Application of response surface methodology and central composite rotatable design for modeling the influence of some operating variables of the lab scale thickener performance

Shahrivar, Alireza Aghajani; Ataallah, Soltani Goharrizi; Majid, Ebrahimzadeh Gheshlaghi; Sarafi, Amir; Mohammad, Razmirad; Abdollahi, Hadi

doi:10.1016/j.ijmst.2013.08.016

Cited by 13 publications

(2 citation statements)

References 14 publications

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“…The ANOVA results for Y 1 and Y 2 showed significant response surface models with high R 2 values (0.9291 and 0.9369, respectively), suggesting that more than 92.91% of the variance is attributable to the variables in COD and 93.69% to those in VS (Table 3). This result also indicates a high significance of the models, P = 0.0001 and 0.0059, respectively, and the third degree equation represents the model under the experimental domain (Alireza et al, 2013). Fig.…”

Section: Fitting the Second Order Polynomial Equation And Statisticalmentioning

confidence: 58%

Optimization of biostimulant for bioremediation of contaminated coastal sediment by response surface methodology (RSM) and evaluation of microbial diversity by pyrosequencing

Subha

Song

Woo

2015

Marine Pollution Bulletin

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Section: Fitting the Second Order Polynomial Equation And Statisticalmentioning

confidence: 58%

Optimization of biostimulant for bioremediation of contaminated coastal sediment by response surface methodology (RSM) and evaluation of microbial diversity by pyrosequencing

Subha

Song

Woo

2015

Marine Pollution Bulletin

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“…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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Benzene-contaminated groundwater remediation using calcium peroxide nanoparticles: synthesis and process optimization

Mosmeri¹,

Alaie²,

Shavandi³

et al. 2017

Environ Monit Assess

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Nano-size calcium peroxide (nCaO) is an appropriate oxygen source which can meet the needs of in situ chemical oxidation (ISCO) for contaminant remediation from groundwater. In the present study, an easy to handle procedure for synthesis of CaO nanoparticles has been investigated. Modeling and optimization of synthesis process was performed by application of response surface methodology (RSM) and central composite rotatable design (CCRD) method. Synthesized nanoparticles were characterized by XRD and FESEM techniques. The optimal synthesis conditions were found to be 5:1, 570 rpm and 10 °C for HO:CaSO ratio, mixing rate and reaction temperature, respectively. Predicted values showed to be in good agreement with experimental results (R values were 0.915 and 0.965 for CaO weight and nanoparticle size, respectively). To study the efficiency of synthesized nanoparticles for benzene removal from groundwater, batch experiments were applied in biotic and abiotic (chemical removal) conditions by 100, 200, 400, and 800 mg/L of nanoparticles within 70 days. Results indicated that application of 400 mg/L of CaO in biotic condition was able to remediate benzene completely from groundwater after 60 days. Furthermore, comparison of biotic and abiotic experiments showed a great potential of microbial stimulation using CaO nanoparticles in benzene remediation from groundwater.

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Application of response surface methodology and central composite rotatable design for modeling the influence of some operating variables of the lab scale thickener performance

Cited by 13 publications

References 14 publications

Optimization of biostimulant for bioremediation of contaminated coastal sediment by response surface methodology (RSM) and evaluation of microbial diversity by pyrosequencing

Optimization of biostimulant for bioremediation of contaminated coastal sediment by response surface methodology (RSM) and evaluation of microbial diversity by pyrosequencing

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

Benzene-contaminated groundwater remediation using calcium peroxide nanoparticles: synthesis and process optimization

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