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CLC number: TK22

On-line Access: 2021-10-18

Received: 2020-11-10

Revision Accepted: 2021-03-18

Crosschecked: 2021-09-26

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Citations:  Bibtex RefMan EndNote GB/T7714


Qin-xuan Hu


Qun-xing Huang


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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.10 P.777-791


A novel time-span input neural network for accurate municipal solid waste incineration boiler steam temperature prediction

Author(s):  Qin-xuan Hu, Ji-sheng Long, Shou-kang Wang, Jun-jie He, Li Bai, Hai-liang Du, Qun-xing Huang

Affiliation(s):  State Key Laboratory of Clean Energy Utilization, Institute of Thermal Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   hqx@zju.edu.cn

Key Words:  Waste incineration grate furnace, Neural network, Time-span input, Main steam temperature, Prediction

Qin-xuan Hu, Ji-sheng Long, Shou-kang Wang, Jun-jie He, Li Bai, Hai-liang Du, Qun-xing Huang. A novel time-span input neural network for accurate municipal solid waste incineration boiler steam temperature prediction[J]. Journal of Zhejiang University Science A, 2021, 22(10): 777-791.

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author="Qin-xuan Hu, Ji-sheng Long, Shou-kang Wang, Jun-jie He, Li Bai, Hai-liang Du, Qun-xing Huang",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A novel time-span input neural network for accurate municipal solid waste incineration boiler steam temperature prediction
%A Qin-xuan Hu
%A Ji-sheng Long
%A Shou-kang Wang
%A Jun-jie He
%A Li Bai
%A Hai-liang Du
%A Qun-xing Huang
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 10
%P 777-791
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000529

T1 - A novel time-span input neural network for accurate municipal solid waste incineration boiler steam temperature prediction
A1 - Qin-xuan Hu
A1 - Ji-sheng Long
A1 - Shou-kang Wang
A1 - Jun-jie He
A1 - Li Bai
A1 - Hai-liang Du
A1 - Qun-xing Huang
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 10
SP - 777
EP - 791
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000529

A novel time-span input neural network was developed to accurately predict the trend of the main steam temperature of a 750-t/d waste incineration boiler. Its historical operating data were used to retrieve sensitive parameters for the boiler output steam temperature by correlation analysis. Then, the 15 most sensitive parameters with specified time spans were selected as neural network inputs. An external testing set was introduced to objectively evaluate the neural network prediction capability. The results show that, compared with the traditional prediction method, the time-span input framework model can achieve better prediction performance and has a greater capability for generalization. The maximum average prediction error can be controlled below 0.2 °C and 1.5 °C in the next 60 s and 5 min, respectively. In addition, setting a reasonable terminal training threshold can effectively avoid overfitting. An importance analysis of the parameters indicates that the main steam temperature and the average temperature around the high-temperature superheater are the two most important variables of the input parameters; the former affects the overall prediction and the latter affects the long-term prediction performance.


目的:生活垃圾焚烧炉主蒸汽温度为炉内燃烧调控的重点监控对象.本文旨在建立一种时域输入的主蒸汽温度神经网络预测模型,以实现主蒸汽温度未来5 min变化趋势的精准预测,并且使预测误差控制在1%以内.
创新点:1. 实现了主蒸汽温度的未来趋势预测,而非当前值预测;趋势预测的结果能提供操作人员一定的参考价值.2. 提出了一种时域输入神经网络模型;该模型能够包含输入输出参数之间的延时特性,因此能获得更高的预测精度.
方法:1. 通过数据相关性分析与延时性分析,确定用于预测主蒸汽温度的输入变量,并减少模型输入层数据维度(表1);2. 提出时域输入算法设计(公式(4)~(5)),构建时域输入主蒸汽温度神经网络预测模型,以实现主蒸汽温度未来5 min变化趋势的精准预测(图8);3. 通过调整模型参数,优化模型结构;4. 通过输入数据敏感度分析,得出对主蒸汽温度预测影响最大的变量(图14).
结论:1. 本文提出的时域输入神经网络模型比传统神经网络模型的预测精度更高;2. 时域输入主蒸汽温度神经网络预测模型在未来1 min内可以实现近零预测误差;3. 根据输入数据敏感度分析可得,对于本研究的焚烧炉,主蒸汽温度本身的数据对于其预测的重要性最高;其次,高温过热器烟气平均温度对于主蒸汽温度远未来预测的重要性较高.


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[1]Al Shamisi MH, Assi AH, Hejase HAN, 2011. Using MATLAB to develop artificial neural network models for predicting global solar radiation in Al Ain city–UAE. In: Assi AH (Ed.), Engineering Education and Research Using MATLAB. InTech, Rijeka, Croatia.

