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

• Narjes Sabeghi ¹
• Behrouz Kiani Talaei ²
• Reza Ghanbari ³
• Khatere Ghorbani-Moghadam ⁴

¹ Departments of Mathematics, Faculty of Basic sciences, Velayat University, Iranshahr, Iran

² University of Sistan and Baluchestan, Zahedan, Iran.

³ Department of Applied Mathematics, 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 Liquids (NGLs) from natural gas, one of which is refrigeration. In this method, the temperature 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 Natural Gas Liquids (NGL) production process by determining the optimal values for specific set-points such as temperature and pressure in various vessels and equipment. 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 Genetic Algorithm (NSGA II) and Artificial Neural Network system (ANN). We solve the defined multi-objective problem by using NSGAII. In order to design 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 NGLs from natural gas, as a case study for this article. Finally, we demonstrated the effectiveness of our proposed algorithm using nonparametric statistical Kruskal-Wallis test.

Keywords

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

Main Subjects

• Optimization

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