Dynamic modeling approach for coal mill parameter estimation in thermal power plant

Mini Project Report, Mphill CS

Mphill. Computer Science 2014-2015 INT Elizabeth Sherly

Nowadays the demand of electricity is increasing but the conventional methods like hydroelectric power generation facing numerous difficulties .Therefore the urgency of the power plant is towering; these have impacted the coal mill and thermal power plant operation safety and reliability. Coal mills are the negligible part of the thermal power plant, in this paper we using a computational intelligent algorithm to estimate the unknown coefficients that are used in the coal milling model. Genetic Algorithm is chosen in this work as it is a stable algorithm for parameter identification, real-time and on-line implementation. The raw data used in modeling can be obtained without any extensive mill tests. The simulation results show a satisfactory agreement between the model response and measured value. The model is verified using on-site measurement data and on-line test. The mill-related features that affect milling performance include coal properties mainly calorific value, grind ability, abrasiveness, moisture content, mill types (low-speed mill, medium-speed mill, high-speed mill), pulverized coal distribution system that is presence of bifurcations and trifurcations, and control strategy, etc. We have tried to estimate the unknown parameter that affect the coal milling process at steady state condition implemented using Genetic Algorithm based on plant measurement data