چكيده به لاتين
One of the most prominent stages in producing drinking water is the process of Coagulation/ flocculation in which colloidal particles that are the main cause of turbidity are removed. Recently, carbon-based nanomaterial due to more surface area and functionalized sites, are considered as a substitute for common chemical materials in water purification. One of these materials is graphene oxide (GO) which thanks to its unique properties, several researches on its capability of pollutant absorption have been done. Due to lack of consideration the coagulation characteristic of graphene oxide in previous studies, the major attempt of this investigation is exploring the coagulation characteristic of GO contributing in removing turbidity as well as optimization and modeling process. Therefore, at the first step, effects of pH, grapheme oxide dose, initial turbidity, mixing time, and settling time on removing turbidity from artificial turbid water samples building out of garden soil and tap water was tested by using jar test. According to the obtained results, efficiency of removing turbidity increased by amplification of grapheme oxide dose from 2.5 mg/L to 40 mg/L, and also of acidic pH (up to 7) but initial turbidity impact on efficiency of removing was affected by other significant factors such as pH and graphene oxide dose. Furthermore, increasing in rapid and slow mixing time did not make substantial difference on efficiency of removing. Moreover, major elimination of constructed flocs have been occurred in first minutes of time settling. At the second stage, coagulation ability of grapheme oxide and how effectiveness input parameters are, were investigated by using Response Surface Methodology (RSM) in Design expert. Based on the results, graphene oxide showed the best performance in removing turbidity of sample (more than 97% turbidity elimination) with initial turbidity of 162.5 NTU and pH of 3, in grapheme oxide dose of 16.25 mg/L. According to Response surface methodology, two parameters pH and grapheme oxide dosage were the most influential parameters in efficiency of turbidity elimination using grapheme oxide which was in the same line as introductory experiments. Besides results of jar tests, microscopic optical images, scanning electron microscope and also particle size distribution analysis illustrated appropriateness of built flocs and accomplishing coagulation process. Also, by consideration the results of zeta potential analysis, the main mechanism of coagulation using grapheme oxide was diagnosed as a sweep coagulation. In further studies, all samples from laboratory in order to develop data mining models (artificial neural network (ANN), Support vector regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS)) for estimation turbidity elimination using grapheme oxide were utilized. To estimate system response to graphene oxide dosage impacts, pH and initial turbidity, data mining models with those techniques were developed and those performance were compared with each other in terms of statistical metrics. Accord to results, ANN had higher accuracy (R2 = 0.9492) in comparison to other techniques. Although ANFIS had a lower accuracy rather than ANN, eluminated a better drawing of real world thanks to considering uncertainties. Furthermore, by utilizing partial mutal information (PMI), pH, graphene oxide dosage and turbidity have been respectively ordered as the most to the less effects on removing turbidity from water. This result was a proof for results of analysis from RSM and introductory tests.
Keywords: Coagulation and Flocculation, Colloid, Turbidity, Coagulant, Graphene
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