The efficiency of using sequential quadratic programming (SQP) for the optimization of a PRICO process for the production of liquefied natural gas (LNG) is demonstrated. Most of the returned objective values have been better, and the execution times much lower, than in most previously published work on similar optimization cases. The optimization runs discussed in this paper require around 5 minutes of execution time.

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This article describes a dynamic, control relevant, mechanistic model of the TEALARC liquified natural gas process. The model is to be used for both steady-state and dynamic controllability analysis. The model therefore needs to be computationally light, but still include enough complexity such as to study the impact of capacity constraints on the control structure. Structured assumptions have been used to obtain simplified representations of gas/liquid flows and thermodynamic properties. The steady-state operating points of the dynamic model have been adapted to a given steady-state process design model. The paper demonstrates that the model is well suited for operability analysis. Steady-state and dynamic characteristics are illustrated.

Solution of phase equilibria with flash calculations is central in many processes. During the integrating of the conservation equations in these systems, flash calculations are traditionally solved in inner loops at each integration step. Some of these systems can be solved more efficiently using modern DAE solvers, where the differential equations and the algebraic equations describing the phase equilibrium are solved simultaneously. In this paper we present a framework, called the Thermodynamic Differential Algebraic Equation (TDAE) method, which handles most two-phase flash variants. The phase boundary is tracked, enabling a robust solution also with phase changes. The time consumption of the TDAE method has been compared to the traditional approach in several examples implemented in both Fortran and Matlab. In some cases, the TDAE method is more than 800 times faster, and in other cases the traditional methodology should be used. We will give insight into how and when DAE solvers can be used to speed up phase equilibrium calculations.