(362f) Rigorous Thermodynamic Analysis of a Baseload LNG Chain with Different Boil-Off Gas Minimization Strategies

Global energy demand is increasing and, to date, fossil fuels, such as coal, natural gas (NG), and oil, are the main energy sources. However, the combustion of this carbon based fuel is contributing to today’s environmental issues. For instance, fossil-fuel combustion in the power generation sector is responsible for nearly 22% of the worldwide greenhouse gas emissions (such as CO₂), the main contributors to global warming [1]. Having said so, for a given amount of energy, NG produces the least amount of CO₂, making it the most vital carbon based energy source. This may also explain the continuous increase in NG demand. For example, in the next decade or so, NG demand is forecasted to increase by 37% [2]. The most feasible way for the transportation of large quantities of NG over long distances is via liquefaction. This, however, requires a complex and highly interactive chain consisting of NG extraction, purification and liquefaction, storage, loading, shipment, and end user unloading, storage, and regasification. While the chain provides an environmentally friendly fuel, it releases significant amounts of CO₂ because of the energy losses embedded in the various processing steps. Our simulations show that the combustion of 3 million tonnes per annual (MTA) of LNG, a typical production rate of a baseload LNG plant, produces approximately 9.9 MTA of CO₂. The LNG chain responsible to supply this LNG will need to combust around 846 MW of Lower Heating Value fuel resulting in 0.97 MTA of CO₂ generation. Accordingly, there is a continuous need to improve and optimize existing LNG chains. Full optimization of such complex system is, however, not a straight forward technique requiring different expertise ranging from engineering/process oriented insights and skills to advanced mathematical and thermodynamics techniques. Thus, it is deemed essential to identify the chain sections that must be prioritized for optimization. This requires quantifying losses and efficiencies and one of the effective tools is exergy analysis. Identifying the areas of high exergy losses within an LNG chain emphasizes the areas where optimization is necessary.

The primary focus of the study is to identify exergy losses across a baseload LNG chain supplying more than 3 MTA of LNG. In this research, a rigorous exergy analysis was performed on an entire actual LNG chain that was simulated using ProMax® and Aspen Plus® simulation software. Exergy loss across each component of the entire chain was determined taking into consideration both physical and chemical contributions. The analysis was not limited to the main processing units, as it was extended to quantify exergy loss across the chain utility sections (i.e. power and steam generators, cooling water loop, etc.). Results revealed that the main contributor to the total exergy loss is the LNG plant utility section, accounting for 69% of the total exergy loss. Within the main LNG process, significant amounts of losses were found to occur in the liquefaction, sweetening and sulfur recovery units; corresponding for 33%, 30% and 30.4% of the plant total exergy loss, respectively, excluding the plant utility. Components responsible for the highest exergy consumption were also highlighted, with the main consumers being the compressors and their drivers, LNG storage, columns (absorbers, distillations) and heat exchangers. The contribution to the total exergy loss provided valuable insights on locations where improvements are needed to translate into environmental and energy benefits. Different optimization options were identified, rapidly evaluated, and found to be attractive for rigorous synthesis and optimization. One optimization opportunity was concerned with the LNG loss during storage, also known as boil-off gas (BOG) generation.

BOG generation takes place because of high pressure LNG flashing to storage condition, heat leaks, and other factors. The evaporation of the stored LNG means loosing portion of the compression energy provided for NG liquefaction. Nearly 12 MW of compression energy is needed to liquefy one MTA of LNG. Our simulation shows that a 1 MW loss in the liquefaction results in 3.3 MW loss in the compressors gas turbine drivers. Thus, LNG loss through BOG generation should be minimized, or effectively utilized in the chain. Different BOG minimization and utilization strategies were investigated using a combination of thermodynamic, synthesis, and simulation techniques taking into consideration loading and holding (i.e. no ship loading) operation modes. Valuable insights were obtained and utilized to recommend new practices for BOG minimization and utilization.