基于径向基函数网络的WCO酯生物柴油性能与排放特性预测

Shiva Kumar, P. Pai, B. R. S. Rao
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引用次数: 12

摘要

径向基函数神经网络(RBFNNs)是一类较新的神经网络,研究了其在废油柴油发动机性能和排放特性预测中的适用性。以负载百分比、压缩比、混合百分比、喷射正时和喷射压力为输入参数,以制动热效率(BTE)、制动比能耗(BSEC)、排气温度(Texh)和发动机排放为输出参数,对RBF网络进行训练。随机选取RBF中心的个数。首先使用启发式方法对RBF单元进行变宽度的训练,然后使用固定宽度的训练。研究表明,在较宽的工况范围内,RBFNN的预测结果与实验结果吻合较好。对性能参数的预测精度均在90%以上,对排放参数的预测精度均在70%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester
Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO). The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), exhaust gas temperature (Texh), and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters.
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