Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization

Abstract

This paper proposed an intelligent approach to predict the biochar yield. The biochar is an important renewable energy that produced from biomass thermochemical processes with yields that depend on different operating conditions. There are some approaches that are used to predict the production of biochar such as least square support vector machine. However, this approach suffers from some drawbacks such as get stuck in local point and high time complexity. In order to avoid these drawbacks, the adaptive neuro-fuzzy inference system approach is used and this approach is trained with a particle swarm optimization algorithm to improve the prediction performance of the biochar. Heating rate, pyrolysis temperature, Moisture content, holding time and sample mass were used as the input parameters and the outputs are biochar mass and biochar yield. The results show that the proposed approach is better than other approaches based on three measures the root mean square error, the coefficient of determination and average absolute percent relative error (0.2673, 0.9842 and 3.4529 respectively).