
   Web of Science ®Google Scholar
• Singhal, S., and L. Wu. 1989. Training multilayer perceptrons with the extended Kalman filter algorithm. In Advances in neural information processing systems, 133–40. San Mateo, CA: Morgan Kaufmann.
   Google Scholar
• Smrekar, J., M. Assadi, M. Fast, I. Kuštrin, and S. De. 2009. Development of artificial neural network model for a coal-fired boiler using real plant data. Energy 34:144–52. doi:10.1016/j.energy.2008.10.010.
   Web of Science ®Google Scholar
• Syn, C. Z., M. Mokhtar, C. J. Feng, and Y. H. P. Manurung. 2011. Approach to prediction of laser cutting quality by employing fuzzy expert system. Expert Systems and Applications 38:7558–68. doi:10.1016/j.eswa.2010.12.111.
   Web of Science ®Google Scholar
• Tang, L., L. Yu, S. Wang, J. Li, and S. Wang. 2012. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Applied Energy 93:432–43. doi:10.1016/j.apenergy.2011.12.030.
   Web of Science ®Google Scholar
• Wilamowski, B. M., and H. Yu. 2010. Improved computation for Levenberg–Marquardt training. IEEE Transactions on Neural Networks 21:930–37. doi:10.1109/TNN.2010.2045657.
   PubMed Web of Science ®Google Scholar
• Wu, F., H. Zhou, J.-P. Zhao, and K.-F. Cen. 2011. A comparative study of the multi-objective optimization algorithms for coal-fired boilers. Expert Systems with Applications 38:7179–85. doi:10.1016/j.eswa.2010.12.042.
   Web of Science ®Google Scholar