Prediction of NOx concentration using modular long short-term memory neural network for municipal solid waste incineration

doi:10.1016/j.cjche.2022.06.028

Chinese Journal of Chemical Engineering ›› 2023, Vol. 56 ›› Issue (4): 46-57.DOI: 10.1016/j.cjche.2022.06.028

Previous Articles Next Articles

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China;Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education, Beijing 100124, China

Received:2021-12-18 Revised:2022-05-18 Online:2023-06-13 Published:2023-04-28
Contact: Junfei Qiao,E-mail:adqiao@bjut.edu.cn
Supported by:
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (62021003, 61890930-5, 61903012, 62073006), Beijing Natural Science Foundation (42130232), the National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302).

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China;Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education, Beijing 100124, China

通讯作者: Junfei Qiao,E-mail:adqiao@bjut.edu.cn
基金资助:
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (62021003, 61890930-5, 61903012, 62073006), Beijing Natural Science Foundation (42130232), the National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302).

Abstract

Abstract: Air pollution control poses a major problem in the implementation of municipal solid waste incineration (MSWI). Accurate prediction of nitrogen oxides (NO_x) concentration plays an important role in efficient NO_x emission controlling. In this study, a modular long short-term memory (M-LSTM) network is developed to design an efficient prediction model for NO_x concentration. First, the fuzzy C means (FCM) algorithm is utilized to divide the task into several sub-tasks, aiming to realize the divide-and-conquer ability for complex task. Second, long short-term memory (LSTM) neural networks are applied to tackle corresponding sub-tasks, which can improve the prediction accuracy of the sub-networks. Third, a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage. Finally, after being evaluated by a benchmark simulation, the proposed method is applied to a real MSWI process. And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Key words: Municipal solid waste incineration, NO_x concentration prediction, Modular neural network, Model

摘要： Air pollution control poses a major problem in the implementation of municipal solid waste incineration (MSWI). Accurate prediction of nitrogen oxides (NO_x) concentration plays an important role in efficient NO_x emission controlling. In this study, a modular long short-term memory (M-LSTM) network is developed to design an efficient prediction model for NO_x concentration. First, the fuzzy C means (FCM) algorithm is utilized to divide the task into several sub-tasks, aiming to realize the divide-and-conquer ability for complex task. Second, long short-term memory (LSTM) neural networks are applied to tackle corresponding sub-tasks, which can improve the prediction accuracy of the sub-networks. Third, a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage. Finally, after being evaluated by a benchmark simulation, the proposed method is applied to a real MSWI process. And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词: Municipal solid waste incineration, NO_x concentration prediction, Modular neural network, Model

Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao. Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration[J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 46-57.

