Aiming at improving the economical efficiency of boiler soot blowing system

this paper takes a 330 MW unit boiler as the object to calculate the optimum soot blowing frequency and clean coefficient when the heat-absorbent net income of the heating surface reaches the maximization.Moreover

based on the neural network software

real-time monitoring for ash depositing situation on convective heating surface and radiant heating surface was carried out through data collected from DCS

so as to compare and monitor the clean coefficient and the critical value timely

thus to take initiative to soot blowing.Application results indicate that

the system with real-time reflecting of ash deposition condition in furnace can instruct the soot blowing be taken according to the demand.Therefore

the energy consumption caused by blind soot blowing is reduced.