Simulation and optimization of natural gas processing and production network consisting of LNG, GTL, and methanol facilities

A considerable growth is expected for the natural gas sector and some believe that it will be the leading primary fuel between 2020 and 2030. Different options are available to utilize the natural gas resource. Three processes namely, LNG, GTL, and methanol considered as the most promising utilization options are addressed in this paper to represent a natural gas processing and production network. The objective of this work is to illustrate the importance of incorporating rigorous simulation models in the decision-making process in the gas processing industry. A steady state simulation is carried out for LNG, GTL, and methanol processed to determine mass and energy balances, operating conditions, and equipment specification. The simulations' flowsheet is beneficial in many aspects. For example, accurate yield values can be obtained. Also, both the capital and operating costs can be estimated. Moreover, the environmental impact can be assessed quantitatively. Then, an optimization model is presented that is able to represent the processing and production network over a wide range of forecasted economic changes. The main feature of this work is the usage of an integrated simulation-optimization framework. Although the modeling, simulation and optimization of a single natural gas system have been addressed previously, the simulation and optimization of enterprise-wide natural gas processing has not been addressed to this extent in the literature. Furthermore, besides considering more than one utilization process, namely LNG, GTL, and methanol, this work addresses the preprocessing units of these utilization processes in a comprehensive manner. The results of the optimization are improved by utilizing data from process simulation. Such data are used to tune the optimization model. An illustrative case study is used to show the applicability of formulated optimization model on natural gas processing and production network and show how accurate representations of the plants are obtained from process simulation. The end result is a more highly optimized and sustainable processing and production network. (C) 2015 Elsevier B.V. All rights reserved.