Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NO<sub>x</sub> emission concentration

Yunpeng Ma; Shilin Liu; Shan Gao; Chenheng Xu; Wenbo Guo; Yunpeng Ma; Shilin Liu; Shan Gao; Chenheng Xu; Wenbo Guo

doi:10.3934/mbe.2023899

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 11: 20317-20344. doi: 10.3934/mbe.2023899

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Research article

Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NO_x emission concentration

1.
School of Information Engineering, Tianjin University of Commerce, Tianjin, China
2.
School of Economics, Tianjin University of Commerce, Tianjin, China

Academic Editor: Yang Kuang

Received: 20 August 2023 Revised: 20 October 2023 Accepted: 02 November 2023 Published: 08 November 2023

How to reduce a boiler's NO_x emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NO_x emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NO_x emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.
- optimization,
- model,
- extreme learning machine,
- teaching-learning-based optimization algorithm,
- evolution computation,
- boiler combustion optimization
Citation: Yunpeng Ma, Shilin Liu, Shan Gao, Chenheng Xu, Wenbo Guo. Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NOx emission concentration[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 20317-20344. doi: 10.3934/mbe.2023899

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Abstract

How to reduce a boiler's NO_x emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NO_x emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NO_x emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.

References

[1]	F. Zou, L. Wang, X. Hei, D. Chen, Teaching-learning-based optimization with learning experience of other learners and its application, Appl. Soft Comput., 37 (2015), 725–736. https://doi.org/10.1016/j.asoc.2015.08.047 doi: 10.1016/j.asoc.2015.08.047
[2]	S. Yu, S. Su, Research and application of chaotic glowworm swarm optimization algorithm, J. Front. Comput. Sci. Technol., 8 (2014), 352–358. https://doi.org/10.3778/j.issn.1673-9418.1310016 doi: 10.3778/j.issn.1673-9418.1310016
[3]	S. He, Q. H. Wu, J. Saunders, Group search optimizer: An optimization algorithm inspired by animal searching behavior, IEEE Trans. Evolut. Comput., 13 (2009), 973–990. https://doi.org/10.1109/TEVC.2009.2011992 doi: 10.1109/TEVC.2009.2011992
[4]	D. Karaboga, B. Akay, A comparative study of Artificial Bee Colony algorithm, Appl. Math. Comput., 214 (2009), 108–132. https://doi.org/10.1016/j.amc.2009.03.090 doi: 10.1016/j.amc.2009.03.090
[5]	J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95-International Conference on Neural Networks, 4 (1995), 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
[6]	M. Dorigo, V. Maniezzo, A. Colorni, Ant System: Optimization by a colony of cooperating agents, IEEE Trans. Syst., Man, Cybern., Part B, 26 (1996), 29–41. https://doi.org/10.1109/3477.484436
[7]	M. M. Eusuff, K. E. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm, J. Water Resour. Plann. Manage., 129 (2003), 210–225. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210) doi: 10.1061/(ASCE)0733-9496(2003)129:3(210)
[8]	D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, J. Global Optim., 39 (2007), 459–471. https://doi.org/10.1007/s10898-007-9149-x doi: 10.1007/s10898-007-9149-x
[9]	X. Li, Z. Shao, J. Qian, An optimizing method based on autonomous animats: Fish swarm algorithm, Syst. Eng.-Theory Pract., 11 (2002), 32–38.
[10]	S. Mirjalili, S. Saremi, S. M. Mirjalili, L. S. Coelho, Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization, Expert Syst. Appl., 47 (2016), 106–119. https://doi.org/10.1016/j.eswa.2015.10.039 doi: 10.1016/j.eswa.2015.10.039
[11]	K. M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Syst. Mag., 22 (2002), 52–67. https://doi.org/10.1109/MCS.2002.1004010 doi: 10.1109/MCS.2002.1004010
[12]	S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Softw., 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
[13]	F. A. Hashim, A. G. Hussien, Snake Optimizer: A novel meta-heuristic optimization algorithm, Knowl.-Based Syst., 242 (2022), 108320. https://doi.org/10.1016/j.knosys.2022.108320 doi: 10.1016/j.knosys.2022.108320
[14]	E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: A Gravitational Search Algorithm, Inf. Sci., 179 (2009), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004 doi: 10.1016/j.ins.2009.03.004
