Comparative study of machine learning methods integrated with different optimisation algorithms for prediction of thermal performance and emissions in a pellet stove

ABSTRACT

The parameters of thermal power, excess air, and supply air distance at burner pot are critical to obtain low emissions and high combustion efficiency in a pellet stove. The main aim of this study is to predict the performance and gaseous emissions in a pellet stove with four different supply airflow distances of 15, 30, 60, and 75 mm above the burner pot base at different excess air ratios and thermal power inputs. For this purpose, different models, namely, support vector regression, k-nearest neighbors, and deep neural network, are developed to predict the performance and emissions of the pellet stove. The thermal power, excess air ratio, and supply air distance were used as input variables to predict the flue gas temperature, efficiency, carbon monoxide (CO), nitrogen oxides (NO_x), and carbon dioxide (CO₂) emissions. The hyperparameters of the designed models are tuned using several optimization approaches: grid search, Bayesian optimization, particle swarm, and genetic algorithms. Experiments are conducted to validate the performances of the models with optimized hyperparameters using statistical metrics such as coefficient of determination, mean-squared error, mean absolute error, and mean absolute percentage error. Analysis results indicate that the deep neural network performs the best with the highest correlation coefficient and lowest error metrics in the prediction of output parameters. The coefficient of determination (R²) values of the DNN model were obtained as 0.8875, 0.8464, 0.934, 0.8692, and 0.9992 in the training phase 0.909, 0.8760, 0.9164, 0.9086, and 0.9991 in the testing phase for the efficiency, T_g, CO, NO_x, and CO₂ parameters, respectively. The results of this study can serve as a guide to help researchers, engineers, and facility owners or operators predict the gaseous emissions levels and to get an idea of the impact of operating conditions on performance and emissions behaviors.

KEYWORDS:

• Biofuels
• Burner pot
• Combustion
• Domestic stoves
• Hyperparameter optimization
• Regression models

Acknowledgements

This work has been supported by the Scientific Research Projects Commission of Samsun University, Turkey with a project number of BAP.MÜF.5503.2021.01. The authors also would like to express their appreciation to the Ifyil/Kuzey Kardelen Heat Industry Company for supports provided to this experimental study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Alirıza Kaleli

Alirıza Kaleli received M.S., and Ph.D. degrees from the Department of Electrical and Electronics Engineering, Atatürk University, Erzurum in Turkey, 2009 and 2014, respectively. Presently he is an associate professor in the Department of Electrical and Electronics Engineering, Samsun University, Samsun in Turkey. He also currently serves as the Head of the Department. His research interests include the development and application of modelling and control theory to a variety of mechatronic systems, with a focus on observation and estimation-based control, on the analysis and control of dynamical systems that arise in engineering applications, and also include machine learning, neural networks, and their applications to dynamic systems and electrical and electronic engineering.

Bilal Sungur

Bilal Sungur received his BSc in Mechanical Engineering from the Ataturk University, Erzurum, Turkey, in 2010. He obtained his MSc and PhD in Mechanical Engineering from the Ondokuz Mayis University, Samsun, Turkey, in 2013 and 2019, respectively. He is currently working as a Lecturer at the Mechanical Engineering Department of Samsun University, Samsun, Turkey. His research interests are numerical modelling and analysis of combustion, air pollution control and energy systems.

Cem Basar

Cem Basar received his BSc in Mechanical Engineering from the Ondokuz Mayis University, Samsun, Turkey, in 2017. He obtained his MSc in Mechanical Engineering from the Samsun University, Samsun, Turkey, in 2023. He is engaged in research on combustion of renewable fuels and energy systems.