Soft redundant instrument for metering station in gas transportation system

This study focuses on the development of a soft redundant instrument to replace secondary hard instrument for saving cost of installation. The model of the soft redundant instrument implements an artificial neural network (ANN) approach; providing the alternative for secondary measurement. Moreover, the reliability and quality of the metering station play a crucial part in the gas transportation system as it affects the billing integrity between the gas supplier and their customers. In this work, the problem with a primary instrument was analysed to diagnose the faulty condition of measurement in metering station. Accordingly, different ANN algorithms were investigated and compared to select a prediction model that provides the best performance. This suggested prediction technique can be considered as an additional verification system to be compared to the hard instrument that is installed on the pipeline. Hence, in order to determine accuracy in the rate of measurement from the proposed model, it is required that variation in measurement between hard and soft instrument should not exceed 1%. Based on this model, the ANN prediction model is able to reconstruct the unreliable data during faulty instrument.