References

[1] M. Nikravan, A.A. Ramezanianpour, R. Maknoon, Study on physiochemical properties and leaching behavior of residual ash fractions from a municipal solid waste incinerator (MSWI) plant, J. Environ. Manage. 260 (2020) 110042. https://doi.org/10.1016/j.jenvman.2019.110042.
[2] H.A. Arafat, K. Jijakli, A. Ahsan, Environmental performance and energy recovery potential of five processes for municipal solid waste treatment, J. Clean. Prod. 105 (2015) 233–240. https://doi.org/10.1016/j.jclepro.2013.11.071.
[3] Z. Bao, W. Lu, Developing efficient circularity for construction and demolition waste management in fast emerging economies: Lessons learned from Shenzhen, China, Sci. Total Environ. 724 (2020) 138264. https://doi.org/10.1016/j.scitotenv.2020.138264.
[4] P. Zhou, D. Guo, T. Chai, Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification, Neurocomputing. 308 (2018) 101–110. https://doi.org/10.1016/j.neucom.2018.04.060.
[5] X. Chen, W. Zhong, C. Jiang, Z. Li, X. Peng, H. Cheng, Key performance index estimation based on ensemble locally weighted partial least squares and its application on industrial nonlinear processes, Chemom. Intell. Lab. Syst. 203 (2020) 104031. https://doi.org/10.1016/j.chemolab.2020.104031.
[6] Y.G. Li, W.H. Gui, C.H. Yang, Y.F. Xie, Soft sensor and expert control for blending and digestion process in alumina metallurgical industry, J. Process Control. 23 (2013) 1012–1021. https://doi.org/10.1016/j.jprocont.2013.06.002.
[7] J. Song, C.E. Romero, Z. Yao, B. He, Improved artificial bee colony-based optimization of boiler combustion considering NOX emissions, heat rate and fly ash recycling for on-line applications, Fuel. 172 (2016) 20–28.
[8] C. Wang, Y. Liu, S. Zheng, A. Jiang, Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process, Energy. 153 (2018) 149–158. https://doi.org/10.1016/j.energy.2018.01.003.
[9] S.M. Safdarnejad, J.F. Tuttle, K.M. Powell, Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously, Comput. Chem. Eng. 124 (2019) 62–79. https://doi.org/10.1016/j.compchemeng.2019.02.001.
[10] G. Wang, O.I. Awad, S. Liu, S. Shuai, Z. Wang, NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis, Energy. 198 (2020) 117286. https://doi.org/10.1016/j.energy.2020.117286.
[11] W. Zheng, C. Wang, Y. Yang, Y. Zhang, Multi-objective combustion optimization based on data-driven hybrid strategy, Energy. 191 (2020) 116478. https://doi.org/10.1016/j.energy.2019.116478.
[12] Y. Lv, T. Yang, J. Liu, An adaptive least squares support vector machine model with a novel update for NOx emission prediction, Chemom. Intell. Lab. Syst. 145 (2015) 103–113. https://doi.org/10.1016/j.chemolab.2015.04.006.
[13] T. Yang, K. Ma, Y. Lv, Y. Bai, Real-time dynamic prediction model of NOx emission of coal-fired boilers under variable load conditions, Fuel. 274 (2020) 117811. https://doi.org/10.1016/j.fuel.2020.117811.
[14] S.N. Tran, A.S. D’Avila Garcez, Deep Logic Networks: Inserting and Extracting Knowledge from Deep Belief Networks, IEEE Trans. Neural Networks Learn. Syst. 29 (2018) 246–258. https://doi.org/10.1109/TNNLS.2016.2603784.
[15] M. Bianchini, F. Scarselli, On the complexity of shallow and deep neural network classifiers, 22nd Eur. Symp. Artif. Neural Networks, Comput. Intell. Mach. Learn. ESANN 2014 - Proc. 25 (2014) 371–376.
[16] 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.
[17] F. Wang, S. Ma, H. Wang, Y. Li, J. Zhang, Prediction of NOX emission for coal-fired boilers based on deep belief network, Control Eng. Pract. 80 (2018) 26–35. https://doi.org/10.1016/j.conengprac.2018.08.003.
[18] D. Adams, D.H. Oh, D.W. Kim, C.H. Lee, M. Oh, 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 (2020) 122310. https://doi.org/10.1016/j.jclepro.2020.122310.
[19] P. Tan, B. He, C. Zhang, D. Rao, S. Li, Q. Fang, G. Chen, Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory, Energy. 176 (2019) 429–436. https://doi.org/10.1016/j.energy.2019.04.020.
[20] T. Chai, J. Zhang, T. Yang, S. Member, Demand Forecasting of the Fused Magnesia Smelting Process with System Identification and deep learning, IEEE Trans. Ind. Informatics. 3203 (2021) 1–10. https://doi.org/10.1109/TII.2021.3065930.
