(89a) Operational Optimization of LNG Processes

Liquefied Natural Gas (LNG), as an efficient means of natural gas transportation and a more environmental friendly fuel for long-haul vehicles, plays a critical role in gas utilization.

In the past few years, many natural gas liquefaction plants have been built worldwide and started to produce LNG. However, due to the difficulties and complexities of managing LNG facilities, many plant operators found it hard to keep the energy consumption at a satisfactory level all the time. The typical energy consumption such as the power demand of refrigerant compressors for LNG production in particular, is 5% or even more than what is actually necessary. Typical reasons for such excessive energy consumption are:

1. Engineering companies design refrigerant composition, operating pressure and temperature for given specifications of natural gas feed. If natural gas flow rate is reduced, or feed composition is changed, the optimal operating conditions of the refrigerant may change, and are not easy to identify by plant operators.

2. Ambient temperature also significantly affects the operation of LNG plants. Increase of 5^oC in ambient temperature typically results in 5% or more energy consumption in LNG production. There are no clear or quantitative guidelines for plant operators to minimize the energy consumption when ambient temperature changes.

3. Plant operators have limited resources to evaluate the performance of LNG plants on a quantitative basis. The capability of existing commercial tool for operational optimization is very limited.

This paper presents a novel methodology for the operational optimization of existing LNG plants. The characteristics of existing compressors are represented by their performance curves, in which the relationship between pressure ratio and volume flow can be identified and used to define equipment constraints for optimization. The size of existing main cryogenic heat exchangers is represented by the UA (heat transfer coefficient U multiplied by heat transfer area A). The maximum limit of UA can be explicitly defined in the optimization and this guarantees that the best operating conditions can be practically achieved with existing main cryogenic heat exchangers.

As the optimization problem itself has a highly non-linear nature, a stochastic optimization approach, Genetic Algorithm (GA) has been selected to avoid being trapped in the local optima. With the GA optimization, the best operating conditions, such as refrigerant composition, pressure and temperature are identified and reported. The optimality of the best operation can also be inspected by the composite curves of the main cryogenic heat exchanger.

The advantage of the proposed optimization methodology is to systematically analyze and optimize the impacts of refrigerant composition, pressure and temperature on the overall energy performance of LNG plants. It also takes into account the operational restrictions imposed by existing equipment, such as refrigerant compressors and main cryogenic heat exchangers.

A case study is presented to illustrate the effectiveness of the developed optimization methodology. The results demonstrate that the power consumption of a typical refrigerant compressor can be reduced by about 7%. Although the optimization methodology has been illustrated with the single mixed refrigerant liquefaction technology in particular, it can be easily extended to other liquefaction technologies, such as propane precooled mixed refrigerant process and dual mixed refrigerant cycles.