Combining 10 meta-heuristic algorithms, CFD, DOE, MGGP and PROMETHEE II for optimizing Stairmand cyclone separator

• Published in: Powder technology Vol. 382; pp. 70 - 84
• Main Authors: Izadi, Ahad, Kashani, Elham, Mohebbi, Ali
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 01.04.2021; Elsevier BV
• Subjects: Algorithms; CFD simulation; Computational fluid dynamics; Computer applications; Cyclone separators; cylinders; Decision making; design; Design of experiments; diameter; dimensions; DOE; experimental design; Fluid dynamics; fluid mechanics; Gas cyclone separator; Genetic algorithms; height; Heuristic; Heuristic methods; Hydrodynamics; industry; length; Mathematical models; Multi-gene genetic programming; Multi-objective optimization; Multiple objective analysis; Particle swarm optimization; pressure; Pressure drop; Problem solving; PROMETHEE II; Separators; technology; width; CFD simulation; Multi-gene genetic programming; Multi-objective optimization; DOE; Gas cyclone separator; PROMETHEE II
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2020.12.056

Gas cyclone separators have been widely used in different industries. In this study, to find the best geometrical ratios of Stairmand cyclone separator, computational fluid dynamics (CFD), design of experiments (DOE), multi-gene genetic programming (MGGP), and ten meta-heuristic algorithms were combined. Six geometrical dimensions of the gas cyclone separator including inlet height and width, vortex finder length and its diameter, cylinder height and cone-tip diameter were optimized. The obtained models from MGGP were optimized by ten meta-heuristic algorithms and non-dominated Pareto fronts were analyzed using six unary and binary metrics and PROMETHEE II as a decision making method. According to the optimization results, multi-objective Particle Swarm Optimization (MOPSO) showed the best performance and generated more preferred designs than Stairmand design compared to other algorithms. These preferred designs increased the collection efficiency within 0.36 to 6% and decreased the pressure drop within 3.3 to 27.5% compared to the Stairmand. [Display omitted] •To improve Stairmand cyclone performance, CFD, DOE, MGGP and ten meta-heuristic algorithms were combined.•17 cyclones with different dimensions based on DOE are simulated by CFD.•Two models including the collection efficiency and pressure drop are obtained by MGGP.•The obtained models are optimized by ten meta-heuristic algorithms to achieve the cyclones with high performance.•MOPSO algorithm generated more optimal designs, which dominated the Stairmand design.