STATISTICAL MODELING OF AN INTEGRATED BOILER

Abstract

In the modern world we all are able to see the amount of energy needed for fulfillment of the demand . So for meeting the demand we had established power plants that gives energy. In this scenario we have more number of thermal power plant that produces energy in the form of electricity after burning the fossils. After surveying so number of energy plants we have decided that the old existing power plants are based on the subcritical technology that has a low efficiency and it is not capable of meeting the demand of energy . So we need to focus on these plants to increasing the efficiency of the thermal power plants. That plants which is working on the subcritical technology has efficiency around 34%. But if we look modern world then modern thermal power station working on the principle of supercritical technology and these plants getting efficiency around 53%. So after discussing all the parameters we concluded that we need to increase the efficiency of old existing power plants for reducing the gap of supply and demand pattern. After getting new regression analysis model we check its capabilities and effectiveness with the help of coefficient of determination methods like we are performed R2 techniques. In this we can know the how many number of independent variables has significant effect of dependent variable. After this we performed the error analysis. First we calculate the individual unit error and then compared to actual model and after that we performed combined model error analysis. For these we used Response Surface Methodology in combination of DOE.