Optimization of Three-Phase Horizontal Separator Using the Genetic Algorithm Method to Reduce Manufacturing Costs and Promote the Phase Separation Process in the Petroleum Industry

Montazeri, Vahab; Ghazi, Atefeh

doi:10.22050/ijogst.2023.386627.1668

Optimization of Three-Phase Horizontal Separator Using the Genetic Algorithm Method to Reduce Manufacturing Costs and Promote the Phase Separation Process in the Petroleum Industry

Document Type : Research Paper

Authors

Vahab Montazeri ¹

Atefeh Ghazi ²

¹ Ph.D., Instructor, Department of Chemical Engineering, Razi University, Kermanshah, Iran

² M.S. Student, Department of Chemical Engineering, Razi University, Kermanshah, Iran

https://doi.org/10.22050/ijogst.2023.386627.1668

Abstract

Separating two immiscible liquids from gas is essential to produce light liquid, heavy liquid, and vapor phases. Water separation from hydrocarbons is a practical example in the oil industry. For such separation in industry, a three-phase separator is used. In this study, different parameters and the weight of the three-phase separator were optimized with the genetic algorithm (GA). Finally, the total cost of manufacturing the separator was decreased. Different types of three-phase separators are vertical, horizontal, and spherical. The separator worked in operating conditions of 172 kPa and 445 K, and the actual weight of the separator was 8131 kg. For the optimization target, the flow of vapor, light liquid, and heavy liquid was considered constant during the optimization process. The objective function (OF) was obtained from the weight of the separator and three multiparameter equations. Also, seven parameters, including the separator aspect ratio (L/D), the height of heavy liquid (H_HL), the height of light liquid (H_LL), the hold-up time (T_H), the surge time (T_S), the low liquid level (H_LLL), and the vapor level (H_v), were used in GA as constraints. The weight of the optimized separator was calculated at approximately 6001 kg. Hence, with this method, the total weight of the separator decreased by about 26.2 % as compared to the actual weight of the separator. On the other hand, the maximum difference between the answers was 3.3%, which was acceptable. Further, the error analysis of the predicted results was calculated by mean absolute percentage error (MAPE) for seven design parameters of the three-phase separator and separator weight, which were in an acceptable level of accuracy. The presented approach could have potential application for developing low-cost manufacturing of three-phase separators in the petroleum industry.

Highlights

Optimization of three-phase separators with genetic algorithm;
Acceptable error in the predicted results;
The reduction of the total weight of the separator by about 24%.

Keywords

Optimization

Three-phase separator

Genetic algorithm

Petroleum

Objective function

Subjects

Accounting

Abdoun, O., and Abouchabaka, J. A., Comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. arXiv preprint arXiv: p.1203-3097, 2012.

Ahmadi, M. H., and Ahmadi, M. A., Thermodynamic analysis and optimization of an irreversible Ericsson cryogenic refrigerator cycle. Energy Conversion and Management, Vol. 89, p. 147-155, 2015.

Azad, A., Pavlopoulos, G. A., Ouzounis, C. A., Kyrpides, N. C., Buluç, A., HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks. Nucleic acids research, Vol. 46, No. 6, e33-e33, 2018.

Bradley, H. B., Petroleum Engineering Handbook, 1987.

Carios, E., Vega, L., Pardo, R., and Ibarra, J., Experimental Study of a Poor Boy Downhole Gas Separator Under Continuous Gas-Liquid Flow. In SPE Artificial Lift Conference-Americas. Society of Petroleum Engineers, 2013.

Couper, J. R., Penney, W. R., and Fair, J. R., Chemical Process Equipment-Selection and Design (Revised 2ndedition), Gulf Professional Publishing, 2009.

Ahmadi, M. H., & Ahmadi, M. A., Thermodynamic analysis and optimization of an irreversible Ericsson cryogenic refrigerator cycle. Energy Conversion and Management, Vol. 89, p. 147-155, 2015.

Feng, J., Chang, Y., Peng, X., and Qu, Z., Investigation of The Oil–Gas Separation in A Horizontal Separator for Oil-Injected Compressor Units. Proceedings Of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, Vol. 222, No. 4. p. 403–412, 2008.

Garg, H., A Hybrid GSA-GA Algorithm for Constrained Optimization Problems. Information Sciences, Vol. 478, p. 499–523, 2019.

