Recent developments in internal combustion engines have heightened the need for alternative biofuel. In the last five years, acetone-butanol-ethanol (ABE) has been extensively studied as a promising biofuel. However, the detailed investigation of its fuel properties has not been performed. One of the vital fuel properties is the cetane index. It is used to define the ignition quality of fuel, but its determination is painstaking and expensive. No previous study has utilized both empirical mathematical and ANN models to predict the cetane index of ABE-diesel blends. This study aims to predict ABE’s cetane index by comparing five empirical mathematical models with seven artificial neural networks (ANN) training algorithms. To the best of our knowledge, this is the first study to examine the cetane index of ABE-diesel blends using both empirical and ANN models. Results revealed that the feed-forward backpropagation network with 4 input, 10 hidden, and 1 output neurons that was trained with Levenberg-Marquardt algorithm (ANN-LM) showed the best performance with the highest values of R (0.9992) and R² (0.9984). It also has the lowest values of MAD, MSE, RMSE and MAPE at 0.2572, 0.4456, 0.6675, and 0.5304, respectively. As compared to the best empirical mathematical model (the 3rd order polynomial), the ANN-LM had slightly better performance accuracy. Therefore, the 4–10-1 ANN structure trained with Levenberg-Marquardt was found to be the best predictor for cetane index of ABE-diesel blends at various blending ratios.

内燃机的最新发展已经增加了对替代生物燃料的需求。在过去的五年中，丙酮-丁醇-乙醇（ABE）作为一种有前途的生物燃料已得到广泛研究。但是，尚未对其燃料特性进行详细研究。至关重要的燃料特性之一是十六烷指数。它用于定义燃料的点火质量，但确定起来费力又费力。以前没有研究利用经验数学模型和ANN模型来预测ABE-柴油混合物的十六烷指数。本研究旨在通过将五个经验数学模型与七个人工神经网络（ANN）训练算法进行比较来预测ABE的十六烷指数。据我们所知，这是首次使用经验模型和ANN模型研究ABE-柴油混合物十六烷指数的研究。结果显示，使用Levenberg-Marquardt算法（ANN-LM）训练的具有4个输入，10个隐藏和1个输出神经元的前馈反向传播网络显示了最佳性能，其中R（0.9992）和R值最高²（0.9984）。它还具有最低的MAD，MSE，RMSE和MAPE值，分别为0.2572、0.4456、0.6675和0.5304。与最佳经验数学模型（三阶多项式）相比，ANN-LM的性能精度稍高。因此，发现用Levenberg-Marquardt训练的4–10-1 ANN结构是在各种混合比下ABE-柴油混合物十六烷指数的最佳预测指标。