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http://dx.doi.org/10.9713/kcer.2019.57.1.51

The Research of Layout Optimization for LNG Liquefaction Plant to Save the Capital Expenditures  

Yang, Jin Seok (Department of Safety Engineering, Pukyong National University)
Lee, Chang Jun (Department of Safety Engineering, Pukyong National University)
Publication Information
Korean Chemical Engineering Research / v.57, no.1, 2019 , pp. 51-57 More about this Journal
Abstract
A plant layout problem has a large impact on the overall construction cost of a plant. When determining a plant layout, various constraints associating with safety, environment, sufficient maintenance area, passages for workers, etc have to be considered together. In general plant layout problems, the main goal is to minimize the length of piping connecting equipments as satisfying various constraints. Since the process may suffer from the heat and friction loss, the piping length between equipments should be shorter. This problem can be represented by the mathematical formulation and the optimal solutions can be investigated by an optimization solver. General researches have overlooked many constraints such as maintenance spaces and safety distances between equipments. And, previous researches have tested benchmark processes. What the lack of general researches is that there is no realistic comparison. In this study, the plant layout of a real industrial C3MR (Propane precooling Mixed Refrigerant) process is studied. A MILP (Mixed Integer Linear Programming) including various constraints is developed. To avoid the violation of constraints, penalty functions are introduced. However, conventional optimization solvers handling the derivatives of an objective functions can not solve this problem due to the complexities of equations. Therefore, the PSO (Particle Swarm Optimization), which investigate an optimal solutions without differential equations, is selected to solve this problem. The results show that a proposed method contributes to saving the capital expenditures.
Keywords
Plant layout optimization; Particle swarm optimization; C3MR Liquefaction process;
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