Abstract

In the present research, the coagulation-flocculation (CF) process was used to eliminate highly turbid water in disaster conditions. To better understand the efficiency of the system, the impact of various numerical factors namely; initial turbidity (10–350 NTU), pH (5–9), coagulant dosage (50–250 mg/L), rapid mixing (120–280 rpm), slow mixing (30–50 rpm), and sedimentation time (10–50 min) were optimized through the central composite design (CCD) under response surface methodology (RSM). Based on analysis of variance (ANOVA), the quadratic model was more suitable for the dataset with R2 = 0.85 for removing turbidity. Moreover, the results of the present study revealed that the highest turbidity removal (99.14%) was observed at pH (9), alum dosage (50 mg/L), initial turbidity (350 NTU), rapid mixing (280 rpm), slow mixing (50 rpm), and sedimentation time (50 min). Furthermore, the residual turbidity at the maximum efficiency of the system was 3 NTU.

HIGHLIGHTS

  • This study has been performed and designed to investigate the efficiency of commercial aluminum sulfate as an efficient coagulant for the removal of turbidity with high concentrations to simulate crises in water resources.

  • Also, the effect of main variables; pH, alum dosage, rapid and slow mixing, and sedimentation time on the process has been optimized and an empirical model has been developed.

INTRODUCTION

Over the past decades, disasters, especially floods and earthquakes, have had adverse effects on water quality such as increasing suspended solids (SS) and colloids more than the standard range (Ang et al. 2016). Among the various water quality indicators, turbidity is one of the most significant indicators for detecting freshwater characteristics (Antov et al. 2018). Typically, the drinking water-based on turbidity value can be divided into four categories as follows: low turbidity (less than 50 NTU), medium turbidity (50–100 NTU), high turbidity (100–200 NTU), and very high turbidity (more than 300 NTU) (Altaher 2012). Excessive turbidity in drinking water can also be associated with the presence of organic materials, dye, and microorganisms such as viruses, and certain bacteria, therefore, it increases the health and environmental concerns (Aboubaraka et al. 2017; Al-Husseini et al. 2018).

Based on the above reasons, the World Health Organization (WHO) proposed that acceptable levels of turbidity in drinking water must be less than 5 NTU (Al-Husseini et al. 2018). Therefore, to achieve WHO standards various chemical-physical technologies such as sequencing batch reactor (SBR) (Azimi et al. 2019), ceramic membranes (Park et al. 2020), ozonation (Hajiali & Pirumyan 2014), and coagulation (Zhang et al. 2018) have been used for removing turbidity from water. Among them, the coagulation–flocculation (CF) process is the most reliable technology to reduce any pollutants (turbidity, particulates, and organic matters) from drinking water (Zhao et al. 2012).

Previous studies confirmed that the CF process has been extensively applied in water treatment plants (WTPs) because of its superior properties such as simplicity, cost-effectiveness, high efficiency, non-toxic method, and low energy consumption (Lim et al. 2018; Ozairi et al. 2020). Typically, the CF process has been performed by using various coagulants such as iron-aluminum salts, and long-chain polymers for the removal of turbidity from raw water (Baruth 2005). Previous works indicated that aluminum salts (alum) with a chemical formula (Al2(SO4)3.18H2O) has a better potential for the removal of turbidity than ferric salts. However, alum cost is higher than ferric chloride (Gobena et al. 2018; Kumari & Gupta 2020). Studies have shown that there is a direct correlation between the use of alum and neuropathological diseases such as Alzheimer's (Huang et al. 2015). Therefore, it is necessary to optimize the main factors such as coagulant dosage, mixing speed, effluent pH, temperature, and time, which have a direct effect on the CF process (Corral Bobadilla et al. 2019).

In recent years, several methods such as an artificial neural network (ANN) (Singh & Gupta 2012), adaptive neuro-fuzzy inference system (ANFIS) (Kim & Parnichkun 2017), multi-layer perceptron (MLP) (Gagnon et al. 1997), generalized regression neural network (GRNN) (Specht 1991), and response surface methodology (RSM) (Corral Bobadilla et al. 2019) have been applied for modeling and optimization of the CF process. Among the aforementioned methods, RSM as a statistical method has been widely used for analyzing, modeling, and optimizing the effect of different variables and their responses on the system (Corral Bobadilla et al. 2019). Furthermore, it can be used in various systems to obtain maximum performance with the ability to decrease the number of examinations (Singh & Kumar 2020).

