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首页> 外文期刊>Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers >A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of response surface methodology (RSM) and artificial neural network (ANN) based on design of experiment (DOE)
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A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of response surface methodology (RSM) and artificial neural network (ANN) based on design of experiment (DOE)

机译：混合建模和统计方法罗恩的优化:比较研究联合应用响应面方法(RSM)和人工神经网络(安)基于实验设计(DOE)

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摘要

The main purpose of catalytic reforming unit is to upgrade low-octane naphtha to high-octane gasoline. In this work, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), to determine the research octane number (RON) of reformates produced from the catalytic naphtha reforming unit were investigated. The article presents a comparative study and combined application between response surface methodology (RSM) and artificial neural networks (ANN) based on design of experiment (DOE) strategy in the modeling and prediction of the research octane number (RON). In this study, DOE-CCRD full factorial design was incorporated into the ANN methodology, so the need of a large quantity of training data was avoided. ANN methodology showed a very obvious advantage over RSM for both data fitting and estimation capabilities. Based on the results of analysis of variance (ANOVA), a multiple determination coefficient of 0.8 and 0.99 were obtained for both RSM and ANN respectively. It has been found that by employing RSM approach coupled with ANN model based on DOE strategy, the visualization of the experimental points in three dimensional spaces can disclose qualitatively and quantitatively the activity relationships. This approach of combination of RSM-ANN-DOE has revealed its ability to solve a quadratic polynomial model involving solving, optimization, complexity and difficult relationships especially nonlinear ones may be investigated without complicated equations involved. The study revealed that, the maximum RON of 88 was obtained at the optimum conditions offered by RSM. Furthermore, at the optimal conditions of (T=521 degrees C, P = 37.6 bar, LHSV= 2.02 h(-1)), the maximum RON of 98 was obtained for the ANN model. However, the models were implemented for the construction of 3D response surface plots from the ANN and RSM models in order to show the most effective variables as well as the effects of their interaction on the research octane number. (C) 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
机译：催化重整装置的主要目的是高辛烷值的升级low-octane石脑油汽油。的响应面方法(RSM)和人工神经网络(ANN),来确定重新格式化的研究法辛烷值(RON)从石脑油催化重整生产单位进行调查。比较研究和组合应用程序响应面方法(RSM)之间基于人工神经网络(ANN)的设计的建模和实验(DOE)策略预测的研究法辛烷值(RON)。在这项研究中,DOE-CCRD全因子设计纳入ANN方法,所以需要大量的训练数据避免的。在RSM数据拟合和优势评估的能力。多个方差分析(方差分析)决定系数为0.8和0.99分别获得RSM和安。已经发现,采用RSM方法加上ANN模型基于能源部策略可视化实验分三种维空间可以披露定性定量的活动的关系。RSM-ANN-DOE组合的方法揭示了其解决二次的能力涉及解决多项式模型,优化,特别是复杂性和困难的关系非线性的可能没有调查复杂的方程。显示,88年的最大罗恩在RSM提供的最优条件。此外,在最优的条件(T = 521摄氏度,P = 37.6酒吧,LHSV h = 2.02 (1))最大98年罗恩ANN模型获得的。然而,模型的实现建筑的三维响应面图安和RSM模型为了显示最多有效的变量的影响他们的交互研究法辛烷值。(C) 2016化学工程师学会。爱思唯尔出版的帐面价值保留所有权利。

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