Optimization of some parameters on agglomeration performance of Zonguldak bituminous coal by oil agglomeration

Aslan, N.; Ünal, İlkay

doi:10.1016/j.fuel.2008.10.039

Cited by 38 publications

(5 citation statements)

References 25 publications

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“…For this purpose, statistical design of experiments have been widely reported for the process characterization, optimization and modeling in recent years [15,[17][18][19][20][21][22]. Although the experimental design technique has been widely studied by many researchers as an established and promising method for optimization and formulation of various types of processes, however, there are no systematic papers in the literature specifically devoted to a study of the response surface modeling of Pb(II) removal from aqueous solution by P. vera L. using an experimental design technique.…”

Section: Introductionmentioning

confidence: 99%

Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box–Behnken experimental design

Yetilmezsoy¹,

Demirel²,

Vanderbei³

2009

Journal of Hazardous Materials

525

197

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Section: Introductionmentioning

confidence: 99%

Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box–Behnken experimental design

Yetilmezsoy¹,

Demirel²,

Vanderbei³

2009

Journal of Hazardous Materials

525

197

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“…Whether the F value has suffi ciently high value or not is assessed with the p value. While low p values indicate rejection of the hypothesis, at the same time they represent the signifi cance of the variable (whether the parameter has a greater effect on the result) (Aslan and Ünal, 2009;Kumar et al, 2009;Amani-Ghadima et al, 2013;Sabuncu, 2014). If the p value is smaller than 0.05 it is signifi cant, if it is larger than 0.1 it is insignifi cant.…”

Section: Resultsmentioning

confidence: 99%

Muğla Yatağan Bağyaka Li̇nyi̇ti̇ni̇n Ultrasoni̇k Dalgalar İle Desülfüri̇zasyon Prosesi̇nde Bazi Parametreleri̇n Opti̇mi̇zasyonu

Sansar¹

2018

Maden Tetkik Ve Arama Dergisi

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In this study, the desulfurization process developed using ultrasonic waves for Muğla Yatağan Bağyaka lignite had the optimum conditions for parameters affecting the ash and sulfur removal potentials determined with the Surface Response Method and a model created. The process parameters to obtain optimum desulfurization and ash removal were chosen as the ultrasonic treatment time, solid content, concentration of chemical reactive (H 2 O 2) and reactive volume, and the optimum values were determined. Using this data with the aid of the Design Export 7.0 program, the regression model was found as a second degree polynomial equation. The coeffi cients of determination (R 2) for desulfurization and ash removal regressions were 0.96 and 0.97, respectively, in the determined model. The model prediction values and experimental results for desulfurization and ash removal in the investigated parameter intervals were compared and the fi t was identifi ed. In the experimental removal of sulfur types with optimum desulfurization it was found that pyritic 17.02%, sulfate 16.67% and organic 9.52%.

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“…The experiments can be conducted in an organized manner and can be analysed systematically to obtain much needed information. The information can be used to optimize the process [22,[24][25][26][27]29].…”

Section: Methodsmentioning

confidence: 99%

Optimizing and evaluating the operational factors affecting the cyanide leaching circuit of the Aghdareh gold processing plant using a CCD model

Azizi

Ghaedrahmati

2015

Proc. R. Soc. A.

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A response surface method using a central composite design was employed to evaluate, model and optimize the influence of five main factors in the gold cyanidation process. These factors were pH, solid percentage, NaCN concentration, particle size and leaching time. A second-order equation was proposed and developed for the relationship between the gold recoveries and influential factors. The modelling results indicated that the factors influencing the degree of cyanide leaching of gold were in the order of leaching time > NaCN concentration 2 > particle size > pH > NaCN concentration > leaching time 2 > solid percentage × particle size > solid percentage × NaCN concentration > solid percentage. Also, we obtained a coefficient of determination ( R 2 ) greater than 93%, which showed that the developed model was well fitted to the experimental data. In addition, the model equation was individually optimized by using quadratic programming to maximize gold recoveries within the experimental range. The optimum condition was found to be pH 10.11 for the solution, 36.07% for the solids content, 729.56 ppm for the NaCN concentration, 37.52 μ m for the particle size and 23.2 h for the leaching time. Under these conditions, the highest recovery of gold was achieved of approximately 91.5%.

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Optimization of some parameters on agglomeration performance of Zonguldak bituminous coal by oil agglomeration

Cited by 38 publications

References 25 publications

Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box–Behnken experimental design

Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box–Behnken experimental design

Muğla Yatağan Bağyaka Li̇nyi̇ti̇ni̇n Ultrasoni̇k Dalgalar İle Desülfüri̇zasyon Prosesi̇nde Bazi Parametreleri̇n Opti̇mi̇zasyonu

Optimizing and evaluating the operational factors affecting the cyanide leaching circuit of the Aghdareh gold processing plant using a CCD model

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