Optimizing natural gas liquids (NGL) production process: A multi-objective approach for energy-efficient operations using genetic algorithm and artificial neural networks

Document Type : Research Article

Authors

• N. Sabeghi ¹
• B. Kiani Talaei ²
• R. Ghanbari ³
• K. Ghorbani-Moghadam ⁴

¹ Department of Mathematics, Faculty of Basic Sciences, Velayat University, Iranshahr, Iran.

² University of Sistan and Baluchestan, Zahedan, Iran.

³ Ferdowsi university of Mashhad, Mashhad, Iran.

⁴ Mosaheb Institute of Mathematics, Kharazmi University, Tehran, Iran.

10.22067/ijnao.2024.83955.1303

Abstract

There are various techniques for separating natural gas liquid (NGL) from natural gas, one of which is refrigeration. In this method, the temper-ature is reduced in the dew point adjustment stage to condense the NGLs. The purpose of this paper is to introduce a methodology for optimizing the NGLs production process by determining the optimal values for specific set-points such as temperature and pressure in various vessels and equip-ment. The methodology also focuses on minimizing energy consumption during the NGL production process. To do this, this research defines a multi-objective problem and presents a hybrid algorithm, including a ge-netic algorithm (NSGA II) and artificial neural network (ANN) system. We solve the defined multi-objective problem using NSGA II. In order to de-sign a tool that is a decision-helper for selecting the appropriate set-points, the ability of the ANN algorithm along with multi-objective optimization is evaluated. We implement our proposed algorithm in an Iranian chemical factory, specifically the NGL plant, which separates NGL from natural gas, as a case study for this article. Finally, we demonstrate the effectiveness of our proposed algorithm using the nonparametric statistical Kruskal–Wallis test.

Keywords

• Natural gas liquid (NGL)
• Multi-objective optimization
• Artifi-cial neural network
• NSGA II

Main Subjects

• Optimization

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