[15]	D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Company, Boston, 1989.
[16]	S. Mirjalili, The ant lion optimizer, Adv. Eng. Software, 83 (2015), 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010 doi: 10.1016/j.advengsoft.2015.01.010
[17]	S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Comput. Appl., 27 (2016), 1053–1073. https://doi.org/10.1007/s00521-015-1920-1
[18]	S. Mirjalili, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl.-Based Syst., 89 (2015), 228–249. https://doi.org/10.1016/j.knosys.2015.07.006 doi: 10.1016/j.knosys.2015.07.006
[19]	S. Mirjalili, SCA: A Sine Cosine Algorithm for solving optimization problems, Knowl.-Based Syst., 96 (2016), 120–133. https://doi.org/10.1016/j.knosys.2015.12.022 doi: 10.1016/j.knosys.2015.12.022
[20]	R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput.-Aided Des., 43 (2011), 303–315. https://doi.org/10.1016/j.cad.2010.12.015 doi: 10.1016/j.cad.2010.12.015
[21]	R. V. Rao, V. Patel, An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems, Sci. Iran., 20 (2013), 710–720. https://doi.org/10.1016/j.scient.2012.12.005 doi: 10.1016/j.scient.2012.12.005
[22]	K. Yu, X. Wang, Z. Wang, Elitist teaching-learning-based optimization algorithm based on feedback, Acta Autom. Sin., 40 (2014), 1976–1983.
[23]	L. Gao, H. Ouyang, X. Kong, H. Liu, Teaching-learning based optimization algorithm with crossover operation, J. Northeastern Univ. (Nat. Sci.), 35 (2014), 323–327. https://doi.org/10.3969/j.issn.1005-3026.2014.03.005 doi: 10.3969/j.issn.1005-3026.2014.03.005
[24]	R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems, Inf. Sci., 183 (2012), 1–15. https://doi.org/10.1016/j.ins.2011.08.006 doi: 10.1016/j.ins.2011.08.006
[25]	S. Yang, Y. Zhang, S. Xu, Z. Liao, J. Li, Parameter identification of photovoltaic cell model based on grouping teaching-learning-based optimization algorithm, Distrib. Energy, 7 (2022), 52–61. https://doi.org/10.16513/j.2096-2185.DE.2207307 doi: 10.16513/j.2096-2185.DE.2207307
[26]	T. Niknam, R. Azizipanah-Abarghooee, M. Rasoul Narimani, A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems, Eng. Appl. Artif. Intell., 25 (2012), 1577–1588. https://doi.org/10.1016/j.engappai.2012.07.004 doi: 10.1016/j.engappai.2012.07.004
[27]	S. C. Satapathy, A. Naik, Data clustering based on teaching-learning-based optimization, in International Conference on Swarm, Evolutionary, and Memetic Computing, 7077 (2011), 148–156. https://doi.org/10.1007/978-3-642-27242-4_18
[28]	A. B. Gunji, B. B. B. V. L. Deepak, C. M. V. A. R. Bahubalendruni, D. B. B. Biswal, An optimal robotic assembly sequence planning by assembly subsets detection method using teaching-learning-based optimization algorithm, IEEE Trans. Autom. Sci. Eng., 15 (2018), 1369–1385. https://doi.org/10.1109/TASE.2018.2791665 doi: 10.1109/TASE.2018.2791665
[29]	C. Wu, Y. He, J. Zhao, Solving set-union knapsack problem by modified teaching-learning-based optimization algorithm, J. Front. Comput. Sci. Technol., 12 (2018), 2007–2020. https://doi.org/10.3778/j.issn.1673-9418.1802021 doi: 10.3778/j.issn.1673-9418.1802021
[30]	M. Ghasemi, S. Ghavidel, M. Gitizadeh, E. Akbari, An improved teaching-learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow, Int. J. Electr. Power Energy Syst., 65 (2015), 375–384. https://doi.org/10.1016/j.ijepes.2014.10.027 doi: 10.1016/j.ijepes.2014.10.027
[31]	G. Li, P. Niu, W. Zhang, Y. Liu, Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching-learning-based optimization, Chemom. Intell. Lab. Syst., 126 (2013), 11–20. https://doi.org/10.1016/j.chemolab.2013.04.012 doi: 10.1016/j.chemolab.2013.04.012
[32]	B. Wang, H. Li, Y. Feng, An improved teaching-learning-based optimization for constrained evolutionary optimization, Inf. Sci., 456 (2018), 131–144. https://doi.org/10.1016/j.ins.2018.04.083 doi: 10.1016/j.ins.2018.04.083
[33]	K. Yu, X. Wang, Z. Wang, An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems, J. Intell. Manuf., 27 (2016), 831–843. https://doi.org/10.1007/s10845-014-0918-3 doi: 10.1007/s10845-014-0918-3
[34]	H. Tsai, Confined teaching learning based optimization with variable search strategies for continuous optimization, Inf. Sci., 500 (2019), 34–47. https://doi.org/10.1016/j.ins.2019.05.065 doi: 10.1016/j.ins.2019.05.065
[35]	R. V. Rao, V. Patel, An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems, Int. J. Ind. Eng. Comput., 3 (2012), 535–560. https://doi.org/10.5267/j.ijiec.2012.03.007 doi: 10.5267/j.ijiec.2012.03.007
