Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass
Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass
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DOI：
10.1016/j.compchemeng.2017.02.008
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发表时间：
2017-06-09
影响因子：
4.3
通讯作者：
Castro, Eulogio
中科院分区：
文献类型：
--
作者：
Charte, Francisco;Romero, Inmaculada;Castro, Eulogio
生物燃料的生产是一个需要调整多个参数的过程。进行改变这些参数并分析输出的实验是必要的，但这些测试的成本限制了它们的数量。因此，设计能够预测不同输入下的不同输出的模型，减少需要完成的实际实验数量是很重要的。响应面方法（RSM）是该任务最常用的方法之一，但机器学习算法代表了一种有趣的替代方法。在本研究中，从相同的问题数据建立的多个模型的预测性能进行了比较：从木质纤维素材料生产生物乙醇。分析了四种机器学习算法，包括两个神经网络、一个支持向量机和一个模糊系统，以及RSM方法。结果表明，作者设计的Reg-(CORBFN)- r -2方法优于其他所有替代方法。(C) 2017 Elsevier Ltd.版权所有。
The production of biofuels is a process that requires the adjustment of multiple parameters. Performing experiments in which these parameters are changed and the outputs are analyzed is imperative, but the cost of these tests limits their number. For this reason, it is important to design models that can predict the different outputs with changing inputs, reducing the number of actual experiments to be completed. Response Surface Methodology (RSM) is one of the most common methods for this task, but machine learning algorithms represent an interesting alternative. In the present study the predictive performance of multiple models built from the same problem data are compared: the production of bioethanol from lignocellulosic materials. Four machine learning algorithms, including two neural networks, a support vector machine and a fuzzy system, together with the RSM method, are analyzed. Results show that Reg-(CORBFN)-R-2, the method designed by the authors, improves the results of all other alternatives. (C) 2017 Elsevier Ltd. All rights reserved.