Modélisation et optimisation de cycles cryogéniques de production de puissance dans un contexte de regazéification de gaz naturel

Authors: Truchon, Patricia
Advisor: Mathieu-Potvin, François
Abstract: Energy losses occurring while liquefied natural gas (LNG) is regasified are well known in the literature and many methods to recover this energy can be used. However, there is no general guidelines that allow to identify the best designs for power generation systems using thermodynamic cycles in the context of regasification of natural gas. The main goal of this project is to develop graphical tools that allow to determine the maximum performance of three systems of power production in the context of natural gas regasification. First, two power generation systems are studied, namely the direct expansion system (DE) and the double expansion system (D2E). Numerical optimization models are developed to maximize the specific work of the systems for a wide range of operating conditions. The resulting figures provide the values of the maximal specific work and of the corresponding optimized design variables (inlet and outlet turbine pressures). Finally, robustness analyses are performed to measure the capacity of the DE and D2E systems to provide good performances when they are used in non-optimal conditions. Second, a Rankine cycle using regasifying LNG as a heat sink is studied. Numerical optimizations are performed to get the maximal specific work of the system and the results are reported in graphical form. These new figures provide the value of the maximal specific work for a wide range of operating conditions, and also the values of the four corresponding optimal design variables (inlet and outlet turbine pressures, mass flow rate ratio, and working fluid). Furthermore, a sub-optimal analysis is performed for each fluid investigated and the resulting figures allow to identify the fluids that can provide a performance close to the performance provided by the best fluids.
Document Type: Mémoire de maîtrise
Issue Date: 2017
Open Access Date: 30 January 2019
Grantor: Université Laval
Collection:Thèses et mémoires

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