[36]	F. Zou, L. Wang, X. Hei, D. Chen, D. Yang, Teaching-learning-based optimization with dynamic group strategy for global optimization, Inf. Sci., 273 (2014), 112–131. https://doi.org/10.1016/j.ins.2014.03.038 doi: 10.1016/j.ins.2014.03.038
[37]	D. Chen, R. Lu, F. Zou, S. Li, Teaching-learning-based optimization with variable-population scheme and its application for ANN and global optimization, Neurocomputing, 173 (2016), 1096–1111. https://doi.org/10.1016/j.neucom.2015.08.068 doi: 10.1016/j.neucom.2015.08.068
[38]	S. Sultana, P. K. Roy, Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems, Int. J. Electr. Power Energy Syst., 63 (2014), 534–545. https://doi.org/10.1016/j.ijepes.2014.06.031 doi: 10.1016/j.ijepes.2014.06.031
[39]	F. Zou, D. Chen, Q. Xu, A survey of teaching-learning-based optimization, Neurocomputing, 335 (2019), 366–383. https://doi.org/10.1016/j.neucom.2018.06.076 doi: 10.1016/j.neucom.2018.06.076
[40]	S. Tuo, L. Yong, F. Deng, Y. Li, Y. Lin, Q. Lu, HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems, Plos One, 12 (2017), 0175114. https://doi.org/10.1371/journal.pone.0175114 doi: 10.1371/journal.pone.0175114
[41]	X. Li, P. Niu, J. Liu, Combustion optimization of a boiler based on the chaos and Lévy flight vortex search algorithm, Appl. Math. Modell., 58 (2018), 3–18. https://doi.org/10.1016/j.apm.2018.01.043 doi: 10.1016/j.apm.2018.01.043
[42]	Y. Niu, J. Kang, F. Li, W. Ge, G. Zhou, Case-based reasoning based on grey-relational theory for the optimization of boiler combustion systems, ISA Trans., 103 (2020), 166–176. https://doi.org/10.1016/j.isatra.2020.03.024 doi: 10.1016/j.isatra.2020.03.024
[43]	Y. Shi, W. Zhong, X. Chen, A. B. Yu, Jie Li, Combustion optimization of ultra supercritical boiler based on artificial intelligence, Energy, 170 (2019), 804–817. https://doi.org/10.1016/j.energy.2018.12.172 doi: 10.1016/j.energy.2018.12.172
[44]	A. Aminmahalati, A. Fazlali, H. Safifikhani, Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA Ⅱ algorithm, Energy, 221 (2021), 119859. https://doi.org/10.1016/j.energy.2021.119859 doi: 10.1016/j.energy.2021.119859
[45]	P. Tan, J. Xia, C. Zhang, Q. Fang, G. Chen, Modeling and reduction of NO_X emissions for a 700MW coal-fired boiler with the advanced machine learning method, Energy, 94 (2016), 672–679. https://doi.org/10.1016/j.energy.2015.11.020 doi: 10.1016/j.energy.2015.11.020
[46]	Q. Li, Q. He, Z. Liu, Low NO_x combustion optimization based on partial dimension opposition-based learning particle swarm optimization, Fuel, 310 (2022), 122352. https://doi.org/10.1016/j.fuel.2021.122352 doi: 10.1016/j.fuel.2021.122352
[47]	H. Xi, P. Liao, X. Wu, Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization, Appl. Therm. Eng., 184 (2021), 116287. https://doi.org/10.1016/j.applthermaleng.2020.116287 doi: 10.1016/j.applthermaleng.2020.116287
[48]	M. V. J. J. Suresh, K. S. Reddy, A. K. Kolar, ANN-GA based optimization of a high ash coal-fired supercritical power plant, Appl. Energy, 88 (2011), 4867–4873. https://doi.org/10.1016/j.apenergy.2011.06.029 doi: 10.1016/j.apenergy.2011.06.029
[49]	A. A. M. Rahat, C. Wang, R. M. Everson, J. E. Fieldsend, Data-driven multi-objective optimization of coal-fired boiler combustion systems, Appl. Energy, 229 (2018): 446–458. https://doi.org/10.1016/j.apenergy.2018.07.101
[50]	F. Wang, S. Ma, H. Wang, Y. Li, Z. Qin, J. Zhang, A hybrid model integrating improved flower pollination algorithm-based feature selection and improved random forest for NO_X emission estimation of coal-fired power plants, Measurement, 125 (2018), 303–312. https://doi.org/10.1016/j.measurement.2018.04.069 doi: 10.1016/j.measurement.2018.04.069
[51]	X. Hu, P. Niu, J. Wang, X. Zhang, Multi-objective prediction of coal-fired boiler with a deep hybrid neural networks, Atmos. Pollut. Res., 11 (2020), 1084–1090. https://doi.org/10.1016/j.apr.2020.04.001 doi: 10.1016/j.apr.2020.04.001
[52]	G. Huang, Q. Zhu, C. Siew, Extreme learning machine: a new learning scheme of feedforward neural network, in 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2 (2004), 985–990. https://doi.org/10.1109/IJCNN.2004.1380068
[53]	Y. Ma, C. Xu, H. Wang, R. Wang, S. Liu, X. Gu, Model NO_x, SO₂ emissions concentration and thermal efficiency of CFBB based on a hyper-parameter self-optimized broad learning system, Energies, 15 (2022), 7700. https://doi.org/10.3390/en15207700 doi: 10.3390/en15207700

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