scholarly journals Milling Parameters Optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and Particle Swarm Optimization

2020 ◽  
Vol 124 (3) ◽  
pp. 937-952
Author(s):  
Haitao Yue ◽  
Chenguang Guo ◽  
Qiang Li ◽  
Lijuan Zhao ◽  
Guangbo Hao
Keyword(s):  
Response Surface Methodology ◽  
Particle Swarm Optimization ◽  
Response Surface ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Thin Wall ◽  
Swarm Optimization ◽  
Milling Parameters

scholarly journals COMPARISON OF PARTICLE SWARM OPTIMIZATION AND RESPONSE SURFACE METHODOLOGY IN FERMENTATION MEDIA OPTIMIZATION OF FLEXIRUBIN PRODUCTION

2019 ◽  
Vol 81 (2) ◽  
Author(s):  
Siti Nurulasilah Suhaimi ◽  
Siti Mariyam Shamsuddin ◽  
Wan Azlina Ahmad ◽  
Shafaatunnur Hasan ◽  
Chidambaram Kulandaisamy Venil
Keyword(s):  
Sensitivity Analysis ◽  
Response Surface Methodology ◽  
Particle Swarm Optimization ◽  
Response Surface ◽  
Particle Swarm ◽  
Media Optimization ◽  
Swarm Optimization ◽  
Empirical Modelling ◽  
Present Response ◽  
Fermentation Media

At present, response surface methodology (RSM) is the most preferred method for fermentation media optimization. However, in the last two decades, artificial intelligence algorithm has become one of the most efficient methods for empirical modelling and optimization. One of the popular developed approaches is Particle Swarm Optimization (PSO), which is used in optimizing a problem. This paper focuses on comparative studies between RSM and PSO in fermentation media optimization for the production of flexirubin production using Chryseobacterium artocarpi CECT 8497T. Two methodologies were compared for in terms of their modeling, sensitivity analysis, and optimization abilities. All experiments were performed accordingly to box-behnken design (BBD), and the generated data was analyzed using RSM and PSO. The sensitivity analysis performed by both methods has given comparative results. Based on the correlation coefficient, the model developed with PSO was found to be superior to the model developed with RSM. The result shows that PSO gives a better pigmentation yield with optimal fermentation concentration.

scholarly journals Optimization of Zinc Recovery from Sphalerite Using Response Surface Methodology and Particle Swarm Optimization

2021 ◽  
Author(s):  
Okechukwu D. Onukwuli ◽  
Ikechukwu A. Nnanwube
Keyword(s):  
Response Surface Methodology ◽  
Particle Swarm Optimization ◽  
Response Surface ◽  
Acid Concentration ◽  
Particle Swarm ◽  
Liquid Ratio ◽  
Swarm Optimization ◽  
Zinc Recovery ◽  
Stirring Rate ◽  
Solid Liquid

Hydrometallurgical leaching process has been identified as a viable procedure for recovering metals of value from their matrices. The optimization of zinc recovery from sphalerite in nitric acid solution was carried out in this study. The Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) tool in matlab were deployed for the optimization studies. RSM modeling gave optimum conditions of 73.0 °C leaching temperature, 3.48 M acid concentration, 0.027 g/mL solid/liquid ratio, 411.02 rpm stirring rate, and 82.82 minutes leaching time; with a zinc yield of 87.67 %. With PSO, about 86.9 % zinc was recovered at a leaching temperature of 69.1 °C, acid concentration of 1.8 M, solid/liquid ratio of 0.031 g/mL, stirring rate of 270 rpm and leaching time of 85 minutes. Thus, PSO and RSM proved to be good optimization tools.

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