Increase in demand for clean energy is one of the strategic issues in Indonesia nowadays, considering the significant economic growth of the country. A conventional LNG supply chain is not the best solution taking into consideration its high investment. The possibility of using a small scale LNG supply chain concept (Mini LNG) is recently sought by the government and private sectors in Indonesia. It is even more promising when we consider the amounts and number of stranded gas fields in the country. One of the main obstacles to the development plan is the geographical position of Indonesia as an archipelagic country.
This paper presents a case study of LNG supply chain model of 10 mmscfd Gas Sales Agreement (GSA) in Batam and its design of LNG transportation model from Batam to Siantan-West Kalimantan [1]. The distance between Batam and Siantan is approximately 392 nautical miles. Two main objectives are covered in this paper. The first one is an implementation of the Analytical Hierarchy Process (AHP) to select the best location for mini LNG plant, and the second one is to design the LNG supply chain model based on optimization approach.
The AHP model uses a pairwise comparison of 4 (four) qualitative attributes and 14 (fourteen) sub-attributes. 3 alternatives of location for mini LNG plant are evaluated, namely: Tanjung Uncang, Pemping Island and Janda Berhias Island. A sensitivity analysis by varying the weight of some critical attributes is also conducted to ensure that preferred location is sensitively selected with minimum error.
The optimization of the LNG supply chain model is carried out by means of Gradually Reduced Gradient (GRG) methods. The Objective is to attain one design that will minimize investment (cost). Decision variables of the model are LNG plant capacity, storage tank capacity in loading and receiving terminal, vessel size, number of round trip, number of operating vessels, regasification capacity at the receiving terminal, and others.
Optimal design and planning of biodiesel supply chain network: a scenario-based robust optimization approach
