(382g) Strategies for Process and Size Selection of Natural Gas Liquefaction Processes: Specific Profit Portfolio Approach By Cost Based Optimization | AIChE

(382g) Strategies for Process and Size Selection of Natural Gas Liquefaction Processes: Specific Profit Portfolio Approach By Cost Based Optimization

Authors 

Lee, I. - Presenter, Yonsei University
Moon, I., Yonsei University
The natural gas liquefaction process is highly energy intensive process due to the cryogenic operating characteristics.1 Thus, the natural gas liquefaction process has the energy intensive characteristics because of its cryogenic operation condition and most of energy is consumed by compression unit.2 Therefore, minimizing compression energy requirement is the major concern in design and optimization of the natural gas liquefaction process. The natural gas liquefaction processes can be classified by the number of refrigeration cycles and the type of refrigerants used.3 Because different processes have different characteristics in energy efficiency and number of equipment, the comparison between various liquefaction processes have to be performed to find the optimal design; in terms of the energy as well as cost Furthermore, the plant size also affects to the capital cost. In economics of scale, larger size of plant can make more profit. The amount of the increased profit by increasing the size is different to different processes. Therefore, the most profitable process can be different by different size of plant. For these reasons, it is needed to optimize in terms of the cost with different liquefaction processes to find optimal design and operating condition in various plant sizes.

The main purpose of this study is to make strategies of process and size selection by finding the optimal natural gas liquefaction processes in various plant sizes. To find optimal design and size, the operating cost including energy requirement and capital cost have to be considered simultaneously. The cost based model can consider the plant size which cannot be considered in thermodynamic model. Moreover, the price and flow rate of the natural gas feed and LNG also affect to the profit. Therefore, the profit optimization model have to be developed because it can consider the cost of law material and the product.

The profit optimization model is developed with energy self-sufficient liquefaction system. Among various natural gas liquefaction processes, Single Mixed Refrigerant (SMR), Dual Mixed Refrigerant (DMR), and Propane-precooled Mixed Refrigerant (C3MR) processes are targeted because they are well known and have representative refrigeration characteristics. Optimization model is developed by gPROMS software and the model includes thermodynamic model and cost model. The cost model basically follows the six-tenth factor rule. The objective function is max. specific profit which is profit per unit mass of LNG (USD/ton-LNG). The unit prices of natural gas and LNG are applied based on the Henry-hub prices announced by EIA, the unit prices of the natural gas and the LNG are applied 2.5 USD per Mcf and 4.5 USD per Mcf respectively to reflect recent prices.4 The detailed mathematical model basically follows the model which is presented in previous work.5

The profit optimization with various size of SMR, DMR, and C3MR processes are performed and the results of optimizations are compared and discussed. The portfolios for the liquefaction ratio, specific energy requirement, specific cost, and specific profit obtained by the profit optimizations for each process. The most economical size range for each process design is figured out by drawing specific profit portfolios. SMR process which has the simplest configuration has an economic strength in the small plant size. Even though the energy efficiency is the lowest among three liquefaction processes, the low plant cost of SMR process can cover the disadvantage of the low efficiency in the small plant size. However, as the plant size increases, DMR process catches up SMR process in terms of the specific profit. The configuration of DMR process is more complex and it requires more equipment than SMR process. For the medium size of plant, the higher energy efficiency makes the DMR process economical compare to SMR process. For the large plant size, the C3MR process is most economical because of the highest efficiency although it requires much more equipment compare to SMR and DMR processes.

This study is performed with 2.5 USD per Mcf for the natural gas price and 4.5 USD per Mcf for the LNG price. By applying those prices, the economical ranges of each process are figured out. In addition, the natural gas liquefaction process selection strategies are suggested. There is a possibility that different prices of the natural gas and the LNG could show different economical ranges of various liquefaction processes. However, the optimizations with the profit optimization model which is developed in this thesis can be also applied to the process selection strategies for different situation. In addition, the specific cost and specific profit portfolios for various process designs by size can be applied to the supply chain network research field, and it can make the result more rigorous by multi-scale problem formation. Moreover, the specific profit portfolio approach can be applied to other chemical process industries.

REFERENCES

[1] Mortazavi, A., et al., “Enhancement of APCI cycle efficiency with absorption chillers,” Energy, 35 (9), pp. 3877-3882 (2010).

[2] Lee, I., et al., “Decision Making on Liquefaction Ratio for Minimizing Specific Energy in a LNG Pilot Plant,” Ind. Eng. Chem. Res., 54 (51), 12920-12927 (2015).

[3] Lim, W., et al., “Current status and perspectives of liquefied natural gas (LNG) plant design,” Ind. Eng. Chem. Res., 52 (9), 3065-3088 (2013).

[4] U.S. Energy Information Administration, “U.S. Natural Gas Prices,”
https://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm, (accessed on Apr. 2017).

[5] Lee, I., and Moon, I., “Economic Optimization of Dual Mixed Refrigerant Liquefied Natural Gas Plant Considering Natural Gas Extraction Rate,” Ind. Eng. Chem. Res., 56 (10), 2804-2814 (2017)