In order to realize reasonable control of particle circulating flow rate

the effects of control parameters on circulating flow rate were analyzed on a self-built double-circulating fluidized bed cold system

such as the wind speed of the bubbling bed

total wind speed and air distribution ratio of the fast bed

static bed height of the bubling bed

and the average particle size. Moreover

on the basis of three different weight optimization algorithms

like the additional momentum method

Levenberg-Marquardt(LM) algorithm and genetic algorithm(GA)

the BP neural network model was established

and the errors between the model predicted values and experimental values were compared. The results show that

the particle circulating flow rate was less influenced by the wind speed in bubbling bed

it increased with the primary air ratio and total wind speed of the fast bed and the static height of the bubling bed

but decreased with the increasing average particle size. The average error between the test sample and the one predicted by the GA-optimized BP neural network was 0.436 5%

the standard deviation was 0.064 1. The predicted values agreed well with the experimental values

indicating the BP neural network model was suitable.