A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling
Artikel i vetenskaplig tidskrift, 2021

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant's NO emission rates is performed, directly comparing each modeling method's performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. These efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method's performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

Computational intelligence

NO emissions x

Intelligent systems

Nonlinear dynamical systems

Recurrent neural networks

Combustion modeling & optimization


Jacob F. Tuttle

University of Utah

Landen D. Blackburn

University of Utah

Klas Andersson

Chalmers, Rymd-, geo- och miljövetenskap, Energiteknik

Kody M. Powell

University of Utah

Applied Energy

0306-2619 (ISSN) 18729118 (eISSN)

Vol. 292 116886


Chalmers kraftcentral


Bioinformatik (beräkningsbiologi)





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