E3S Web Conf.
Volume 68, 2018The 1st Sriwijaya International Conference on Environmental Issues 2018 (1st SRICOENV 2018)
|Number of page(s)||8|
|Section||Mitigation and Adaptation in Climate Change in Wetland|
|Published online||27 November 2018|
Biosorption Processof Synthetic Textile Waste-water using Bjerkandera Agustavia Response Surface Methodology (RSM)
1 Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
2 Department of Environmental Engineering, Faculty of Landscape Architecture and Environmental Technology, Universitas Trisakti, Jakarta, Indonesia
* Corresponding author: firstname.lastname@example.org
Textile industry generates large quantities of wastewater. Discharging effluent of textile industry without treatment is led to the degradation of the quality of receiving water bodies.A high color, high BOD/COD and salt (Total Dissolved Solids, TDS) load are founded in the textile wastewater. Several alternative of methods,including physico-chemical methods such as filtration, carbon activated, coagulation and chemical flocculation have been used to treat textile industry wastewater. Although these methods are effective, but they are expensive and result concentrated sludge that creates a secondary disposal problem. The passive uptake of organic and inorganic species including metals and dyes from aqueous solutions by the use of non-growing/living microbial mass or their derivatives is namely biosorption.The effects of pH, weight of biosorbent, contact time and size of biosorbent in biosorption process using Bjerkandera adusta in synthetic textile wastewater were investigated and optimized using response surface methodology (RSM). The optimum removal conditions were determined at pH 4, contact time 90 minutes, weight of biosorbent 3000 mg/L, and size of biosorbent 0.4 mm. Color removal of 53.55% was demonstrated, the experimental data and model predictions agreed well. In the optimization, R2 and 2correlation coefficients for the quadratic model was estimated quite satisfactorily as 0.988 and 0.977, respectively.
© The Authors, published by EDP Sciences, 2018
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