Multi-objective optimization of Organic Rankine Cycle (ORC) design considering exergy efficiency and inherent safety for LNG cold energy utilization. (March 2019) - Lee, Younggeun; Kim, Jeongnam; Ahmed, Usama; Kim, Changsoo; Lee, Youn-Woo
Abstract: Natural gas (NG) is an environment-friendly energy source with low CO2 and SOx emission. NG is usually liquefied during processing for transportation and storage, and then vaporized before consumption for multiple purposes. Usually, cold energy from the vaporization process is not utilized and hence wasted. One of the means of utilizing the cold energy is Organic Rankine Cycle (ORC), through which electricity can be generated from any grade of heat sources. Accordingly, ORC has been extensively investigated with the aim of increasing efficiency. However, although the ORC process has high potential risk of fatality, the safety aspect has not been previously studied. In this study, ORC design considering the thermodynamic and safety aspects is explored with a multi-objective optimization methodology. Exergy efficiency is used for the thermodynamic aspect, and risk distance is used for the safety aspect, with inherent safety techniques for designing safer processes in the conceptual design. Considering the working fluid as the main factor for optimal ORC design, six working fluids in three categories (pure component, binary components, ternary components) are investigated. Pareto optimal solutions are obtained from the multi-objective optimization method with two objectives based on working fluids for ORC design. Optimization variables include working condition (pressure), working fluid conditions (components). Moreover, the final optimal solution is selected using decision techniques, such as LINMAP and TOPSIS. Highlights: ORC design for utilizing cold energy from LNG is investigated, considering the thermodynamic and safety aspects. Exergy efficiency and risk distance are used as parameters of the thermodynamic and safety aspects. Pure, binary, and ternary component working fluids are explored for determining the optimal ORC design. Pareto optimal solutions are obtained using the multi-objective optimization method with genetic algorithm (GA).