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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
Author(s) -
Khoshnevisan Ladan,
Hourfar Farzad,
Alhameli Falah,
Elkamel Ali
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6217
Subject(s) - principal component analysis , standard deviation , mean squared error , artificial neural network , approximation error , radial basis function , dimensionality reduction , energy consumption , computer science , statistics , artificial intelligence , mathematics , engineering , electrical engineering
Summary 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 ( R 2 ). 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.