The invention relates to a
soot blowing optimization method for an intelligent
circulating fluidized bed boiler, belongs to the technical field of boiler equipment and
intelligent control, and is suitable for efficient operation and maintenance of circulating
fluidized bed (CFB) boilers in the industries of
electric power,
chemical engineering,
metallurgy and the like. The method aims at solving the technical problems that real-time sensing of the
soot deposition state is insufficient, and
soot blowing strategy
hysteresis is high. The method comprises the steps of multi-dimensional data real-time acquisition and fusion, dust deposition state intelligent prediction and
decision making, closed-loop feedback optimization and adaptive adjustment, and
system integration and intelligent operation and maintenance. A high-precision sensor network (including
hearth temperature,
flue gas parameters, heating surface
temperature gradient and the like) is deployed in a key area of a boiler, an
infrared thermal imaging or image recognition technology is combined to monitor an ash deposition state, and an ash deposition prediction model is constructed through a
machine learning
algorithm (such as LSTM,
random forest and CNN). Based on real-
time data and model analysis, the soot blowing frequency, strength and medium parameters are dynamically adjusted through fuzzy control or a self-adaptive PID
algorithm, and accurate soot blowing is achieved.