Application of inverse methods based algorithms to Liquefied Natural Gas (LNG) storage management

Liquefied Natural Gas is one of the major fossil fuels used throughout the world and is to gain even more of the market share being the cleanest burning fossil fuel and also due to its availability. In addition, now it becomes crucial to improve the existing LNG storage models to support safe and economic managing of LNG storage sites. In this article two inverse methods are compared. First one, being the previously developed optimization method for the real-time inverse problem, is analyzed and its main characteristics are identified. Next, an alternative method is being proposed, based on normal equations applied to nonlinear parameter estimation, dealing with the main limitations of the previous approach. Both methods significantly improve the accuracy of LNG storage models and credibility of predictions, by making use of the available measurements from the tank's gauges and processing them with the developed algorithms.

► We compare performance of two inverse methods applied to LNG storage bulk modelling.
► First one is the optimization approach, newer method is based on normal equations.
► Both algorithms improve credibility and accuracy of LNG rollover onset predictions.
► The more recent approach offers considerably better time performance.