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首页> 外文期刊>Energy Conversion & Management >Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN)
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Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN)

机译：使用由人工神经网络（ANN）改编的偏最小二乘（PLS），从脂肪酸甲酯（FAME）分布图准确估算生物柴油十六烷值（CN）

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Cetane number (CN) is among the most important properties of biodiesel because it quantifies combustion speed or in better words, ignition quality. Experimental measurement of biodiesel CN is rather laborious and expensive. However, the high proportionality of biodiesel fatty acid methyl esters (FAMEs) profile with its CN is very appealing to develop straightforward and inexpensive computerized tools for biodiesel CN estimation. Unfortunately, correlating the chemical structure of biodiesel to its CN using conventional statistical and mathematical approaches is very difficult. To solve this issue, partial least square (PLS) adapted by artificial neural network (ANN) was introduced and examined herein as an innovative approach for the exact estimation of biodiesel CN from its FAMEs profile. In the proposed approach, ANN paradigm was used for modeling the inner relation between the input and the output PLS score vectors. In addition, the capability of the developed method in predicting the biodiesel CN was compared with the basal PLS method. The accuracy of the developed approaches for computing the biodiesel CN was assessed using three statistical criteria, i.e., coefficient of determination (R-2), mean-squared error (MSE), and percentage error (PE). The ANN-adapted PLS method predicted the biodiesel CN with an R2 value higher than 0.99 demonstrating the fidelity of the developed model over the classical PLS method with a markedly lower R2 value of about 0.85. In order to facilitate the use of the proposed model, an easy-to-use computer program was also developed on the basis of ANN-adapted PLS method for determining the biodiesel CN from its FAMEs profile. (C) 2016 Elsevier Ltd. All rights reserved.
机译：十六烷值（CN）是生物柴油最重要的特性之一，因为它可以量化燃烧速度，或者更准确地说，是燃烧质量。生物柴油CN的实验测量相当费力且昂贵。但是，生物柴油脂肪酸甲酯（FAMEs）轮廓与其CN的比例很高，非常吸引人，以开发出直接且廉价的计算机工具来估算生物柴油CN。不幸的是，使用常规的统计和数学方法将生物柴油的化学结构与其CN关联起来非常困难。为了解决这个问题，本文介绍了人工神经网络（ANN）改编的偏最小二乘（PLS），并将其作为一种创新的方法，根据其FAMEs曲线准确估算生物柴油CN。在提出的方法中，使用ANN范式对输入和输出PLS得分向量之间的内部关系进行建模。此外，将开发的方法预测生物柴油CN的能力与基础PLS方法进行了比较。使用三种统计标准，即确定系数（R-2），均方误差（MSE）和百分比误差（PE），评估了开发的计算生物柴油CN的方法的准确性。适应ANN的PLS方法预测的生物柴油CN的R2值高于0.99，这表明所开发模型的保真度高于经典PLS方法，其R2值较低，约为0.85。为了方便使用所提出的模型，还基于ANN的PLS方法开发了一种易于使用的计算机程序，用于从其FAMEs轮廓确定生物柴油CN。 （C）2016 Elsevier Ltd.保留所有权利。

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