By changing operating conditions

such as conveying pressure P1

total differential pressure ΔPT and fluidizing gas flow rate Qf

and by changing properties of pulverized coal

such as particle size dp

moisture content W and coal category

experimental investigations on solids flux ψ of pulverized coal were conducted on a pilot scale experimental setup of high-pressure and dense-phase pneumatic conveying. Considering the complexity of high-pressure and dense-phase solids-gas flow

artificial neural network(ANN)was introduced based on sufficient experiments.Original BP neural network and two kinds of improved algorithm were used and their simulation and prediction results were compared.The results showed that ψ increased with P1 and ΔPT

and it increased firstly and then decreased with Qf.ψ reduced with the increase of dp and W

and it was also affected by coal category.The two kinds of improved algorithm in BP neural network could successfully simulate and predict ψ of pulverized coal

however

convergence speed and prediction accuracy could not be satisfied simultaneously.The work in this paper can guide the control

operation

simulation and prediction of key characteristic parameters for high-pressure and dense-phase pneumatic conveying system.