Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory

Abstract With the rapid development of renewables, increasing demands for the participation of coal-fired power plants in peak load regulation is expected. Frequent transients result in continuous, wide variations in NOX emission at the furnace exit, which represents a substantial challenge to the operation of SCR systems. A precise NOX emission prediction model under both steady and transient states is critical for solving this issue. In this study, a deep learning algorithm referred to as long short-term memory (LSTM) was introduced to predict the dynamics of NOX emission in a 660 MW tangentially coal-fired boiler. A total of 10000 samples from the real power plant, covering 7 days of operation, were employed to train and test the model. The learning rate, look-back time steps, and number of hidden layer nodes were meticulously optimized. The results indicate that the LSTM model has excellent accuracy and generalizability. The root mean square errors of the training data and test data are only 7.6 mg/Nm3 and 12.2 mg/Nm3, respectively. The mean absolute percentage errors are within 3%. Additionally, a comparative study between the LSTM and the widely used support vector machine (SVM) was conducted, and the result indicates that the LSTM outperforms the SVM.

[1]  D. Dunn-Rankin,et al.  Ammonium bisulfate formation and reduced load SCR operation , 2017 .

[2]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Fang Wan,et al.  Analysis on Practical Application Problems of SCR Technology In Coal- Fired Power Plants , 2011 .

[5]  Hao Zhou,et al.  Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks , 2004 .

[6]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[7]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Jie Zhang,et al.  Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant , 2015, Fuel.

[10]  Lutz Prechelt,et al.  Automatic early stopping using cross validation: quantifying the criteria , 1998, Neural Networks.

[11]  Jingge Song,et al.  Improved artificial bee colony-based optimization of boiler combustion considering NOX emissions, heat rate and fly ash recycling for on-line applications , 2016 .

[12]  Ron Cass,et al.  Adaptive Process Optimization using Functional-Link Networks and Evolutionary Optimization , 1996 .

[13]  Primož Potočnik,et al.  Multi-step-ahead prediction of NOx emissions for a coal-based boiler , 2013 .

[14]  Soteris A. Kalogirou,et al.  Artificial intelligence for the modeling and control of combustion processes: a review , 2003 .

[15]  Soteris A. Kalogirou,et al.  Applications of artificial neural networks in energy systems , 1999 .

[16]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[17]  Feng Wu,et al.  Combining support vector regression and cellular genetic algorithm for multi-objective optimization of coal-fired utility boilers , 2009 .

[18]  Qiang Yao,et al.  Clean Coal Technologies in China: Current Status and Future Perspectives , 2016 .

[19]  Peifeng Niu,et al.  Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization , 2013 .

[20]  Zhansong Wu,et al.  Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems , 2011 .

[21]  Moustafa Elshafei,et al.  Soft sensor for NOx and O2 using dynamic neural networks , 2009, Comput. Electr. Eng..

[22]  Eugenio Schuster,et al.  Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms , 2009 .

[23]  Gang Chen,et al.  Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method , 2016 .

[24]  Bernd Epple,et al.  Progress in dynamic simulation of thermal power plants , 2017 .

[25]  Jizhen Liu,et al.  An adaptive least squares support vector machine model with a novel update for NOx emission prediction , 2015 .

[26]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[27]  Zheng Yao,et al.  A new approach for function approximation in boiler combustion optimization based on modified structural AOSVR , 2009, Expert Syst. Appl..

[28]  Ronald J. Williams,et al.  Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .

[29]  Feng Hong,et al.  A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data , 2017 .

[30]  V. Selladurai,et al.  ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler , 2013, Clean Technologies and Environmental Policy.

[31]  Martin Schmitz,et al.  Development and validation of a dynamic simulation model for a large coal-fired power plant , 2015 .

[32]  Wojciech Zaremba,et al.  Recurrent Neural Network Regularization , 2014, ArXiv.

[33]  Guoqiang Li,et al.  Combustion optimization of a coal-fired boiler with double linear fast learning network , 2016, Soft Comput..

[34]  Ji Xia,et al.  NO X Emission Model for Coal-Fired Boilers Using Principle Component Analysis and Support Vector Regression , 2016 .

[35]  Ming Zhou,et al.  A Recursive Recurrent Neural Network for Statistical Machine Translation , 2014, ACL.

[36]  Jizhen Liu,et al.  A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler , 2013 .

[37]  Hao Zhou,et al.  Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization , 2012, Eng. Appl. Artif. Intell..