Multi-parameter sensitivity analysis of polyhydroxyalkanoate (PHA) production utilizing sludge hydrolysis liquid based on BP neural network method

Multi-parameter sensitivity analysis method was proposed to analyze the parameters affecting the yield of Polyhydroxyalkanoate (PHA) utilizing sludge hydrolysis liquid. Based on experiment dates, a model based on back propagation neural network (BPNN) used for predicting the yield of PHA was set up. The accuracy of this predicted model was verified by contrastive analysis between theoretical and laboratorial data. On the basis of weights and threshold value of each variable parameter gained in the trained BPNN, Garson algorithm was used for calculating the parameter sensitivity coefficient. Results show the prediction model built by BPNN has high credibility, and multi-parameters sensitivity analysis method can evaluate the impact of multi-factor on PHA production yield therefore has greater practical value.