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首页> 外文期刊>Fuel Processing Technology >Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network
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Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network

机译：基于近似分析的煤总热值预测：支持向量机，交替条件期望和人工神经网络的比较

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摘要

The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including hidden neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale. (C) 2014 Elsevier B.V. All rights reserved.
机译：煤炭的总热值（GCV）在直接使用和转化为其他燃料形式的煤炭中都非常重要。 GCV的测量通常需要复杂的炸弹量热实验设备和专业知识，而近距离分析的成本更低，更容易且更快速。本文介绍了三种回归模型（即支持向量机（SVM），交替条件期望（ACE）和反向传播神经网络（BPNN））基于最近的分析信息预测煤的GCV的应用。获得了76个等级变化较大的中国煤样的分析数据，并将其用作这些模型的输入。建模结果表明：1）这三种方法一般都能够利用最接近的分析参数来跟踪GCV的变化趋势。 2）在所研究的模型中，SVM在泛化能力方面表现最佳; 3）BPNN在训练阶段和ACE在训练和测试阶段都有可能胜过SVM;然而，其预测精度受模型参数的影响很大，包括隐藏神经元数目，学习率和初始权重。 4）就平均能力而言，ACE在泛化能力方面比BPNN稍好。 （C）2014 Elsevier B.V.保留所有权利。

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