[21] G. Yang, Y. Wang, X. Li, Prediction of the NOx emissions from thermal power plant using long-short term memory neural network, Energy. 192 (2020) 116597. https://doi.org/10.1016/j.energy.2019.116597.
[22] M.A. Bertolero, B.T. Thomas Yeo, M. D’Esposito, The modular and integrative functional architecture of the human brain, Proc. Natl. Acad. Sci. U. S. A. 112 (2015) E6798–E6807. https://doi.org/10.1073/pnas.1510619112.
[23] H.J. Park, K. Friston, Structural and functional brain networks: From connections to cognition, Science. 342 (2013) 1238411. https://doi.org/10.1126/science.1238411.
[24] O. Sporns, R.F. Betzel, Modular brain networks, Annu. Rev. Psychol. 67 (2016) 613–640. https://doi.org/10.1146/annurev-psych-122414-033634.
[25] A.O. Hoori, S. Member, A. Al Kazzaz, R. Khimani, Y. Motai, S. Member, A.J. Aved, Electric Load Forecasting Model Using a Multicolumn Deep Neural Networks, IEEE Trans. Ind. Informatics. 67 (2020) 6473–6482.
[26] L. Wang, S. Mao, B. Wilamowski, R.M. Nelms, Ensemble Learning for Load Forecasting, IEEE Trans. Green Commun. Netw. 4(2020) 616-628. https://doi.org/10.1109/TGCN.2020.2987304.
[27] Z. Yuan, L. Meng, X. Gu, Y. Bai, H. Cui, C. Jiang, Prediction of NOx emissions for coal-fired power plants with stacked-generalization ensemble method, Fuel. 289 (2021) 119748. https://doi.org/10.1016/j.fuel.2020.119748.
[28] Y. Liu, C. Yang, K. Huang, W. Gui, Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network, Knowledge-Based Syst. 188 (2020) 105006. https://doi.org/10.1016/j.knosys.2019.105006.
[29] M. Ali, A. Sarwar, V. Sharma, J. Suri, Artificial neural network based screening of cervical cancer using a hierarchical modular neural network architecture (HMNNA) and novel benchmark uterine cervix cancer database, Neural Comput. Appl. 31 (2019) 2979–2993. https://doi.org/10.1007/s00521-017-3246-7.
[30] P. Melin, I. Miramontes, G. Prado-Arechiga, A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis, Expert Syst. Appl. 107 (2018) 146–164. https://doi.org/10.1016/j.eswa.2018.04.023.
[31] R. Chandra, S. Cripps, Coevolutionary multi-task learning for feature-based modular pattern classification, Neurocomputing. 319 (2018) 164–175. https://doi.org/10.1016/j.neucom.2018.08.011.
[32] J. Qiao, X. Guo, W. Li, An online self-organizing modular neural network for nonlinear system modeling, Appl. Soft Comput. J. 97 (2020) 106777. https://doi.org/10.1016/j.asoc.2020.106777.
[33] W. Li, M. Li, J. Zhang, J. Qiao, Design of a self-organizing reciprocal modular neural network for nonlinear system modeling, Neurocomputing. 411 (2020) 327–339. https://doi.org/10.1016/j.neucom.2020.06.056.
[34] Z. Li, Z. Miao, Effects of moisture and its input form on coal combustion process and NOx transformation characteristics in lignite boiler, Fuel. 266 (2020) 116970. https://doi.org/10.1016/j.fuel.2019.116970.
[35] P.W. Li, C.S. Chyang, H.W. Ni, An experimental study of the effect of nitrogen origin on the formation and reduction of NOx in fluidized-bed combustion, Energy. 154 (2018) 319–327. https://doi.org/10.1016/j.energy.2018.04.141.
[36] Pattern recognition with fuzzy objective function algorithms (James C. Bezdek)
[37] L.F. Zhu, J.S. Wang, H.Y. Wang, A Novel Clustering Validity Function of FCM Clustering Algorithm, IEEE Access. 7 (2019) 152289–152315. https://doi.org/10.1109/ACCESS.2019.2946599.
[38] S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Comput. 29 (2001) 147–158.
[39] H. Peng, F. Long, C. Ding, Feature selection based on mutual information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy, IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005) 1226–1238. https://doi.org/10.1109/TPAMI.2005.159.
[40] T. Zhongda, li Shujiang, W. Yanhong, W. Xiangdong, A multi-model fusion soft sensor modelling method and its application in rotary kiln calcination zone temperature prediction, Trans. Inst. Meas. Control. 38 (2016) 110–124. https://doi.org/10.1177/0142331215573099.
[41] X. Meng, P. Rozycki, J.F. Qiao, B.M. Wilamowski, Nonlinear System Modeling Using RBF Networks for Industrial Application, IEEE Trans. Ind. Informatics. 14 (2018) 931–940. https://doi.org/10.1109/TII.2017.2734686.

