Optimizing conditions for wet grinding of synthetic rutile using response surface methodology

Chen, G.; Peng, Jinhui; Chen, J.

doi:10.1007/bf03402324

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…Statistical experimental design methods involve using mathematical models for designing chemical processes and analyzing the process results [4]. Among them, response surface methodology (RSM) stands out as a popular method utilized in many fields [5,6]. RSM is a combination of mathematical and statistical techniques used in an empirical study of relationships and optimization [7].…”

Section: Introductionmentioning

confidence: 99%

Application of multivariate optimization to electrochemical determination of methyldopa drug in the presence of diclofenac at a nanostructured electrochemical sensor

Ghoreishi

Saeidinejad

Behpour

et al. 2015

Sensors and Actuators B: Chemical

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Section: Introductionmentioning

confidence: 99%

Application of multivariate optimization to electrochemical determination of methyldopa drug in the presence of diclofenac at a nanostructured electrochemical sensor

Ghoreishi

Saeidinejad

Behpour

et al. 2015

Sensors and Actuators B: Chemical

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“…The related literature shows that classical RSM is commonly applied using a second-order polynomial as a prediction model [143]. For example, optimal conditions of rotation speed, solid concentration, and grinding time were obtained for wet grinding in a ball mill using central composite design with a second-order polynomial [152]. In fact, an excellent determination coefficient (R 2 = 0.9989) was obtained, which indicates a good agreement with experimental values.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 69%

“…One advantage of the classical RSM is that it needs a smaller number of experiments, which means it is cheaper and requires less time. These characteristics explain the large number of applications, including flotation [148,149], grinding [150][151][152], and thickening [153], among other processes.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

2019

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Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted.

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“…Low CV values (3.80%) show higher accuracy and a good reliability of the experimental values. 39,40,44 The F value are also used to estimate the significance level of each independent variables. χ 2 shows the maximum F value of 158.84, which indicates that the collector dosage has the greatest influence on the decarbonizing ratio compared with χ 1 , χ 3 and χ 4 .…”

Section: Resultsmentioning

confidence: 99%

“…For this process to be effective, a process optimization exercise to identify the optimum conditions is performed. 39,40,44 Response surface methodology (RSM) is one of the relevant multivariate techniques that has the capability to perform multivariant experimental design, statistical modeling and process optimization. It is the most economical and convenient method for characterizing a complicated experimental process with minimum number of experiments.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the decarbonizing ratio of boron carbide powders by reverse flotation using response surface methodology

Bai

Sun

et al. 2020

Int J Applied Ceramic Tech

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Free carbon is the main impurity in boron carbide and has many side effects on the quality of boron carbide. In this study, reverse flotation was used for the first time to remove free carbon in boron carbide. The response surface methodology was utilized to optimize the reverse flotation factors, and the samples were analyzed by X-ray diffraction, scanning electron microscope, laser particle size analyzer, and chemical analysis. The study results reveal that the main factors affecting the decarbonization ratio were slurry concentration, collector dosage, foaming agent dosage and pH value. Furthermore, the results also show that reverse flotation could be applied effectively to the removal of free carbon in boron carbide. Slurry concentration of 25.14%, collector dosage of 567.9 g/t, foaming agent dosage of 199.32 g/t and pH value of 8.4 were found to be the best conditions. Under the optimal conditions, the decarbonization ratio is 84.23%. Mass ratio of free carbon in boron carbide reduced from 2.98 to 0.47.

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Optimizing conditions for wet grinding of synthetic rutile using response surface methodology

Cited by 6 publications

References 10 publications

Application of multivariate optimization to electrochemical determination of methyldopa drug in the presence of diclofenac at a nanostructured electrochemical sensor

Application of multivariate optimization to electrochemical determination of methyldopa drug in the presence of diclofenac at a nanostructured electrochemical sensor

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Optimization of the decarbonizing ratio of boron carbide powders by reverse flotation using response surface methodology

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