MLP artificial neural network model for predicting natural gas composition variability

Abstract

This paper proposes an artificial neural network model, MLP 17-37-5, to predict the share of five natural gas components (methane, ethane, propane, nitrogen, and carbon dioxide) in the natural gas mixture in a pipeline network, depending on selected calendar and weather factors. Natural gas composition variability in the pipeline network results from supplying it with gas of varying composition and from different suppliers. Using statistical analysis of 35,064 actual measurement data sets, factors (model input data) significantly affecting gas composition variability were selected. Models differing in structure (the MLP 17-37-5 model or five MLP 17-37-1 models) and the number of neurons in the hidden layer (from 20 to 230 neurons) were trained using sets ranging from 8,760 to 35,064 actual data points obtained using the chromatographic method. The quality of the models was assessed based on the correlation coefficient, while the quality of the forecasts was assessed based on the nRMSE error of the forecasts obtained for a new data set of 8,760 data points. The MLP 17-37-5 model was shown to predict natural gas composition with an average error of nRMSE = 0.321. The article proposes a ready-made tool, which has no equivalent in the literature, allowing for a significant reduction in the number of chromatographic tests performed to determine the composition of natural gas. This is a completely new approach to studying changes in the composition of natural gas over time, which in the proposed forecasting model depend on selected factors (not analyzed in chromatographic studies).