RESUMEN
Bioplastic has been perceived as a promising candidate to replace petroleum-based plastics due to its environment-friendly and biodegradable characteristics. This study presents the chitosan reinforced starch-based bioplastic film prepared by the solution casting and evaporation method. The effects of processing parameters, i.e., starch concentration, glycerol loading, process temperature and chitosan loading on mechanical properties were examined. Optimum tensile strength of 5.19 MPa and elongation at break of 44.6% were obtained under the combined reaction conditions of 5 wt.% starch concentration, 40 wt.% glycerol loading, 20 wt.% chitosan loading and at a process temperature of 70 °C. From the artificial neural network (ANN) modeling, the coefficient of determination (R2) for tensile strength and elongation at break were found to be 0.9955 and 0.9859, respectively, which proved the model had good fit with the experimental data. Interaction and miscibility between starch and chitosan were proven through the peaks shifting to a lower wavenumber in FTIR and a reduction of crystallinity in XRD. TGA results suggested the chitosan-reinforced starch-based bioplastic possessed reasonable thermal stability under 290 °C. Enhancement in water resistance of chitosan-incorporated starch-based bioplastic film was evidenced with a water uptake of 251% as compared to a 302% registered by the pure starch-based bioplastic film. In addition, the fact that the chitosan-reinforced starch-based bioplastic film degraded to 52.1% of its initial weight after 28 days suggests it is a more sustainable alternative than the petroleum-based plastics.
RESUMEN
The purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805-0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259-2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy.
