Parameter Optimization for Forecasting of Boiler Losses and Efficiency using Statistical Model

Abstract

Estimation of boiler efficiency to the highest degree of acceptance is the need of the hour. The correct evaluation of boiler efficiency is hypothetical due to several reasons, as mathematical equations are complex, time consuming and prone to human error. The statistical model based estimation is more efficient. This paper presents the optimization of boiler parameters based on statistical models for the prediction of boiler losses and efficiency. The data is collected from a well established and sophisticated cement plant and is used for building the models and testing the forecast values of boiler losses and efficiency. Linear regression is performed for the prediction of boiler losses and boiler efficiency. Independent variables for regression analysis are selected from a large set of boiler parameters. Parameter selection for the prediction of both boiler losses and boiler efficiency is based on the basis of experience. The final independent variables are optimized to get higher accuracy. Six models are built. Three of these models are for individual boiler loss predictions and other three models for the prediction of boiler efficiency. Out of three models for boiler efficiency prediction, each model is built by considering a particular independent variable. The independent variables are: hydrogen content in coal, moisture in coal, and both hydrogen and moisture content in coal. The dependent variable in all three cases is boiler efficiency. The optimized model for prediction of boiler efficiency is built considering hydrogen and moisture content in coal as an independent variable.