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

Abstract

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 (NOx), 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, Tg, CO, NOx, 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.