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Background
Industry has an important role in the development of any country and communities. Textile industry is one of my countries needed (1). The textile industry is classified into three parts: cellulose fibers, protein fibers and synthetic fibers. The type of dyes and chemicals used in the textile industry are depends on the fiber production (2). The textile wastewater has a high color, high BOD/COD and salt (TDS) load. The textile wastewater begotten from cotton dyeing industry is very polluted due to presence of reactive dyes which are not readily amenable to biological treatment (1,3). Thus, Without suitable treatment, discharge of wastewaters into water can Undesirable effects on aquatic environment by reducing light infiltration and photosynthesis; being hazardous and toxic to aquatic life (4,5). The textile wastewater must be properly treated before their discharge to the water resources and the environment (6).
There are several methods for the elimination of textile wastewaters, including the application of photo decomposition (7), electrolysis (8), adsorption (9), oxidation (10), biodegradation (11) and other processes.
Flotation is a physical approach in which the solid particles are transported to the surface layer from the liquid phase. The main preference of this approach over the sedimentation is that in the former method, fine particles with densities close to that of water and slow settlement can be withdrawn more efficiently and in shorter period of time (12). Dissolved air flotation (DAF) is one of the novel techniques for industrial wastes and waste water treatment (13). Flotation is performed by releasing air bubbles into the flotation tank, using pressure and then, by decreasing in the pressure as it encounters the atmospheric pressure (13,14). Coagulation and flocculation chemistry are important factors in DAF process. Particle mobilization for bubble particle attachment requires coagulation. This will result in the efficient removal of the particles (12,13). Employing a coagulants DAF process has been shown to improve textile wastewater removal efficiency (13). Poly-aluminum chloride (PAC) and organic polymers have been widely used in many countries, extensively (14,15). Some of the advantages of the PAC offer over simple salts are broader pH operating range, lower temperature sensitivity, smaller sludge production and better sludge dewater ability (13). It is widely used in dealing with municipal sewage and purifying industrial wastewater such as refinery, cutting oil/water and poultry slaughterhouse wastewater (14,16).
Aims of the study:
The purpose of this study was to determine the operational parameters, PAC concentration, saturation pressure, coagulation time and time flotation for treatment of textile wastewater, using a combined coagulation and dissolved air flotation processes.
 

Materials & Methods

Collection and preparation of samples
This experimental study was conducted on a laboratory scale reactor using a semi-batch mode. In this study, the effect of useful parameters such as Biological Oxygen Demand (BOD₅), Chemical Oxygen Demand(COD), NH₃–N, Total Suspended Solids (TSS), Total Kjeldahl Nitrogen (TKN) and pH were measured (by Standard Methods of Water and Wastewater) (ERRATA) (17).
The textile wastewater sample was provided from textile industries in Zahedan city of Iran. Then, the sample was transported to the laboratory in plastic containers stored in a refrigerator at 4 ◦C. The stock solution of the poly-aluminum which used for the experiments was 1000 mg/L. The experimental DAF system consisted of a flotation column, a compressor, an unpack saturator column and a pressure gauge as shown in Fig 1. Jar test apparatus (Phipps and Bird, JLT6) were performed coagulation, equipped with six beakers one-liter volume. The coagulation and DAF processes were performed in four steps: (i) the pH of the wastewater (4 L)was adjusted according to the experiment and the sample was added to the flotation cell, (ii) PAC was added to the cell according to the design of experiment (iii) the wastewater and PAC were rapidly mixed (380 rpm for 2 min) and slow mixed(30±2 rpm for 10min), (iv) water saturated with air was injected from the saturator into the flotation cell for 5s, (v) flotation was allowed to occur and samples were collected From the sampling point. After flocculation, appropriate quantities of saturated water were introduced, flotation was allowed to proceed and samples were collected for analysis. The pH was adjusted by a pH meter (model MIT65); HCl and NaOH were used to adjust pH in this study. The COD was readied by the dichromate method, colorimetric method at wavelength 600 nm (14, 18) with Hach spectrophotometer (HACH DR/5000), the TSS was determined by gravimetric standard method in 103 °C to 105 °C. TDS and EC were determined by EC meter (model HACH) and NH₃-N was tested by the Nessler’s reagent spectrophotometry.
Analysis of samples
The removal of the studied parameters was calculated according Eq1 (18,19):
R=           (1)
Where C_i (initial concentration) and C_f (final concentration)
Data analysis
All conducted analyses were performed triplicate in the present study. The calculated data are represented as mean values±standard deviation (SD). The statistical analysis of obtained data was conducted, using Excel.
Figure 1).image of the experimental DAF
 

Results

The characteristics of raw textile wastewater (COD, BOD₅, NH₃–N, TSS, TKN and pH) are as shown in Table1. Initial stage of the experiment was designed to determine optimum amount of PAC coagulant 90mg/l for textile wastewater in coagulation reaction. Figure 2 shows COD, BOD₅ and TSS highest removal efficiency in the coagulation process is 53.45%, 43.2% and 79.01% at 90 mg/l poly-aluminum chloride dose.

Figure 2) Effect of different doses of coagulant PAC on coagulation
 

Table1) Textile wastewater characterizations

Parameter	unit	minimum	maximum	average	The standard deviation
COD	mg/L	3940	5320	4954	349.4
BOD	mg/L	2200	3050	2851	218.66
TKN	mg/L	70	195	147	36.36
TP	mg/L	25	48	38	7.68
TSS	mg/L	320	465	398	40.2
PH	---	6.4	8.6	7.7	0.76