关键词: 高压, 气力输送, 输送速率通量, 神经网络

Abstract: By changing operating conditions, such as conveying pressure P₁, total differential pressure ΔP_T and fluidizing gas flow rate Q_f, and by changing properties of pulverized coal, such as particle size d_p, 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 ΔP_T, and it increased firstly and then decreased with Q_f.ψ reduced with the increase of d_p 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.

Key words: high-pressure, pneumatic conveying, solid flux, neural network