Self-adaptive whale optimization for the design and modelling of boiler plant

• Autorzy: Savargave S. B. , Deshpande A. M.
• Języki publikacji: EN

Abstrakty

EN

Recently, boiler plants are have been the subject of intensive investigations in the context of energy-saving technologies and management for power saving and reduction of emissions. Modern boiler design offers several benefits with this respect. In the past, improper design of boilers has been the cause of explosions which led to the loss of life and property. Modern designs attempt to avoid such mishaps. This paper presents a novel Self-Adaptive Whale Optimization Algorithm (SAWOA) for improving the learning characteristic of the neural network, the major intention being to model the characteristics of the boiler plant and so to effectively predict the boiler behaviour. The performance analysis of the introduced model has been carried out using the three test cases with consideration of several parameters. In the experimental analysis, the introduced technique is compared with the existing ones, based on such approaches as Neural Model (NM), Firefly (FF-NM), Adaptive Firefly NM (AFF-NM), and Whale Optimization Algorithm-NM (WOANM). In this comparison, the error, i.e. the difference between the actual and the predicted value, was used, and the results revealed that the error is lower for the introduced technique under different experimental scenarios. The experimental results demonstrate that the performance level of SAWOA is by 18% better than those of NM, FF-NM, and AFF-NM, and by 3.74% better than that of WOA-NM. This confirms the quality of performance of the proposed approach regarding boiler plants.

Słowa kluczowe

EN

boiler; whale optimization; neural model; temperature outlet; feed water flow

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2018
• Tom: Vol. 47, no. 4
• Strony: 329--356
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Savargave S. B.

• Department of Mechanical Engineering, A. P. Shah Institute of Technology, Thane, University of Mumbai, India

autor

Deshpande A. M.

• A. P. Shah Institute of Technology, Thane, University of Mumbai, India

Bibliografia

• [1] Athanasios, N., Nikolaos, N., Nikolaosa, M., Panagiotis, G. and Kakaras, E. (2015) Optimization of a log wood boiler through CFD simulation methods. Fuel Processing Technology, 137, 75-92, September.
• [2] Bahman, Z. N. and Ali, A. (2011) Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model. Energy Conversion and Management, 52(2): 911–916, February.
• [3] Beyhan, S. and Kavaklioglu, K. (2015) Comprehensive Modeling of UTube Steam Generators Using Extreme Learning Machines. IEEE Transactions on Nuclear Science, 62(5): 2245-2254.
• [4] Bhatnagar, K. and Subhash, G. (2017) Extending the Neural Model to Study the Impact of Effective Area of Optical Fiber on Laser Intensity. International Journal of Intelligent Engineering and Systems, 10, 274-283.
• [5] Chandok, J. S., Kar, I. N. and Suneet, T. (2008) Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and backpropagationneural networks. Energy Conversion and Management, 49(8): 1989–1998, August.
• [6] Deng, J., Stobart, R. and Maass, B. (2011) The Applications of Artificial Neural Networks to Engines. In: K. Suzuki, ed., Artificial Neural Networks - Industrial and Control Engineering Applications. IntechOpen, 309-332, DOI: 10.5772/15783
• [7] DIN 1942 standard (1994) Acceptance testing of steam generators, edition 02.
• [8] Flynn, M. and Malley, M. (1999) A drum boiler model for long term power system dynamic simulation. IEEE Trans Power Syst, 14(1): 209– 217, February.
• [9] He, X. and Yang, X. S. (2013) Firefly Algorithm: Recent Advances and Applications. Int. J. of Swarm Intelligence, 1, 1, 36-50.
• [10] Hengyan, X., Lingmei, W. and Huahua, C. (2011) The study of optimizing circulating fluidized bed boiler operational parameters based on neural network and genetic algorithm. In: D.X.
• [11] Hunan, L.G. Changsha, eds, Fourth International conference on Intelligent Computation technology and automation, Shenzen, Guangdong (ICICTA’2011). IEEE Computer Society, 287-290.
• [12] Hopfield, J. J. (1982) Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc. Nat. Acad. Sci., 79(8): 2554– 2558, April.
• [13] Kljajic, M., Gvozdenac, D. and Vukmirovic, S. (2012) Use of Neural Networks for modeling and predicting boiler’s operating performance. Energy, 45(1): 304-311, September.
• [14] Kocaarslan, I. and Cam, E. (2007) Experimental modelling and simulation with adaptive control of power plant. Energy Conversion and Management, 48(3): 787–796, March.
• [15] Kocaarslan, I., Ertugrul, C. and Tiryaki, H. (2006) A fuzzy logic controller application for thermal power plants. Energy Conversion and Management, 47(4): 442–458, March.
• [16] Ling, Y., Zhou, Y. and Luo, Q. (2017) Levy Flight Trajectory-BasedWhale Optimization Algorithm for Global Optimization. IEEE Access, 5, 61686186.
• [17] Liu, X. and Bansal, R. C. (2014) Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant. Applied energy, 130, 658-669, October.
• [18] Liu, X. J., Kong, X. B., Hou, G. L. and Wang, J. H. (2013) Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzyneural network methods. Energy Conversion and Management, 65, 518– 527, January.
• [19] Maxwell, J. Clerk (1892) A Treatise on Electricity and Magnetism, 3rd ed., 2. Oxford: Clarendon, 68-73.
• [20] McCulloch, W. S. and Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin Math Biophys, 5, 115-133.
• [21] Mirjalili, S. and Lewis, A. (2016) The Whale Optimization Algorithm. Advances in Engineering Software, 95, May 2016, 51-67.
• [22] Rusinowski, H. and Stanek, W. (2007) Neural modelling of steam boilers. Energy Conversion and Management, 48(11): 2802–2809, November.
• [23] Sayed, M., Gharghory, S.M. and Kama, H. (2015) Gain Tuning PI controllers for Boiler Turbine Unit using a New Hybrid Jump PSO. Journal of Electrical Systems and Information Technology, 2(1): 99-110, May.
• [24] Secco, S. D., Juan, O., Louisy, M. L., Lucas, J. Y., Plion, P. and Porcheron, L. (2015) Using a genetic algorithm and CFD to identify low NOx configurations in an industrial boiler. Fuel, 158, 672–683, October.
• [25] Song, J., Romero, C. E., Yao, Z. and He, B. (2016) Improved artificial bee colony-based optimization of boiler combustion considering NOX emissions, heat rate and fly ash recycling for on-line applications. Fuel, 172, 20-28, May.
• [26] Szega, M. and Nowak, G. T. (2015) An optimization of redundant measurements location for thermal capacity of power unit steam boiler calculations using data reconciliation method. Energy, 92, 135-141, December.
• [27] Vandani, A. M. K., Bidi, M. and Ahmadi, F. (2015) Exergy analysis and evolutionary optimization of boiler blowdown heat recovery in steam power plants. Energy Conversion and Management, 106, 1-9, December.
• [28] Wei, J. L., Wang, J. H. and Wu, Q. H. (2007) Development of a multisegment coal mill model using an evolutionary computation technique. IEEE Transactions on Energy Conversion, 22(3): 718–27, August.
• [29] Yang, T., Cui, C., Shen, Y. and Lv, Y. (2016) A novel denitration cost optimization system for power unit boilers. Applied Thermal Engineering, 96, 400-410, March.
• [30] Yang, X. S. (2008) Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK. Yang, X. S. (2009) Firefly algorithms for multimodal optimisation. Proc. 5th International Symposium on Stochastic Algorithms, SAGA 2009: Stochastic Algorithms: Foundations and Applications, 5792, 169-178.