Application of Coupled Simulation Optimization Methodology to Study Dual Nitrogen Expander Liquefaction Response to Feed Gas Variations from an Optimized Design

Interest in dual nitrogen expander liquefaction technology for floating liquefied natural gas (FLNG) applications is driven by the following factors: inflammable refrigerantsimplicitylow weightno refrigerant sloshing or maldistribution due to motionquick start-upeasy adjustment for changing feed conditions The downside is that dual nitrogen expander technology offers significantly lower liquefaction efficiency than competing FLNG technologies. The proper selection and sizing of the upstream gas treating and liquefaction system is critical during Front End Engineering Design (FEED) to ensure that the system footprint, weight and center-of-gravity is appropriately estimated as this effects the sizing, design and performance of the floating hull. This paper will demonstrate how the process design can be optimized over a range of feed compositions or conditions if some flexibility is built into the liquefaction heat exchange during design. This preserves flexibility as a key advantage of the technology. The intent is to reduce process inefficiencies and promote competitiveness with other technologies. Note that there are many different nitrogen expander technology configurations available in the market. The configuration used here is generic and used to demonstrate the optimization concept. With 10 independent variables and coupling between the variables, this optimization is difficult to perform using simple manual methods. Therefore we will employ a coupled simulation-optimization method. This paper also provides insight to the application of coupled simulation-optimization to problems, as illustrated by the specific application to a dual titrogen expander technology. Although this method is applicable to the initial design of liquefaction processes, the focus here is on off-design optimization of the facility later in the design cycle and in operation. This optimization methodology is shown to provide benefits beyond the initial process design, extending into the operation of the facility. The methodology does not rely upon a specific tool set and there are non-academic tools that support this approach.