Artificial neural network prediction of CO emission and ash yield from co-combustion of empty fruit bunch, palm kernel shell and kaolin.

The quantity of ash yield and carbon monoxide (CO) emitted during co-combustion of empty fruit bunch (EFB), palm kernel shells (PKS) and kaolin in a grate furnace depend on the fuels mixing ratio, the combustion temperature and duration. These factors can be tuned to minimize ash deposition and CO emission which is partly responsible for the greenhouse effect. In this study, seventy-three (73) data points were obtained from combustion of EFB, PKS and kaolin mixtures based on D-optimal design. Artificial neural network (ANN) model, optimized with Taguchi technique, was developed to predict ash yield (AY) and CO emission from the combustion of the fuel mixture. The data were divided into training, validation and testing in a 2:1:1 relative proportion. The optimized ANN architecture for AY and CO emission were 5-11-3-1 and 5-6-3-1, respectively, with scale conjugate gradient training algorithm and a learning rate of 0.1. Results of the ANN model agreed significantly with the experimental results with coefficients of determination (R) of 0.96 and 0.93 for ash yield and CO emission, respectively. The mathematical models for the ash and CO emission using the D-optimal design indicate a good fit with R of 0.916 and 0.906, respectively. Parametric studies based on the two models showed that ash yield and CO emission reduced with increased combustion temperature and increased fraction of PKS within the temperature range of 800-1000 °C. These results indicated that both ANN and D-optimal can be deployed to select mixture with minimal ash yield and CO emission.