The modeling and products prediction for biomass oxidative pyrolysis based on PSO-ANN method: An artificial intelligence algorithm approach

In this work, the oxidative pyrolysis of two kinds of biomass was conducted on a fixed bed reactor. The pyrolysis product yields (water, tar, gas and char) as well as two kinds of gaseous species (CO and CO2) were analyzed. Based on the experiments, the artificial neural network (ANN) model was built and trained for product yield prediction. Experimental results show that the reaction mechanism of oxidative pyrolysis was much complicated than inert pyrolysis. With the addition of oxygen, homogenous and heterogenous oxidation of volatiles or nascent char significantly influenced the product distribution. Modeling results showed that the combination transfer function of logsig and purelin for hidden layer was suitable for modeling of biomass oxidative pyrolysis. Individual ANN model had weak prediction performance for the data points located around the boundary of database. This weakness as well as the tendency of local convergence leaded to the decline of prediction accuracy for individual ANN model. Particle swarm optimization (PSO) was further used to optimize the initial weight and threshold value of the ANN model, which significantly increased the prediction accuracy. The relative error of the PSO-ANN model decreased to below 10%, which proved to be a effective tool for modeling of biomass oxidative pyrolysis.

Automated summary

The experimental results showed that the reaction mechanism of oxidative pyrolysis was complex and influenced by the addition of oxygen. Using a combination of logsig and purelin as the transfer function for the hidden layer was suitable for modeling. Particle swarm optimization (PSO) significantly increased the prediction accuracy of the ANN model.