Authors : K.V. Narayanan , E. Natarajan and B.S. Sreejaa
Abstract: Concerns regarding the potential global environmental impacts of fossil fuels used in power generation and other energy supplies are increasing worldwide. One of the methods of mitigating these environmental impacts is increasing the fraction of renewable and sustainable energy in the national energy usage. A number of techniques and methods have been proposed for reducing gaseous emissions of NOx, SO2 and CO2 from fossil fuel combustion and for reducing costs associated with these mitigation techniques. Some of the control methods are expensive and therefore increase production costs. Among the less expensive alternatives, cofiring has gained popularity with the electric utilities producers. This study discusses about the emission model for cofiring of coal and biomass using Artificial Neural Network. The model uses an Elman network which is trained and simulated using back propagation algorithm. The ANN model was constructed and trained with experimental data got when biomass was cofired in 18.68 MW power plant with bituminous coal in 3 proportions of 20, 40 and 60% by mass. Bagasse, wood chips (Julia flora), sugarcane trash and coconut shell are the biomass fuels cofired with coal in this study. The impacts of mixing ratio and bed temperature on the emissions were considered. The model predicted emissions were consistent with experimental data.
K.V. Narayanan , E. Natarajan and B.S. Sreejaa , 2006. Artificial Neural Network Model for Emissions from Cofiring of Coal and Biomass in A Travelling Grate Boiler in India. Journal of Engineering and Applied Sciences, 1: 355-359.