Study on Modeling and Prediction of Carbon Paper Base Paper Properties Based on GA-BPNN Algorithm
In this study,carbon paper base paper was prepared by changing carbon fiber length,carbon fiber/PVA fiber mass fraction,dis-persant dosage and other process parameters.The influences of different process parameters on tensile strength,porosity,air permeability,and resistivity were explored.In addition,genetic algorithm was used to improve the back-propagation neural network algorithm(GA-BPNN algorithm),to construct a performance prediction model of carbon paper base paper.The results showed that the length of carbon fiber was positively correlated with the tensile strength,porosity,and air permeability of carbon paper base paper,and negatively correlated with the electrical resistivity.The mass fraction of carbon fiber to PVA fiber was negatively correlated with the tensile strength of carbon paper base paper,but positively correlated with porosity,permeability,and resistivity.The dosage of dispersant was positively correlated with the ten-sile strength and resistivity of carbon paper base paper,but negatively correlated with the porosity and permeability of carbon paper base pa-per.The mean relative error(MRE)of the prediction models for the tensile strength,porosity,permeability,and resistivity of carbon paper base paper were 5.49％,5.75％,5.21％,and 5.54％,respectively,and the MRE of the prediction models were all less than 10％,which was consistent with the relationship trend of the experimental process parameters on the properties of carbon paper base paper.
carbon paper base paperback-propagation neural network algorithmprediction model
万方数据
维普