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Huiqi Wang, Jianpo Ren, Shihao Zhang, Jiayu Dai, Yue Niu, Ketao Shi, Qiuxiang Yin, Ling Zhou. Measurement and correlation of solubility of 9-fluorenone in 11 pure organic solvents from T = 283.15 to 323.15 K [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 235-241.
[2]	Jindong Dai, Chi Zhai, Jiali Ai, Guangren Yu, Haichao Lv, Wei Sun, Yongzhong Liu. A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 262-274.
[3]	Borui Liu, Tao Zhang, Yi Zheng, Kailong Li, Hui Pan, Hao Ling. A dynamic control structure of liquid-only transfer stream distillation column [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 135-145.
[4]	Zhonghao Li, Yuanyuan Yang, Huanong Cheng, Yun Teng, Chao Li, Kangkang Li, Zhou Feng, Hongwei Jin, Xinshun Tan, Shiqing Zheng. Measurement and model of density, viscosity, and hydrogen sulfide solubility in ferric chloride/trioctylmethylammonium chloride ionic liquid [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 210-221.
[5]	Yaran Bu, Changchun Wu, Lili Zuo, Qian Chen. The calculation and optimal allocation of transmission capacity in natural gas networks with MINLP models [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 251-261.
[6]	Junhao Wang, Shugang Ma, Peng Chen, Zhipeng Li, Zhengming Gao, J. J. Derksen. Mixing of miscible shear-thinning fluids in a lid-driven cavity [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 112-123.
[7]	Weikai Ren, Runsong Dai, Ningde Jin. Modeling of liquid film thickness around Taylor bubbles rising in vertical stagnant and co-current slug flowing liquids [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 179-194.
[8]	Hongwei Liang, Wenling Li, Zisheng Feng, Jianming Chen, Guangwen Chu, Yang Xiang. Numerical simulation of gas-liquid flow in the bubble column using Wray-Agarwal turbulence model coupled with population balance model [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 205-223.
[9]	Yun-Zhang Liu, Lu-Yao Zhang, Dan He, Li-Zhen Chen, Zi-Shuai Xu, Jian-Long Wang. Solubility measurement, correlation and thermodynamic properties of 2, 3, 4-trichloro-1, 5-dinitrobenzene in fifteen mono-solvents at temperatures from 278.15 to 323.15 K [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 224-233.
[10]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 244-255.
[11]	Guangyuan Chen, Tong Zhou, Meng Zhang, Zhongxiang Ding, Zhikun Zhou, Yuanhui Ji, Haiying Tang, Changsong Wang. Effects of heavy metal ions Cu²⁺/Pb²⁺/Zn²⁺ on kinetic rate constants of struvite crystallization [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 10-16.
[12]	Li Xia, Yule Pan, Tingting Zhao, Xiaoyan Sun, Shaohui Tao, Yushi Chen, Shuguang Xiang. Estimating heat capacities of liquid organic compounds based on elements and chemical bonds contribution [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 30-38.
[13]	Xiongzhuo Zhu, Dali Gao, Chong Yang, Chunjie Yang. A blast furnace fault monitoring algorithm with low false alarm rate: Ensemble of greedy dynamic principal component analysis-Gaussian mixture model [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 151-161.
[14]	Pengbao Lian, Lizhen Chen, Dan He, Guangyuan Zhang, Zishuai Xu, Jianlong Wang. Crystallization thermodynamics of 2,4(5)-dinitroimidazole in binary solvents [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 173-182.
[15]	Zhenzhou Ma, Xu Hou, Bochong Chen, Liu Zhao, Enxian Yuan, Tingting Cui. Experiment and modeling of coke formation and catalyst deactivation in n-heptane catalytic cracking over HZSM-5 zeolites [J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 165-172.