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An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

Published online by Cambridge University Press:  24 October 2022

Jing Liang ,
Hao Guo Open the ORCID record for Hao Guo [Opens in a new window] ,
Ke Chen ,
Kunjie Yu ,
Caitong Yue  and
Xia Li
Jing Liang
Affiliation:
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Hao Guo
Affiliation:
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Ke Chen*
Affiliation:
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Kunjie Yu
Affiliation:
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Caitong Yue
Affiliation:
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Xia Li
Affiliation:
School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
*
*Corresponding author. E-mail: chenkezixf@zzu.edu.cn
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Abstract

With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.

Keywords

circulating fluidized bed boilerparticle swarm optimizationextreme learning machineKalman filtering

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 4 , April 2023 , pp. 1087 - 1097
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Gao, X. S., Xu, D. G., Wang, Y., Pan, H. H. and Shen, W. M., “Multifunctional robot to maintain boiler water-cooling tubes,” Robotica 27(6), 941–948 (2009).CrossRefGoogle Scholar
Jagtap, H. P., Bewoor, A. K., Pathan, F. and Kumar, R., “Application of Particle Swarm Optimization Method to Availability Optimization of Thermal Power Plants,” In: Nature-Inspired Optimization in Advanced Manufacturing Processes and Systems (CRC Press, 2020) pp. 97–112.Google Scholar
Santra, S., Sakthivel, R., Shi, Y. and Mathiyalagan, K., “Dissipative sampled-data controller design for singular networked cascade control systems,” J. Frankl. Inst. 353(14), 3386–3406 (2016).CrossRefGoogle Scholar
Adams, D., Oh, D. H., Kim, D. W., Lee, C. H. and Oh, M., “Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine,” J. Clean. Prod. 270, 122310 (2020).CrossRefGoogle Scholar
Pandey, K. K. and Parhi, D. R., “Trajectory planning and the target search by the mobile robot in an environment using a behavior-based neural network approach,” Robotica 38(9), 1627–1641 (2020).CrossRefGoogle Scholar
Garro, B. A., Rodríguez, K. and Vázquez, R. A., “Classification of dna microarrays using artificial neural networks and abc algorithm,” Appl. Soft Comput. 38(July), 548–560 (2016).CrossRefGoogle Scholar
Shahamiri, S. R. and Salim, S. S. B., “Real-time frequency-based noise-robust automatic speech recognition using multi-nets artificial neural networks: a multi-views multi-learners approach,” Neurocomputing 129, 199–207 (2014).CrossRefGoogle Scholar
Bouhoune, K., Yazid, K., Boucherit, M. S. and Chériti, A., “Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic,” Appl. Soft. Comput. 55(2), 289–301 (2017).CrossRefGoogle Scholar
Kim, N., Jeong, B. and Park, K., “A novel methodology to explore the periodic gait of a biped walker under uncertainty using a machine learning algorithm,” Robotica 40(1), 120–135 (2022).CrossRefGoogle Scholar
Huang, G. B., Zhu, Q. Y. and Siew, C. K., “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks,” In: IEEE International Joint Conference on Neural Networks 2004, vol. 2 (2004) pp. 985–990.Google Scholar
Guliyev, N. J. and Ismailov, V. E., “On the approximation by single hidden layer feedforward neural networks with fixed weights,” Neural Netw. 98(4), 296–304 (2018).CrossRefGoogle ScholarPubMed
Huang, G. B., Zhu, Q. Y. and Siew, C. K., “Extreme learning machine: Theory and applications,” Neurocomputing 70(1–3), 489–501 (2006).CrossRefGoogle Scholar
Zhu, Q. Y., Qin, A. K., Suganthan, P. N. and Huang, G. B., “Evolutionary extreme learning machine,” Patt. Recognit. 38(10), 1759–1763 (2005).CrossRefGoogle Scholar
Cao, J. W., Lin, Z. P. and Huang, G. B., “Self-adaptive evolutionary extreme learning machine,” Neural Process. Lett. 36(3), 285–305 (2012).CrossRefGoogle Scholar
Matias, T., Souza, F., Araújo, R. and Antunes, C. H., “Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine,” Neurocomputing 129(16), 428–436 (2014).CrossRefGoogle Scholar
Han, F., Yao, H. F. and Ling, Q. H., “An improved evolutionary extreme learning machine based on particle swarm optimization,” Neurocomputing 116(1), 87–93 (2013).CrossRefGoogle Scholar
Xu, Y. F., Chen, G. C. and Yu, J. S., “The Kalman Particle Swarm Optimization Algorithm and Its Application in Soft-Sensor of Acrylonitrile Yield,” In: International Conference on Natural Computation (2006), pp. 176–179.Google Scholar
Pano, V. and Ouyang, P. R., “Gain tuning of position domain pid control using particle swarm optimization,” Robotica 34(6), 1351–1366 (2016).CrossRefGoogle Scholar
Wu, Y. L., Liu, G., Guo, X. P., Shi, Y. H. and Xie, L. X., “A self-adaptive chaos and kalman filter-based particle swarm optimization for economic dispatch problem,” Soft Comput. 21(12), 3353–3365 (2017).CrossRefGoogle Scholar
Gilabert, P. L., Braithwaite, R. N. and Montoro, G., “Beyond the moore-penrose inverse: strategies for the estimation of digital predistortion linearization parameters,” IEEE Microw. Mag. 21(12), 34–46 (2020).CrossRefGoogle Scholar
Chen, K., Lv, Q., Lu, Y. and Dou, Y., “Robust regularized extreme learning machine for regression using iteratively reweighted least squares,” Neurocomputing 230(1), 345–358 (2017).CrossRefGoogle Scholar
Mo, Y., Song, Z., Li, H. and Jiang, Z., “A hierarchical safety control strategy for exoskeleton robot based on maximum correntropy kalman filter and bounding box,” Robotica 37(12), 2165–2175 (2019).CrossRefGoogle Scholar
Peng, P., Chen, C. and Yang, Y., “Particle swarm optimization based on hybrid Kalman filter and particle filter,” J. Shanghai Jiaotong Univ. (Sci.) 25(6), 681–688 (2020).CrossRefGoogle Scholar
Sengupta, S., Basak, S. and Peters, R. A., “Particle swarm optimization: A survey of historical and recent developments with hybridization perspectives,” Mach. Learn. Knowl. Extract. 1(1), 157–191 (2018).CrossRefGoogle Scholar
Liang, J., Guo, H., Yu, K. J., Qu, B. Y., Yue, C. T. and Qiao, K. J., “An Improved Composite Differential Evolutionary Algorithm with Self-Adaptive Mutation Strategy for Identifying Photovoltaic Model Parameters,” In: 5th Asian Conference on Artificial Intelligence Technology (ACAIT) (2021) pp. 591–599.Google Scholar
Zhu, G. Y. and Zhang, W. B., “Optimal foraging algorithm for global optimization,” Appl. Soft Comput. 51(5), 294–313 (2017).CrossRefGoogle Scholar
Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y. and Suganthan, P. N., Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization, Technical report, Zhengzhou University (2016).Google Scholar
Derrac, J., García, S., Molina, D. and Herrera, F., “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Lect. Notes Comput. Sci. 1(1), 3–18 (2011).Google Scholar
Liang, J., Zhang, L. Y., Yu, K. J., Qu, B. Y., Yue, C. T. and Qiao, K. J., “A Differential Evolution Based Self-Adaptive Multi-Task Evolutionary Algorithm,” In: 5th Asian Conference on Artificial Intelligence Technology (ACAIT) (2021) pp. 150–156.Google Scholar