The Optimized Design and Application of Intelligent SootBlowing System Based on Cleaning Coefficient and Neural Network

摘要

Aiming at the disadvantage of power plant sootblowing system presently, this paper takes 330MW unit boiler as research object, firstly calculates boiler optimal sootblowing frequency and cleaning coefficient when the heat net income of heat absorbing surface is maximum. And then based on neural network software, both boiler convection heat absorbing surface and radiate heat absorbing surface are implemented real-time monitoring by using DCS data. At the same time, it compares monitoring cleaning coefficient with critical cleaning coefficient real-time, and the optimized system will alarm and instructs the operator to take sootblowing command when it requirements. The reality application performence in power plant has proved that this system have great advantage and practicability in energy-saving.