Surrogate-assisted constrained hybrid particle swarm optimization algorithm for propane pre-cooled mixed refrigerant LNG process optimization

被引:4
作者
Ahmed, Rasel [1 ,2 ]
Mahadzir, Shuhaimi [1 ,2 ]
Ferdush, Jannatul [3 ]
Matovu, Fahad [1 ]
Mota-Babilioni, Adrian [4 ]
Hafyan, Rendra Hakim [5 ]
机构
[1] Univ Teknol PETRONAS, Dept Chem Engn, Seri Iskandar, Malaysia
[2] Virginia Polytech Inst & State Univ, Dept Chem Engn, Blacksburg, VA 24061 USA
[3] Bangladesh Univ Profess, Dept Disaster Management & Resilience, Dhaka, Bangladesh
[4] Univ Jaume I UJI, Dept Mech Engn & Construct, ISTENER Res Grp, Castellon de La Plana, Spain
[5] Univ Surrey, Sch Chem & Chem Engn, Guildford GU2 7XH, England
关键词
Propane pre-cooled mixed refrigerant (C3MR); process; Liquified natural gas (LNG); Feasibility-based constraint handling; Radial basis function network (RBFN); Particle swarm optimization (PSO); Social learning particle swarm optimization; (SLPSO); Surrogate modeling and optimization; NATURAL-GAS LIQUEFACTION; DESIGN; MODELS; CYCLE;
D O I
10.1016/j.energy.2024.132165
中图分类号
O414.1 [热力学];
学科分类号
摘要
The propane pre-cooled mixed refrigerant (C3MR) process is one of the most widely used and efficient natural gas liquefaction processes. However, optimization of this process involves various design and operational constraints, complex thermodynamics, and hence nonconvex in nature. Therefore, it ' s computationally expensive, often involving trial and error approaches. Existing optimization algorithms often possess flaws such as insufficient accuracy and premature convergence, leading to suboptimal solutions that may violate essential process constraints. This article proposes a novel method to optimize the C3MR process that handles the computational complexity, meets the constraints, and achieves feasible solutions quickly. The proposed method includes modified feasibility-based constraint handling and radial basis function network-assisted surrogate modeling and optimization, where the power consumption is optimized using a hybrid of the particle swarm optimization (PSO) and social learning PSO algorithm. The proposed algorithm achieves the optimum power consumption (121109.31 kW), which is 21.5 % less than the base case (154200 kW). The optimization results are compared with similar optimization algorithms, where the proposed algorithm outperformed the other algorithm regarding optimal solution, convergence, and speed. The results from this study are compared to previous studies from the literature, which validate the accuracy and applicability of the proposed method.
引用
收藏
页数:15
相关论文
共 44 条
[41]   A hybrid Particle Swarm Optimization(PSO) algorithm schemes for integrated process planning and production scheduling [J].
Zhao, Fuqing ;
Zhu, Aihong ;
Yu, Dongmei ;
Yang, Yahong .
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, :6772-+
[42]   Surrogate-model-based, particle swarm optimization, and genetic algorithm techniques applied to the multiobjective operational problem of the fluid catalytic cracking process [J].
Cuadros Bohorquez, Jose F. ;
Tovar, Laura Plazas ;
Wolf Maciel, Maria Regina ;
Melo, Delba C. ;
Maciel Filho, Rubens .
CHEMICAL ENGINEERING COMMUNICATIONS, 2020, 207 (05) :612-631
[43]   A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process [J].
Fattahi, Parviz ;
Rad, Naeeme Bagheri ;
Daneshamooz, Fatemeh ;
Ahmadi, Samad .
ASSEMBLY AUTOMATION, 2020, 40 (03) :419-432
[44]   Wind-assisted microgrid grid code compliance employing a hybrid Particle swarm optimization-Artificial hummingbird algorithm optimizer-tuned STATCOM [J].
Imtiaz, Saqif ;
Yang, Lijun ;
Khan, Hafiz Muhammad Azib ;
Munir, Hafiz Mudassir ;
Alharbi, Mohammed ;
Jamil, Mohsin .
WIND ENERGY, 2024, 27 (07) :711-732