Data-driven Least-squares Support Vector Machine Model for Integrating Wireless Sensor Data in Logistics Optimization and Reducing Denitrification Cost in Thermal Power Generation
DOI:
https://doi.org/10.13052/dgaej2156-3306.4137Keywords:
Wireless Sensor Networks, Real-time Sensor Data, Denitrification Economic Optimization, Coal-fired Boiler, Least Squares Support Vector Machine, BP Network-based Variable Selection, Fuzzy Association Rule Minin g, Wireless Communication, Logistics OptimizationAbstract
Flue gas denitrification of boilers in large coal-fired power stations has high operating costs, and its online optimization can reduce denitrification costs and enhance the competitiveness of power generation enterprises. In addition to the operational aspects of denitrification, the logistics of denitrification agents, such as transportation, storage, and distribution, also contribute significantly to the overall cost. This study comprehensively focuses on the online modeling and optimization of denitrification cost of thermal power units, incorporating logistics costs related to denitrification agents. The paper proposes a system that integrates wireless sensor data and real-time wireless communication from thermal power plants, aiming to construct an online denitrification and logistics integrated economic optimization system. The system establishes a boiler denitrification cost prediction model using a data-driven least-squares support vector machine (LSSVM) method combined with the BP algorithm for input variable selection. An improved genetic algorithm is applied for offline optimization of the unit’s constant operating load points and construction of an offline expert database. Additionally, a logistics cost prediction sub-model is included, analyzing historical logistics data related to denitrification agents, such as transportation distances, storage durations, and vehicle utilization rates. A fuzzy association rule mining algorithm (FARM) is utilized to extract correlations between load, logistics parameters, and optimization variables, enabling real-time optimization of both denitrification and logistics costs. The results show that the BP-LSSVM modeling method effectively reduces model complexity and improves prediction accuracy, while the GA-FARM optimization method significantly reduces comprehensive denitrification and logistics costs, providing a framework for online real-time optimization in thermal power units. Least Squares Support Vector Machine (LSSVM) is a regression-based machine learning technique known for its high accuracy and good generalization ability, especially in complex nonlinear systems. In this study, LSSVM is employed to model the denitrification cost. Meanwhile, the Back Propagation (BP) algorithm is used for effective input variable selection to reduce model complexity and enhance performance.
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References
Zhao, W., Wu, Z., and Gu, Y. (2010). Combustion optimization of power plant boilers based on least squares support vector machines. Proceedings of the CSEE, (1), 1–6.
Wang, C. C. (2013). Economic control of NOx concentration at the inlet of denitrification equipment. China Electric Power, (1), 86–89.
Zhao, H., Wang, P. H., and Lu, L. (2008). Modeling method for thermal efficiency and NOx emission response characteristics of power plant boilers. Proceedings of the CSEE, (32), 96–100.
Gu, Y., Zhao, W., and Wu, Z. (2012). An optimal MVs decision-model for boiler combustion optimization. Proceedings of the CSEE, 32(2), 39–44.
Gu, Y., Zhao, W., and Wu, Z. (2011). Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems. Journal of Process Control, 21(7), 1040–1048.
Smrekar, J., Assad, M., Fast, M., et al. (2009). Development of artificial neural network model for a coal-fired boiler using real plant data. Energy, 34(2), 141–152.
Smrekar, J., Potocnik, P., and Senegacnik, A. (2013). Multi-step-ahead prediction of NOx emissions for a coal-based boiler. Applied Energy, 106, 89–99.
Kusiak, A., and Zheng, S. (2006). Combustion efficiency optimization and virtual testing: A data-mining approach. IEEE Transactions on Industrial Informatics, 2(3), 176–184.
Zhang, Q., Xu, S. S., and Wang, Z. Q. (2004). Advances and engineering applications of selective catalytic reduction flue gas denitrification technology. Thermal Power Generation, (4), 1–6.
Zheng, L., Zhou, H., and Cen, K. (2004). Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks. Energy, 29(1), 167–183.
Lv, Y., Liu, J., and Yang, T. (2012). Nonlinear PLS integrated with error-based LSSVM and its application to NOx modeling. Industrial & Engineering Chemistry Research, 51(49), 16092–16100.
Wang, C. C. (2013). Economic control of NOx concentration at the inlet of denitrification equipment. China Electric Power, (1), 86–89.
Kuo, K., and Cheng, Y. (2006). Discriminating the genuineness of Chinese medicines using least squares support vector machines. Chinese Journal of Analytical Chemistry, 34(4), 561–564.
Romero, C. E., Yao, Z., et al. (2009). Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms. Fuel, 88(5), 806–816.
Suykens, J. A. K., and Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.
