Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Department of Mechanical Engineering, Politeknik Negeri Medan, Medan, North Sumatra, 20155, Indonesia.
Environ Sci Pollut Res Int. 2017 Nov;24(32):25383-25405. doi: 10.1007/s11356-017-0141-9. Epub 2017 Sep 20.
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.
本研究旨在研究以麻疯树生物柴油-柴油混合物为燃料的四缸共轨涡轮增压柴油机的性能、排放和燃烧特性。本研究使用 MATLAB 软件开发了基于核的极限学习机（KELM）模型，以预测发动机的性能、燃烧和排放特性。为了获取用于训练和测试 KELM 模型的数据，选择发动机转速作为输入参数，而性能、废气排放和燃烧特性则作为 KELM 模型的输出参数。通过将预测数据与实验数据进行比较，验证了 KELM 模型预测的性能、排放和燃烧特性。结果表明，对于 KELM 模型和实验数据，参数的确定系数都在 0.9805-0.9991 的范围内。平均绝对百分比误差在 0.1259-2.3838 之间。本研究表明，KELM 建模是生物柴油生产中的一项有用技术，因为它可以帮助科学家和研究人员准确地预测内燃机的性能、废气排放和燃烧特性。
