(573y) Optimal Compressor Operational Policies for LNG Storage Tanks Under Unloading Mode Operation: A Numerical Approach

The increasing energy demand forces our dependency on fossil fuels, leading to excess CO₂ emissions. These emissions are major greenhouse gas contributors, responsible for climate change, ozone layer depletion, and increasing earth's surface temperature¹. Therefore, the government is forcing industries to shift from fossil fuels to renewable energy resources, and natural gas (NG) acts as a transition fuel². Over larger distances, NG is transported under cryogenic conditions for ease of handling, safer operation and higher energy density. NG under cryogenic conditions is called 'Liquefied Natural gas' (LNG). Despite heavy insulation, a continuous heat ingress in the tank results LNG evaporation, known as 'boil-off gas' (BOG). BOG generation pressurizes the tank and needs to be managed appropriately for the plant's safety. La Spezia (Italy, 1971) and Partington (UK, 1993) are the two major incidents that occurred due to over-pressurization of LNG tank³. In plants, compressors regulate tank pressure and are operated heuristically at varying capacities (0%, 50%, 75%, and 100%) to maintain safe tank pressure and optimize the compressor energy requirement. During LNG unloading, compressors are operated continuously at 100% capacity due to the lack of well-known tank dynamics. Thus, it is necessary to understand the dynamics of the storage tank under unloading operation, and optimize compressor energy.

Optimizing the compressor's energy consumption has been studied by many researchers over the years. Shin et al.⁴ observed that a compressor operated at higher capacity is safe but increases the terminal's operating cost. They developed a dynamic model to estimate tank pressure and provided an algorithm to regulate pressure through the compressor under minimum capacity. Further, Jang et al.⁵ improved the model by providing a new algorithm to distribute the compressor load and optimizing the energy consumption. They developed a dynamic model to estimate tank pressure using the Peng-Robinson equation of state for better accuracy. Ye et al.⁶ considered the compressor and low-pressure (LP) pump to optimize energy requirements. They considered pump running or on standby mode, number of compressors and compressor capacity in the analysis. The optimization decreases the number of compressors and LP pumps operation. Li and Li⁷ estimated the operating condition that generated minimum BOG by varying influencing factors and managed it using a single compressor operation. Effendy et al.⁸ provide a dynamic model to estimate BOG generation by considering the storage tank and recirculation line during holding mode operation. As mentioned earlier, all the studies focus on BOG generation or compressor operation during holding mode operation, but none focus on unloading operation. Our work seeks to fill these gaps by considering the Effendy et al.⁸ model as a base for estimating BOG generation and developing a new unloading operation model. The model considers the additional feature of unloading the nozzle and compressor to manage generated BOG under a safety range. The developed model helps to formulate new compressor operation policies to optimize energy consumption.

This paper develops a dynamic model of an industrial LNG storage tank. The tank has a diameter of 80m and a height of 40m, and it has a nozzle for unloading cargo. The tank can fill a maximum of 30m of LNG and drain a minimum of 2m from the tank. Initially, the tank contains some LNG inventory and is unloaded to the maximum level. Unloading at high pressure generates a large amount of BOG due to flashing and increases the tank pressure abruptly. The dynamics of the unloading operation are not well known, so the compressor is operated at 100% capacity to regulate safe tank pressure. In this work, we estimate tank pressure using Peng-Robinson equation of state and compare it with plant data to calculate the critical parameters for better model accuracy. Further, tank pressure is regulated by a compressor operated at variable capacities and performs several scenarios by varying initial LNG levels, namely 20% (6m), 40% (12m), 60% (18m), 80% (24m), and 100% (30m). Our model recommends new policies for safe operation and optimal compressor energy requirements in the receiving terminal.

References:

(1) Climate Change: Atmospheric Carbon Dioxide | NOAA Climate.gov. http://www.climate.gov/news-features/understanding-climate/climate-chang... (accessed 2024-03-27).

(2) EI Key Findings global gas 2021 DT. http://ceros.mckinsey.com/energy-insights-global-gas-key-findings-2021 (accessed 2023-02-19).

(3) Rollover in LNG Storage Tanks 2012-2015 -. https://giignl.org/document/rollover-in-lng-storage-tanks/ (accessed 2024-03-26).

(4) Shin, M. W.; Shin, D.; Choi, S. H.; Yoon, E. S. Optimal Operation of the Boil-off Gas Compression Process Using a Boil-off Rate Model for LNG Storage Tanks. Korean J. Chem. Eng. 2008, 25 (1), 7–12. https://doi.org/10.1007/s11814-008-0002-9.

(5) Jang, N.; Shin, M. W.; Choi, S. H.; Yoon, E. S. Dynamic Simulation and Optimization of the Operation of Boil-off Gas Compressors in a Liquefied Natural Gas Gasification Plant. Korean J. Chem. Eng. 2011, 28 (5), 1166–1171. https://doi.org/10.1007/s11814-010-0487-x.

(6) Ye, Z.; Mo, X.; Zhao, L. MINLP Model for Operational Optimization of LNG Terminals. Processes 2021, 9 (4). https://doi.org/10.3390/pr9040599.

(7) Li, Y.; Li, Y. Dynamic Optimization of the Boil-Off Gas (BOG) Fluctuations at an LNG Receiving Terminal. Journal of Natural Gas Science and Engineering 2016, 30, 322–330. https://doi.org/10.1016/j.jngse.2016.02.041.

(8) Effendy, S.; Khan, M. S.; Farooq, S.; Karimi, I. A. Dynamic Modelling and Optimization of an LNG Storage Tank in a Regasification Terminal with Semi-Analytical Solutions for N2-Free LNG. Computers & Chemical Engineering 2017, 99, 40–50. https://doi.org/10.1016/j.compchemeng.2017.01.012.