Design and optimization of the sustainable natural gas liquefaction process plant: A process system engineering approach

Abstract

With the rapid development of the world economy and technologies, the energy demand is increasing. Based on the current regulations energy sectors need low-emission and low-carbon energy solutions. Natural gas (NG) is considered a green future fuel that is usually stored and transported as compressed gas or cryogenic liquid. The liquefied NG has 1/600th of its volume which helps in transportation. Liquefied natural gas (LNG) operations are the most energyintensive techniques. During refrigeration and liquefaction, the units of the LNG consume around ∼40−50% of the entire LNG supply chain energy. To date, the simplest single mixed refrigerant (SMR) cycle-based NG liquefaction plants have exergy efficiencies of around 26.97%. Moreover, the conventional SMR process utilizes a hydrocarbon-based mixed refrigerant (i.e., C1 to C3 and N2) mixture that is further harmful to the ecosystem. In this research work, it was found that the addition of eco-friendly hydro-fluoro-olefin (HFO123yf) with conventionally used refrigerants reduces energy consumption under the same conditions. Further hydraulic turbines were proposed to replace the expansion valves require for the Joule Thompson cooling effect that further increasing the process reversibility and energy efficiency. Hence, this research utilizes the optimization of the HFO based modified mixed refrigerant mixture (HSMR) for optimal operation of the LNG plant. The evaluated result shows that the proposed stochastic biogeography-based optimization (BBO) algorithm successfully reduces the overall energy consumption by 0.232 kW/kg-NG. This energy consumption is about 41.709 % compared to our base case study and it was 36% more efficient compared to the conventional SMR published study. This research further explores the sensitive decision variables that affect power consumption using the sensitivity analysis index method. The sensitivity indices may enable us to operate the plant in any abrupt changing or uncertain conditions when needed quick optimization using only a few key sensitive optimizing variables which are useful for real-time plant optimization. Moreover, the economic analysis of the proposed plant shows a total annualized cost of 6,158,000 USD with a 20% rate of return, per year.