2015
DOI: 10.15244/pjoes/40270
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Design and Optimization of Cu(II) Adsorption Conditions from Aqueous Solutions by Low-Cost Adsorbent Pumice with Response Surface Methodology

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Cited by 37 publications
(17 citation statements)
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“…It is a powerful optimization method which has recently been used for other optimization processes (Şahan et al 2010). The reasons for its popularity are that it does not require additional consumption of chemicals for each parameter, nor is it especially time-consuming, costly or labor-intensive (Chi et al 2012, Myers and Montgomery 2002, Öztürk and Şahan 2015, Şahan and Öztürk 2014). This method is based on the fi t of mathematical models (linear, square polynomial functions, and others) to the experimental results generated from the designed experiment and confi rmation of the model obtained via statistical techniques.…”
Section: Introductionmentioning
confidence: 99%
“…It is a powerful optimization method which has recently been used for other optimization processes (Şahan et al 2010). The reasons for its popularity are that it does not require additional consumption of chemicals for each parameter, nor is it especially time-consuming, costly or labor-intensive (Chi et al 2012, Myers and Montgomery 2002, Öztürk and Şahan 2015, Şahan and Öztürk 2014). This method is based on the fi t of mathematical models (linear, square polynomial functions, and others) to the experimental results generated from the designed experiment and confi rmation of the model obtained via statistical techniques.…”
Section: Introductionmentioning
confidence: 99%
“…Optimization of MB adsorption study was finalized using central composite design (CCD), a sub-program of RSM, through Design-Expert (version 12.0) software. The response can be associated with the operating parameters by linear or quadratic models (Ozturk & Sahan, 2015). Seventeen experimental runs were carried out to develop the correlation between the functional variables of Mg-PKS biochar composite to the removal of MB from an aqueous solution.…”
Section: Adsorbent Characterizationmentioning
confidence: 99%
“…[39][40][41] The benefits of using this approach are the consumption of chemicals, the expenditure, and the time of experiments can be minimized. [42][43][44][45][46][47][48] The RSM includes a mathematical algorithm based on experimental results obtained from experiments designed by a program, and the model verification is derived from statistical analysis. The polynomial functions are used to define the variables under investigation and to express the optimized or required experimental conditions.. 42 In RSM, the variables analyzed are chosen based on the intent of the research.…”
Section: Introductionmentioning
confidence: 99%
“…The RSM is a robust mathematical and statistical approach commonly used to analyze and optimize the most effective process conditions 39‐41 . The benefits of using this approach are the consumption of chemicals, the expenditure, and the time of experiments can be minimized 42‐48 . The RSM includes a mathematical algorithm based on experimental results obtained from experiments designed by a program, and the model verification is derived from statistical analysis.…”
Section: Introductionmentioning
confidence: 99%