(340p) Safe Operation of Floating LNG Tank Via Model Predictive Control

With increasing environmental regulations, the importance of eco-friendly energy sources is increasing rapidly, especially in the ocean industry such as ship transportation. Following the newly modified regulation of the International Maritime Organization (IMO), the maximum allowable sulfur emission was recently reduced from 3.5% to 0.5% since January 2020 [1]. One of the eco-friendly energy sources is liquefied natural gas (LNG), and it is highly attractive because of its extremely low sulfur and nitrogen components, which satisfy the IMO regulations. But its production rate is exceeding the current storage capacities and consumption rates, and hence, is proving to be a burden on land-based storage systems. Therefore, traders are looking at other alternatives such as building safe storage systems on the ocean. With this viewpoint, LNG has been stored on the ocean using huge ships containing storage tanks below its boiling temperature (-162℃). Because of such very low temperatures, natural heat conduction into the storage tank causes the evaporation of LNG resulting in the formation of Boil-off gas (BOG). This evaporation of LNG results in the pressure build-up inside the tank along with an increase in vapor temperature, which can lead to critical accidents. Therefore, it is crucial to control the tank pressure and this usually has been done using a BOG compressor [2].

Most of the research studies in this direction have focused on estimation of BOG due to LNG weathering and prediction of rollover phenomena under constant pressure; in these studies, they assumed that the BOG compressor is always able to stabilize the pressure [3-5]. Additionally, studies have been conducted to calculate the optimal compressor schedule in order to regulate the pressure [6-7]. However, these studies do not take into account the occurrence of unanticipated events such as a fire break-out around the tank. In this scenario, since the floating LNG (FLNG) tank pressure is controlled using the BOG compressor, it cannot be stabilized within a short period of time. As a result, the pressure inside the tank fluctuates rapidly, which has a direct effect on the vapor outlet flowrate, and this leads to the entire system becoming unstable [8]. Also, the compressor performance is affected by the amount of its usage as well which could result in its poor performance [7,9]. Therefore, it is necessary to regulate the pressure using an advanced and robust controller that is capable of steadying the internal pressure even in critical situations.

Motivated by this challenge, an integrated modeling and model predictive control (MPC) framework was developed for regulation of the pressure within a FLNG tank. First, a high-fidelity model of the FLNG tank system was developed using the theoretical film model [10]. Then, to circumvent the large computational requirement of the high-fidelity model, a reduced-order model (ROM) was developed using the N4SID algorithm was utilized in the proposed MPC framework. Compared with conventional control approaches such as PI control, the tank pressure can be regulated without large oscillations. Additionally, the performance of the MPC was further validated by successfully regulating the pressure in a fire occurrence scenario.

Literature cited:

[1] IHS Market, “IMO 2020: What Every Shipper Needs To Know,” 2019.

[2] Y. M. Kurle, S. Wang, and Q. Xu, “Dynamic simulation of LNG loading, BOG generation, and BOG recovery at LNG exporting terminals,” Comput. Chem. Eng., vol. 97, pp. 47–58, 2017.

[3] C. Migliore, C. Tubilleja, and V. Vesovic, “Weathering prediction model for stored liquefied natural gas (LNG),” J. Nat. Gas Sci. Eng., vol. 26, pp. 570–580, 2015.

[4] Y. Lin, C. Ye, Y. yun Yu, and S. wei Bi, “An approach to estimating the boil-off rate of LNG in type C independent tank for floating storage and regasification unit under different filling ratio,” Appl. Therm. Eng., vol. 135, pp. 463–471, 2018.

[5] K. B. Deshpande, W. B. Zimmerman, M. T. Tennant, M. B. Webster, and M. W. Lukaszewski, “Optimization methods for the real-time inverse problem posed by modelling of liquefied natural gas storage,” Chem. Eng. J., vol. 170, pp. 44–52, 2011.

[6] H. Kim, M. W. Shin, and E. S. Yoon, “Optimization of Operating Procedure of LNG Storage Facilities Using Rigorous BOR Model,” vol. 41, IFAC, 2008.

[7] M. W. Shin, D. Shin, S. H. Choi, E. S. Yoon, and C. H. Han, “Optimization of the operation of boil-off gas compressors at a liquified natural gas gasification plant,” Ind. Eng. Chem. Res., vol. 46, pp. 6540–6545, 2007.

[8] M. W. Shin, D. G. Shin, S. H. Choi, and E. S. Yoon, “Optimal operation of the boil-off gas compression process using a boil-off rate model for LNG storage tanks,” Korean J. Chem. Eng., vol. 25, no. 1, pp. 7–12, 2008.

[9] Z. Lv, Y.L. Chen, Q. Zhang, and J.D. Li, “The Design of Pressure Regulating System for Large LNG Storage Tank Dome Gas Lift,” Appl. Mech., vol. 511–512, pp. 1081–1084, 2014.

[10] H. Janet, C. W. Shipman, and J. W. Meader, “A predictive model for rollover in stratified LNG tanks,” Am. Inst. Chem. Eng. J., vol. 29, no. 2, pp. 199–207, 1983.