Optimization of response surface methodology (RSM) for defluorination of spent carbon cathode (SCC) in fire-roasting aluminum electrolysis

Zhu, Zhi; Xu, Lei; Han, Zhaohui; Liu, Jianhua; Zhang, Libo; Tian, Shihong; Xu, Yan; Koppala, Sivasankar

doi:10.1016/j.mineng.2022.107565

Cited by 18 publications

(1 citation statement)

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“…RSM employs mathematical models to describe the relationship between input variables and response variables of interest. The models can then be used to predict response variables for various input factor combinations [47][48][49]. In regard to FGMs, RSM can be used to optimize material properties by determining the optimal distribution of constituent materials along the material's thickness.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Optimization and Characterization of Centrifugal-Cast Functionally Graded Al-SiC Composite Using Response Surface Methodology and Grey Relational Analysis

et al. 2023

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In this study, an optimization approach was employed to determine the optimal main parameters that improve the performance of functionally graded composites manufactured using a combination of stirring and horizontal centrifugal casting. Pure aluminum reinforced with silicon carbide particles was used as the material for the composites. The effects of key input parameters such as mold speed, pouring temperature, stirring speed, and radial distance were optimized using a combination of grey relational analysis and response surface methodology. The statistical significance of the predicted grey relational grade model was assessed through an analysis of variance to identify the appropriate main parameters. The results showed that radial distance had the greatest impact on the performance of the composites, followed by pouring temperature. The optimal combination of main parameters was determined to be a mold speed of 1000 rpm, a pouring temperature of 750 °C, a stirring speed of 150 rpm, and a radial distance of 1 mm. Confirmation tests using these optimal values resulted in a 54.69% improvement in the grey relational grade.

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Section: Design Of Experimentsmentioning

confidence: 99%