Artificial Neural Network Forecasting of Dense Phase Pneumatic Conveying Parameters

Dense phase pneumatic conveying experiment of fine coal and millet are carried out using compress air and hydrogen at horizontal pipeline. Solid mass rate at different gas flow is forecasted by using improved BP neural network in dense phase pneumatic conveying. The result shows improved BP neural network can successfully forecast solid mass rate under different experimental conditions and solid mass rate contour diagram at different gas flow is plotted. According to the diagram, parameters of dense phase pneumatic conveying can be optimized, solids mass rate can be controlled, and blind operation of dense phase pneumatic conveying can be decreased.