An artificial neural network for nonlinear estimation of the turbine flow-meter coefficient

Turbine flow meters find various applications in the process industries, such as batch control, measuring fuel oil and gas consumption, controlling blending processes, etc. The turbine meter is a rotor driven by the fluid being metered, at a speed proportional to the flow rate.The actual behavior of a turbine flow meter is a complex function of many variables; among these are the temperature, pressure, and viscosity of the fluid; the lubricating qualities of the fluid; bearing wear; and environmental factors. The turbine meter coefficient is referred to as the ‘K factor’, and is defined as the number of pulses per unit volume. At present, there is no single mathematical equation to predict the actual K factor. More accurate estimations and trending of the K factor will not only facilitate preventive maintenance, replacement analysis, etc., but will also ensure that material flow accounting is accurate.This research explores the use of neural-network models to aid in the estimation of the actual K factor that reflects the effect of the actual operating conditions of the turbine meter. This research analyzed data from three different turbine flow meters measuring the rate of pumping oil from the North Sea, for a company that operates off-shore oil platforms. The use of neural networks presents a new approach to the capturing of the underlying nonlinear relationships among the various input variables and the K factor. The results from this study report significant percentage reductions in mean absolute errors for the neural-network predictions over the company’s present estimation practices for the turbine flow-meter coefficient.