[2]Bao CL, Zhang JF, 2013. Combustion optimization of power plant boilers based on RBF neural network model. Power Equipment, 27(2):97-100 (in Chinese).

[3]Basheer IA, Hajmeer M, 2000. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1):3-31.

[4]Bukovský I, Kolovratník M, 2012. A neural network model for predicting NOx at the Mělník 1. Acta Polytechnica, 52(3):17-22.

[5]Chavan PD, Sharma T, Mall BK, et al., 2012. Development of data-driven models for fluidized-bed coal gasification process. Fuel, 93:44-51.

[6]Chen DZ, Christensen TH, 2010. Life-cycle assessment (EASEWASTE) of two municipal solid waste incineration technologies in China. Waste Management & Research, 28(6):508-519.

[7]Golgiyaz S, Talu MF, Onat C, 2019. Artificial neural network regression model to predict flue gas temperature and emissions with the spectral norm of flame image. Fuel, 255:115827.

[8]Han L, Zhang ZY, 2012. The application of immune genetic algorithm in main steam temperature of PID control of BP network. Physics Procedia, 24:80-86.

[9]Iliyas SA, Elshafei M, Habib MA, et al., 2013. RBF neural network inferential sensor for process emission monitoring. Control Engineering Practice, 21(7):962-970.

[10]Kabugo JC, Jämsä-Jounela SL, Schiemann R, et al., 2020. Industry 4.0 based process data analytics platform: a waste-to-energy plant case study. International Journal of Electrical Power & Energy Systems, 115:105508.

[11]Kalogirou SA, 2000. Applications of artificial neural-networks for energy systems. Applied Energy, 67(1-2):17-35.

[12]Liukkonen M, Hiltunen T, Hälikkä E, et al., 2011. Modeling of the fluidized bed combustion process and NOx emissions using self-organizing maps: an application to the diagnosis of process states. Environmental Modelling & Software, 26(5):605-614.

[13]Meher SK, Behera SK, Kim MC, et al., 2015. Multiple decision expert systems for performance analysis of a boiler system. Applied Artificial Intelligence, 29(9):839-858.

[14]National Bureau of Statistics, 2018. China Statistical Yearbook 2018. China Statistics Press, Beijing, China (in Chinese).

[15]Norhayati I, Rashid M, 2018. Adaptive neuro-fuzzy prediction of carbon monoxide emission from a clinical waste incineration plant. Neural Computing and Applications, 30(10):3049-3061.

[16]Oko E, Wang MH, Zhang J, 2015. Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant. Fuel, 151:139-145.

[17]Olden JD, Jackson DA, 2002. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1-2):135-150.

[18]Pai TY, Lo HM, Wan TJ, et al., 2015. Predicting air pollutant emissions from a medical incinerator using grey model and neural network. Applied Mathematical Modelling, 39(5-6):1513-1525.

[19]Shaha AP, Singamsetti MS, Tripathy BK, et al., 2020. Performance prediction and interpretation of a refuse plastic fuel fired boiler. IEEE Access, 8:117467-117482.

[20]Shapiro-Bengtsen S, Andersen FM, Münster M, et al., 2020. Municipal solid waste available to the Chinese energy sector-provincial projections to 2050. Waste Management, 112:52-65.

[21]Smrekar J, Potočnik P, Senegačnik A, 2013. Multi-step-ahead prediction of NOx emissions for a coal-based boiler. Applied Energy, 106:89-99.

[22]Srivastava N, Hinton G, Krizhevsky A, et al., 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929-1958.

[23]Sung AH, 1998. Ranking importance of input parameters of neural networks. Expert Systems with Applications, 15(3-4):405-411.

[24]Tóth P, Garami A, Csordás B, 2017. Image-based deep neural network prediction of the heat output of a step-grate biomass boiler. Applied Energy, 200:155-169.

[25]Tzafestas SG, Dalianis PJ, Anthopoulos G, 1996. On the overtraining phenomenon of backpropagation neural networks. Mathematics and Computers in Simulation, 40(5-6):507-521.

[26]You HH, Ma ZY, Tang YJ, et al., 2017. Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. Waste Management, 68:186-197.

[27]Zhao M, Yan WJ, Zheng J, 2010. Combustion optimization modelling for utility boilers based on generalized dynamic fuzzy neural networks. Thermal Power Generation, 39(3):19-22 (in Chinese).

[28]Zhou H, Meng AH, Long YQ, et al., 2014. An overview of characteristics of municipal solid waste fuel in China: physical, chemical composition and heating value. Renewable and Sustainable Energy Reviews, 36:107-122.

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