| With China entering the WTO, our national economy is faced with another opportunity for fast development and the electric industry as the base of the national economy becomes more and more important. In the future, all electric power plants will carry out the work named "online price competition", so the internal management chiefly aims at reducing energy consumption .It is obvious that improving the reliability and economic gains of coal-fired boiler are essential to the existence and development for the electric power plant.The thermal efficiency is the most important economy index to measure the coal-fired boiler and the generator. Adjusting combustion of operational condition is a great measure to improve the thermal efficiency of coal-fired boiler. The flue gas heat loss q2 and non-full burning loss q4 are the important parameters to instruct and optimize the boiler operation. Some factors are often influenced by others, so it is a multivariable coupling system. In this thesis, an optimization method was applied in analyzing the relation of this complicated system, on the basis of the theoretical analysis and practical calculation, this paper puts forward to a synthetic evaluation model and it does make any sense.The adjustment of the operation of the boiler and the combustion base on the on-line calculation of coal-fired boiler, so it is a very important issue .The flue gas heat loss q2 and non-full burning loss q4 are the important index to measure the thermal efficiency of coal-fired boiler.In this thesis, the neural network was applied in constructing evaluation model to forecast the thermal efficiency. The demand of accuracy and time are satisfied by utilizing a changed BP network to forecast q2 and q4 of coal-fired boiler. The work basically accomplish the purpose of real time on-line inspecting the thermal efficiency and adjusting operation condition of boiler and enhancing the boiler economic bene fit.
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