e-ISSN 2231-8526
ISSN 0128-7680

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

Nur Sumaiyyah Supian, Johan Sohaili and Nur Farhan Zon

Pertanika Journal of Science & Technology, Volume 28, Issue 4, October 2020


Keywords: Magnetism, optimization, sewage, steel wool, turbidity

Published on: 21 October 2020

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.

ISSN 0128-7680

e-ISSN 2231-8526

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