Abstract: In addition to energy consumption of grinding section in wheat pulverization,the pneumatic conveying section is also a major aspect of energy consumption.Analyilzing pneumatic conveying energy consumption can provide foundation for decreasing energy consumptionin pneumatic conveying section based on nonlinear mathematical model of pneumatic conveying,redundancy of equipment matching parameters,and pipe pressure loss by processing and flour texture fluctuation effect.So it was necessary to analyze the energy consumption model of pneumatic conveying in order to reduce the energy consumption in the flour pneumatic conveying section.The nonlinear relationship between the condition of the powder and the energy consumption wascomprehensively considered.The BP (Back Propagation) Neural Network with strong adaptive ability and self-organization ability was selected to establish the energy consumption model of pneumatic conveying flour section,and adjusting the training and testing ratio of the set processing data,the total error and the number of cycles in the model training process,etc.The pneumatic conveying section of the powder workshop of 500 t/da energy production in a flour mill in Henan,sample data of per hour processing ton,wheat grain humidity and air supply volume were collected as samples.The energy consumption model was trained,predicted and verified,and then the power consumption and the measurement value were compared with the equipment parameters,pipeline marking and unit production volume.The results showed that using the BP neural network fitting algorithm was feasible to build the energy loss model of the pneumatic conveying section.The mathematical model can better reflect the energy consumption in the pneumatic conveying section.Meanwhile the simulation results can predict accurately the conveying section of flour pneumatic energy consumption;the error was less than 7%.