scholarly journals Optimization and Modelling of Turbidity Removal of Sewage using High-Gradient Magnetic Separation (HGMS) by Response Surface Methodology (RSM)

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Nur Sumaiyyah Supian ◽  
Johan Sohaili ◽  
Nur Farhan Zon
Keyword(s):  
Stainless Steel ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Magnetic Separation ◽  
Suspended Matter ◽  
Circulation Time ◽  
Small Range ◽  
Steel Wool ◽  
Turbidity Removal ◽  
Suggested Approach

Endless industrial development and growing society occasionally create an enormous volume of wastewater, which leads to some issues on wastewater treatment. Existing conventional screening processes have various limitations and drawbacks. Therefore, this study investigated the use of a combination of non-corrosive stainless steel wool and a permanent magnet to increase magnetic gradient, hence reducing suspended matter in sewage through turbidity test. An approach for optimizing the reduction of suspended matter through turbidity analysis was conducted using central composite design (CCD) under response surface methodology (RSM). Three critical independent variables, such as magnet strength, circulation time, and steel wool, and turbidity removal as the response, were further studied to analyze their interaction effects. As a result, an optimal value of turbidity removal was found at 90.3% under the specified optimum conditions of magnet strength of 245 mT, 116 g of non-corrosive stainless steel wool, and 16 h of circulation time. Statistical analysis had shown that the magnet strength, circulation time, and steel wool significantly affected the turbidity removal performance. Furthermore, design of experiment was significantly verified by a small range of error between predicted and actual data. Consequently, a higher gradient of magnetic separation was proven to effectively remove suspended matter using inexpensive non-corrosive stainless steel wool without using magnetic adsorbent. Thus, the suggested approach was found to be cost-effective and environmentally friendly for sewage treatment.

Optimization of combined microwave pretreatment–magnetic separation parameters of ilmenite using response surface methodology

2012 ◽  
Vol 232 ◽  
pp. 58-63 ◽  
Author(s):  
Guo Chen ◽  
Jin Chen ◽  
Jun Li ◽  
Shenghui Guo ◽  
C. Srinivasakannan ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Magnetic Separation ◽  
Microwave Pretreatment ◽  
Separation Parameters

Application of response surface methodology for modeling and optimization of trichloroacetic acid and turbidity removal using potassium ferrate(VI)

2016 ◽  
Vol 57 (52) ◽  
pp. 25317-25328 ◽  
Author(s):  
Hassan Aslani ◽  
Ramin Nabizadeh ◽  
Simin Nasseri ◽  
Alireza Mesdaghinia ◽  
Mahmood Alimohammadi ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Trichloroacetic Acid ◽  
Potassium Ferrate ◽  
Turbidity Removal ◽  
Modeling And Optimization

Modeling assessment of recovering iron from red mud by direct reduction: magnetic separation based on response surface methodology

2018 ◽  
Vol 25 (5) ◽  
pp. 497-505 ◽  
Author(s):  
Ran Wang ◽  
Zheng-gen Liu ◽  
Man-sheng Chu ◽  
Hong-tao Wang ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Magnetic Separation ◽  
Red Mud ◽  
Direct Reduction

Modelling and multi-response optimization of wire electric discharge machining parameters using response surface methodology and grey–fuzzy algorithm

2015 ◽  
Vol 231 (10) ◽  
pp. 1760-1774 ◽  
Author(s):  
Bikash Choudhuri ◽  
Ruma Sen ◽  
Subrata Kumar Ghosh ◽  
Subhash Chandra Saha
Keyword(s):  
Stainless Steel ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Wire Electric Discharge Machining ◽  
Grey Relational ◽  
Stainless Steel 316

Wire electric discharge machining is a non-conventional machining wherein the quality and cost of machining are influenced by the process parameters. This investigation focuses on finding the optimal level of process parameters, which is for better surface finish, material removal rate and lower wire consumption for machining stainless steel-316 using the grey–fuzzy algorithm. Grey relational technique is applied to find the grey coefficient of each performance, and fuzzy evaluates the multiple performance characteristics index according to the grey relational coefficient of each response. Response surface methodology and the analysis of variance were used for modelling and analysis of responses to predict and find the influence of machining parameters and their proportion of contribution on the individual and overall responses. The measured values from confirmation experiments were compared with the predicted values, which indicate that the proposed models can be effectively used to predict the responses in the wire electrical discharge machining of AISI stainless steel-316. It is found that servo gap set voltage is the most influential factor for this particular steel followed by pulse off time, pulse on time and wire feed rate.

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