(238c) Dynamic Data Reconciliation Using Conventional Dynamic Simulation Software for LNG Terminal

While the risk of nuclear power generation became real, the Liquefied Natural Gas(LNG) demand is growing higher because of relative safety and eco-friendliness. In order to supply LNG to consumers and power plant consistently, LNG transport terminal is necessary. LNG Terminal has several storage tanks those are huge enough to prevent LNG supply risk and long and complicate pipeline to send LNG from LNG carrier to storage tanks or from storage tanks to demands. In management of this facility, safety is the most important thing in many other issues such as economically optimal operation or environmental harm because LNG is one of the highly flammable fossil fuels and there were numbers of catastrophic accident which kill hundreds of people in history. For the safe operation, many and various type of operation data of LNG must be monitored and in general LNG terminals plenty of temperature, pressure, flow rate sensors are installed for this reason and they are printing the data out in real-time. However using the real-time data without any pre-process has risk of error included in raw data. Every sensor is exposed of noise and failure that can cause misunderstanding about terminal’s status. In addition about this terminal case, the problem is more serious because it is intimately related in safety. For an example, if temperature of LNG is increased but it is not measured because of sensor failure, then without any notice BOG is formed and finally it can make serious problem such as BOG leakage due to pressure rise. In order to prevent the sensor error, data reconciliation is widely used in many chemical processes. From 1970s there have been many researches about the technique and recently dynamic data reconciliation is developed for rectification of time-varying variables. Though its technological progress, dynamic data reconciliation remains unable to be simultaneous by the real-time data because of its computation time.

In this research, a new data reconciliation algorithm in real-time with conventional dynamic process simulation program is proposed to solve these data problems. A well-developed dynamic simulation model built with HYSYS dynamics is utilized as first-principle equations and for minimizing sum of squared differences MISO controller acts as an optimizer. Using the controller prevents to fall into calculation loop and is able to guess the reconciled value in real-time which is difficult for other researches. These are shown through a case study targets LNG terminal unloading pipeline during operation mode changes. As a result of the case study, the algorithm makes quite satisfying reconciled data close enough to other dynamic data reconciliation methods and requires much less work than others.