An experimental and modelling approach to produce biochar from banana peels through pyrolysis as potential renewable energy resources

Abstract

Biochar obtained from biomass through pyrolysis can be considered as an alternative source of renewable energy. In this work, banana peels were used as feedstock in the pyrolysis process to produce biochar. Experimental work was conducted using an electric tubular furnace and a Thermogravimetric Analyzer (TGA). The effect of temperature was investigated together with heating rate and residence time. Results show that biochar with highest energy content with respect to char yield and heating value was obtained at 325 °C. The yield was 47 wt% and its gross heating value was 25.9 MJ kg−1. The residence time was 30 min. The heating rate had minimal effect on the char yield and heating value. Modelling of pyrolysis process was undertaken using feedforward neural network method. The modelling work focused on the effect of temperature and time on the weight loss of the banana peels during pyrolysis thermal degradation in the TGA. Neural network model was developed based on single layer hidden neurons using Matlab™ software. The model was trained based on the backpropagation technique using the Levenberg–Marquardt optimization algorithm. An iterative program was used to determine the best model for each hidden neuron. Results show that neural network model with hidden neurons 21 is the best with lowest mean squared error (MSE = 0.35451) and highest output–target correlation value (R2 = 0.99958). It can be concluded that renewable energy source can be obtained using banana peels as the feedstock for the pyrolysis process.