Ghaffarkhah, A., Dijvejin, Z. A., Shahrabi, M. A., Moraveji, M. K., and Mostofi, M., Coupling of CFD and Semiempirical Methods for Designing Three-Phase Condensate Separator: Case Study and Experimental Validation. Journal Of Petroleum Exploration and Production Technology, Vol. 9, No. 1. p. 353–382, 2019.

Ghaffarkhah, A., Shahrabi, M. A., Moraveji, M. K., and Eslami, H., Application of CFD for Designing Conventional Three Phase Oilfield Separator. Egyptian Journal of Petroleum, Vol. 26, No. 2. p. 413–420, 2017.

Gong, H., Luo, X., Yang, Y., Huo, C., Peng, Y., Yu, B., and Zhang, H., Structural Optimization and Separation Characteristic of a Separating Device for Three Phases: Oil, Water, and Solid. Process Safety and Environmental Protection, Vol. 171, p. 200–213, 2023.

Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, And Artificial Intelligence, MIT Press, 1992.

Jianfei, L. U. O., Weitie, L., Xiaolong, C., and Jingyuan, L. I., Optimization of fermentation media for enhancing nitrite-oxidizing activity by artificial neural network coupling genetic algorithm. Chinese Journal of Chemical Engineering, Vol. 20, No. 5, p. 950-957, 2012.

Joshy, A., Ma, A., and Nambiar, A., CFD Analysis and Optimization of Three Phase Oil Separator, 2022.

Konak, A., Coit, D. W., and Smith, A. E., Multi-Objective Optimization Using Genetic Algorithms: A Tutorial. Reliability Engineering and System Safety, Vol. 91, No. 9, p. 992–1007, 2006.

Li, J. and Gu, Y., Coalescence of Oil-In-Water Emulsions in Fibrous and Granular Beds. Separation and Purification Technology, Vol. 42, No. 1, p. 1–13, 2005.

Manasrah, A. M. and Ba Ali, H., Workflow Scheduling Using Hybrid Ga-Pso Algorithm in Cloud Computing. Wireless Communications and Mobile Computing, 2018.

Monnery, W. D., and Svrcek, W. Y., Successfully Specify Three-Phase Separators. Chemical Engineering Progress, Vol. 90, p. 29–29, 1994.

Nazemzadegan, M. R., Kasaeian, A., Toghyani, S., Ahmadi, M. H., Saidur, R., and Ming, T., Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm. Frontiers in Energy, Vol. 14, No. 3, p. 649-665, 2020.

Qaroot, Y. F., Kharoua, N., and Khezzar, L., Discrete Phase Modeling of Oil Droplets in The Gas Compartment of a Production Separator. In ASME 2014 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, Pp. V007T09A046-V007T09A046, 2014.

Sayda, A. F., and Taylor, J. H. Modeling and Control of Three-Phase Gravity Separators in Oil Production Facilities. In 2007 American Control Conference, IEEE, p. 4847–4853, 2007.

Shoghl, S. N., Naderifar, A., Farhadi, F., and Pazuki, G., Optimization of Separator Internals Design Using CFD Modeling in The Joule-Thomson Process. Journal of Natural Gas Science and Engineering, Vol. 89, p. 103889, 2021.

Stewart, M., and Arnold, K., Gas-Liquid and Liquid-Liquid Separators. Gulf Professional Publishing,

2008.

Van Veldhuizen, D. A., and Lamont, G. B. Multiobjective Evolutionary Algorithms: Analyzing the State-Of-The-Art. Evolutionary Computation, Vol. 8, No. 2, p. 125–147, 2000.

Wang, L., Wang, S., Zhang, R., Wang, C., Xiong, Y., Zheng, X., and Rui, Z., Review of multi-scale and multi-physical simulation technologies for shale and tight gas reservoirs. Journal of Natural Gas Science and Engineering, Vol. 37, p. 560-578, 2017.

Wilkinson, D., Waldie, B., Nor, M. M., and Lee, H. Y., Baffle Plate Configurations to Enhance Separation in Horizontal Primary Separators. Chemical Engineering Journal, Vol. 77, No. 3, p. 221–226, 2000.

Wongthatsanekorn, W., and Phruksaphanrat, B., Genetic Algorithm for Short-Term Scheduling of Make-Anpack Batch Production Process. Chinese Journal of Chemical Engineering, Vol. 23, p. 1475–1483, 2015.

Xu, C., Kang, Y., You, Z., and Chen, M., Review on Formation Damage Mechanisms and Processes in Shale Gas Reservoir: Known and to Be Known. Journal Of Natural Gas Science and Engineering, Vol. 36, p. 1208–1219, 2016.