Prediction of FBC Boiler Efficiency using Machine Learning Algorithm

It is now clear that a fluidized bed combustion boiler is a feasible choice with many benefits over a traditional firing system. These boilers burn coal, washery waste, rice husk, bagasse, and other agricultural wastes as fuels. Calculations of boiler efficiency using the conventional method is time-consuming and very expensive. The work presented in this paper deals with establishing the statistical model for boiler efficiency using boiler parameters. Different machine learning algorithm like, Multilinear regression, support vector regression, K-nearest neighbors and decision tree regression have been used to build the boiler efficiency model. K-nearest neighbors algorithm shows the best model accuracy. So we can predict Fluidized bed combustion boiler efficiency using real time boiler parameters.