Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant,International Journal of Energy Research

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Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant
International Journal of Energy Research ( IF 4.3 ) Pub Date : 2020-11-25 , DOI: 10.1002/er.6217
Ladan Khoshnevisan ₁ , Farzad Hourfar _{1,

2} , Falah Alhameli ₂ , Ali Elkamel ₂

Affiliation

Processing natural gas, as a widely used source of energy in our life, is imperative to eliminate the impurities in order to make it consumable. So, appropriate modeling of different units in a real gas processing plant (GPP) is an essential research field. Moreover, high‐dimensional data, with probably unnecessary information, gathered from a real application may lead to complicated models. As a result, the original dataset, obtained through a three‐level design of experiments, should be refined to achieve the most effective observations in a lower dimension vector space. On the other hand, the original dataset needs to be normalized to a standard normal distribution in order to tune the effects of all the variables on the system operation. In this study a radial basis function‐neural network (RBF‐NN) is designed to model the total consumed energy in separation, sweetening, and dehydration units and also the water content in the refined gas in a typical GPP, using a reduced dimension dataset achieved by applying principal component analysis (PCA) on the normalized data. The proposed procedure is evaluated through some well‐known and standard criteria such as error relative deviation, root mean square error, the percentage of the average absolute relative deviation %AARD, sum of squared error, standard deviation, and correlation factor (R²). Simulation and analytical results demonstrate that the designed PCA‐RBF‐NN procedure can precisely model the dynamics of energy consumption and the final water content in a typical GPP with the confidence level of 98.6% through six principal components achieved by PCA technique. Furthermore, small values of the error measurements are obtained while using the developed RBF‐NN model.

中文翻译：

结合实验设计，机器学习和主成分分析来预测天然气加工厂的能耗和产品质量

处理天然气作为我们生活中广泛使用的能源，必须消除杂质以使其可消耗。因此，在真实的天然气处理厂（GPP）中对不同单元进行适当的建模是必不可少的研究领域。此外，从实际应用程序中收集的高维数据（可能带有不必要的信息）可能会导致复杂的模型。因此，应完善通过三级实验设计获得的原始数据集，以在较低维向量空间中获得最有效的观察结果。另一方面，原始数据集需要归一化为标准正态分布，以便调整所有变量对系统操作的影响。在这项研究中，设计了径向基函数神经网络（RBF-NN），以降维数据集为模型，对典型GPP中分离，脱硫和脱水单元中的总消耗能量以及精炼气中的水分进行建模通过对归一化数据应用主成分分析（PCA）可以实现。建议的程序通过一些众所周知的标准标准进行评估，例如误差相对偏差，均方根误差，平均绝对相对偏差％AARD的百分比，平方误差总和，标准偏差和相关因子（R ²）。仿真和分析结果表明，所设计的PCA-RBF-NN过程可以通过PCA技术实现的六个主要成分，以98.6％的置信度精确建模典型GPP中的能耗动态和最终水含量。此外，使用发达的RBF-NN模型可获得较小的误差测量值。

更新日期：2020-11-25

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