Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

Автор: Douglas T. Mugweni, Hadi Harb

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 5 vol.11, 2021 года.

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Controlling drum level is a major and crucial control objective in thermal power plant steam boilers. The drum level as a controlled variable is highly characterized by complex non-linear process dynamics as well as measurement noise and long-time delays. Developing a data-driven process model is particularly advantageous as it could be built from ongoing operational data. Such a model could be used to assist existing controllers by providing predictions regarding the drum level. The aim of this paper is to develop such a model and to propose a control architecture that can be easily integrated into existing control hardware. For that purpose, different neural networks are used, Multilayer Perceptron (MLP), Nonlinear Autoregressive Exogenous (NARX), and Long Short Term (LSTM) neural networks. LSTM and MLP were able to capture the dynamics of the process, but LSTM showed superior performance. The results demonstrate that the use of traditional machine learning criteria to evaluate a process model is not necessarily adequate. Using the model in an open-loop and a closed-loop simulation is more suitable to test its ability to capture the dynamics of the process. A novel architecture that integrates the process model within an existing closed-loop controller is proposed. The architecture uses adaptive weights to ensure that a good model is given more influence than a bad model on the controller’s output.

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Drum level control, Steam boiler plant, Neural Networks, Proportional Integral Derivative control, System identification, Predictive control

Короткий адрес: https://sciup.org/15017836

IDR: 15017836   |   DOI: 10.5815/ijem.2021.05.01

Список литературы Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

  • Deshpande, Purva, Nilima Warke, Prakash Khandare, and Vijay Deshpande. "Thermal power plant analysis using artificial neural network." In 2012 Nirma University International Conference on Engineering (NUiCONE), pp. 1-6. IEEE, 2012.
  • Smrekar, J., Assadi, M., Fast, M., Kuštrin, I., & De, S. (2009). Development of artificial neural network model for a coal-fired boiler using real plant data. Energy, 34(2), 144-152.
  • Smrekar, J., Pandit, D., Fast, M., Assadi, M., & De, S. (2010). Prediction of power output of a coal-fired power plant by artificial neural network. Neural Computing and Applications, 19(5), 725-740.
  • ÅSTRÖM, Karl Johan et BELL, Rodney D. Drum-boiler dynamics. Automatica, 2000, vol. 36, no 3, p. 363-378.
  • CHEN, Sheng, BILLINGS, S. A., et GRANT, P. M. Non-linear system identification using neural networks. International journal of control, 1990, vol. 51, no 6, p. 1191-1214.
  • Kumpati, S. Narendra, and Parthasarathy Kannan. "Identification and control of dynamical systems using neural networks." IEEE Transactions on neural networks 1.1 (1990): 4-27.
  • Duan, Yanjie, Yisheng Lv, and Fei-Yue Wang. "Travel time prediction with LSTM neural network." 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016.
  • Sagheer, Alaa, and Mostafa Kotb. "Time series forecasting of petroleum production using deep LSTM recurrent networks." Neurocomputing 323 (2019): 203-213.
  • Zhao, Zheng, et al. "LSTM network: a deep learning approach for short-term traffic forecast." IET Intelligent Transport Systems 11.2 (2017): 68-75.
  • MATLAB 2019b, the MathWorks, Inc., Natick, Massachusetts, United States.
  • Wedajo T. Abdisa, Hadi Harb, "A Neural Network Based Motor Bearing Fault Diagnosis Algorithm and its Implementation on Programmable Logic Controller", International Journal of Intelligent Systems and Applications (IJISA), Vol.11, No.10, pp.1-14, 2019.
  • HU, L. J., ZHANG, Ke, et LIU, Tao. Study on the boiler drum water level based on fuzzy adaptive control. In: 24th Chinese Control and Decision Conference (CCDC). 2012. p. 1659-1663.
  • LU, C. X., REES, N. W., et DONALDSON, S. C. The use of the Åström-Bell model for the design of drum level controllers in power plant boilers. IFAC Proceedings Volumes, 2005, vol. 38, no 1, p. 139-144.
  • Sunil, P. U., Jayesh J. Barve, and PS V. Nataraj. "Boiler drum-level control using QFT." 2013 Nirma University International Conference on Engineering (NUiCONE). IEEE, 2013.
  • Qiliang, Yang, Xing Jianchun, and Wang Ping. "Water level control of boiler drums using one IEC61131-3-based DCS." 2007 Chinese Control Conference. IEEE, 2007.
  • Gowthaman, E., et al. "Performance analysis of hybrid fuzzy-PID controller action on boiler drum level control." 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016.
  • A. Kozáková, S. Bucz “Multiloop control of a drum boiler”, Journal of Electrical Systems and Information Technology 1 (2014) 26–35.
  • Boesack, Craig D., Priyanka Thakur, and Albert Smit. “Experiences on power plant modelling and its parameter estimation.” International Journal of Intelligent Information Processing 5.3 (2015): 67.
  • R. Horalek, J. Hlava, “Comparison of Linear and Nonlinear Model Predictive Control of Benchmark Drum Boiler”, Annals of DAAAM for 2011 & Proceedings of the 22nd International DAAAM Symposium, Volume 22, No. 1, ISSN 1726-9679.
  • Selvi, BS Thamarai, D. Kalpana, and T. Thyagarajan. “Modeling and prediction of boiler drum in a thermal power plant.” 2017 Trends in Industrial Measurement and Automation (TIMA). IEEE, 2017.
  • Oko, Eni, Meihong Wang, and Jie Zhang. "Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant." Fuel 151 (2015): 139-145.
  • G. A. Oluwande, “Exploitation of Advanced Control Techniques in Power Generation”, Computing and Control Engineering Journal, P63-67, April 2001, Institution of Engineering and Technology (IET).
  • Horvath, Gábor. “Neural Networks in Systems Identification”. Neural Networks for Instrumentation, Measurement and Related Industrial Applications, Edited by S. Ablameyko, L. Goras, M. Gori and V. Piuri. NATO Science Series 185 (2002): 43-78
  • Haykin, S. Neural Networks, A Comprehensive Foundation, Prentice Hall, New Jersey. 1994
  • Nguyen Q. et al., “Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil”, Mathematical Problems in Engineering, vol. 2021.
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