(399f) Comprehensive Dynamic Simulation Model of a Cryogenic Storage Tank

Many industrially useful gases such as hydrogen, argon, helium, oxygen, nitrogen, methane, ethane, ethylene, propane, propylene, butane, and ammonia are gases at ambient conditions. Hydrogen is becoming more important, as the world moves towards clean and renewable energy fuels. The demand for natural gas (NG) as an energy source with the lowest carbon, sulfur, and particulate emissions is growing at twice the rate of global energy demand. Ammonia is gaining attention as a potential hydrogen carrier. These gases are normally stored and transported in their cryogenic liquid states in huge cylindrical or spherical, heavily insulated, and auto-refrigerated tanks. In such tanks, evaporation of saturated liquid, or condensation of boiled off vapors (called Boil-Off Gas or BOG) are inevitable. While BOG generation auto-refrigerates the liquid, BOG must be removed periodically to keep the tank pressure below a safe limit. BOG also represents a loss of valued product, unless reliquefied or reused. However, BOG reliquefaction is energy intensive. Therefore, it is important to understand, quantify, and minimize BOG generation in an industrial cryogenic storage tank, which is a 2-phase system with temporal liquid level, complex spatiotemporal physical phenomena (heat transfer, evaporation, and condensation), and spatiotemporal state variables (temperature, pressure, and composition).

Several studies in the past have attempted to simulate cryogenic tanks using either simplified lumped parameter models or complex full-fledged CFD (Computational Fluid Dynamics) models. While the latter are computationally too expensive for practical applications (Saleem et al., 2018), most of the former lack the rigor. Most models (Migliore et al, 2015) assume vapor and liquid phases to be in Vapor-Liquid Equilibrium (VLE), which is not the reality (Effendy et al., 2017; Migliore et al., 2017) in an industrial tank. Process simulators such as Aspen Hysys® and Unisim® have inbuilt tank modules for dynamic simulation, but with serious limitations. These lumped parameter models cannot predict spatial temperature and pressure gradients, do not allow independent non-equilibrium initial hold-up conditions, have no provisions for some heat leaks, and have non-intuitive ways to deal with mixing in the tank. Therefore, it is highly desirable to develop a simpler, faster, and more rigorous dynamic simulation model for this heart of most energy systems.

Modeling the spatiotemporal behavior of a cryogenic storage tank poses two key challenges. One is the handling of the moving vapor-liquid interface, and the other is the accurate prediction of the BOG generation rate. While many existing models consider only the evaporation, condensation cannot be ignored. In a multicomponent system, some components may evaporate, and others may condense at the same time. In this work, we will present a practically useful, comprehensive, and spatiotemporal mathematical model for simulating the dynamic behavior of a generic cylindrical tank holding a multi-component cryogenic liquid. The tank can be completely or partially insulated and allows multiple feed and product streams. The model uses a novel, flexible, and moving spatial grid instead of the fixed grid used in most models and CFD. It does not assume well-mixed liquid and vapor phases and allows non-equilibrium behavior. It also offers a practically meaningful and intuitive treatment for mixing feeds with the tank contents and withdrawing products. It models evaporation/condensation at the vapor-liquid interface using appropriate driving forces based on sound interphase mass transfer principles. The mass, energy, and component balances describing the spatiotemporal variations of pressure, temperature, and composition in the vapor and liquid holdups result in a complex differential algebraic (DAE) equation system. The system is further reduced using clever mathematical manipulations to improve solution speed. The solution requires repeated iterative and nonlinear vapor-liquid equilibrium or flash calculations that need accurate physical properties. For these, we have designed a series of ANN (Artificial Neural Network) models with help from Aspen Hysys to reduce computational burden without sacrificing accuracy in physical property estimation. Our simulation model can predict the dynamic behavior of any cryogenic storage tank in any mode of operation (loading, unloading, supplying, etc.) It estimates BOG generation accurately by capturing the effect of competitive multi-component evaporation and condensation occurring at the interface. The developed model is thus computationally cheaper and comprehensive in capturing all major tank dynamics. It provides a sound basis for the design, operation, and control of these tanks.

References

Saleem, et al., A CFD simulation study of boiling mechanism and BOG generation in a full-scale LNG storage

tank, Comput. Chem. Eng. 115 (2018) 112–120.

Migliore, C. Tubilleja, V. Vesovic, Weathering prediction model for stored liquefied natural gas (LNG), J. Nat.

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Migliore, A. Salehi, V. Vesovic, A non-equilibrium approach to modelling the weathering of stored Liquefied

Natural Gas (LNG), Energy 124 (2017) 684–692.

Effendy, et al., Dynamic modelling and optimization of an LNG storage tank in a regasification terminal with

semi-analytical solutions for N2-free LNG, Comput. Chem. Eng. 99 (2017) 40–50.