Muyibi & Alfugara (2003) used alum and Moringa oleifera seed as coagulants for the removal of turbidity from water at different levels; low turbidity (21.5–49.3 NTU), moderate turbidity (51.8–114 NTU) and high turbidity (163–494 NTU). Their results showed that the process can achieve the minimum residuals of turbidity, 1.4, 1.9, and 0.9 NTU, using Moringa oleifera, alum, and alum with Moringa oleifera, respectively (Muyibi & Alfugara 2003). Another study by Baghvand et al. (2010) reported that the highest turbidity removal by using alum and ferric chloride as coagulant was 82.9–99.0 and 92.9–99.4%, respectively (Baghvand et al. 2010). Agbovi & Wilson (2019) examined and optimized the removal efficiency of turbidity using an amphoteric chitosan-based flocculant–ferric chloride by RSM. They found that the turbidity removal under optimum conditions was 96.7%. Furthermore, the results showed that the process is highly dependent on the pH, FeCl3 dosage, and the flocculant dosage (Agbovi & Wilson 2019). Usefi & Asadi-Ghalhari (2019) used the coagulation–flocculation process in the elimination of turbidity using rice starch and the system was optimized using the central composite design (CCD) approach. The results demonstrated that 98.4% of turbidity was removed at the optimal point. Moreover, they showed that among the four independent variables (pH, settling time, rice starch dosage, and slow mixing), pH had the most significant effect on the turbidity removal (Usefi & Asadi-Ghalhari 2019).

Therefore, due to the importance of optimizing the coagulant factors, herein, the influences of operational parameters (coagulant dose and pH, rapid mixing, slow mixing, and sedimentation time) on the removal of turbidity were investigated using the CCD-RSM method. Furthermore, this is the first study that has completely described the role of rapid mixing; slow mixing on the coagulation-flocculation process.

MATERIALS AND METHODS

Chemicals

Commercial aluminum sulfate (Al2 (SO4)3·14H2O; 17%), with a molecular weight (MW) of 342.15 g/mol was purchased from Goglagh Company, Iran. All reagents with high analytical grade were purchased from Merck Company, Germany.

Turbid water preparation

To prepare turbid water in desired turbidities, clay soil (without any modification) from the suburb of Kermanshah city was collected and dried at 150 °C for 150 min using an oven (Memmert 854, Germany). The synthetic turbid water was prepared daily using 10 g of dried clay soil in 2 L of tap water and mixed at 30 rpm for 1 hour to complete hydration of the particles. Consequently, the desired range of turbid water (10–350 NTU) was prepared using the stock solution.

Coagulation–flocculation experiment

The CF process was performed using a Jar test apparatus (AQUALYTIC, Germany) with six beakers (1,000 mL working volume). The jar test was conducted at three stages: coagulation (rapid mixing; 120–280 rpm for 1 min), flocculation (slow mixing; 30–50 rpm for 20 min), and sedimentation at different settling times (10–50 min). At the beginning of the process, all beakers were filled with turbid water with initial turbidity of 10–350 NTU and then alum at desired dosages (50–250 mg/L) was added into the solutions. The solution pH was adjusted to the desired values of 5–9 using sodium hydroxide (NaOH) and sulfuric acid (H2SO4) using a pH meter (WTW, Germany). The rapid and slow mixing was conducted subsequently based on Table 1. After the end of the process, sampling was carried out from 2 to 3 cm under the surface of the solution. The residual of turbidity was analyzed using a turbidimeter (2100 p, HACH Company, USA). The efficiency of the system was calculated according to Equation (1) (Lim et al. 2018):
formula
(1)
Table 1

The level of independent variables

VariablesUnitSymbolsCoded levels
LowCentreHigh
Initial turbidity NTU 10 180 350 
Coagulant dosage mg/l 50 150 250 
Rapid mixing (RM) rpm 120 200 280 
Slow mixing (SM) rpm 30 40 50 
pH – 
Settling time (ST) min 10 30 50 
VariablesUnitSymbolsCoded levels
LowCentreHigh
Initial turbidity NTU 10 180 350 
Coagulant dosage mg/l 50 150 250 
Rapid mixing (RM) rpm 120 200 280 
Slow mixing (SM) rpm 30 40 50 
pH – 
Settling time (ST) min 10 30 50 

Experimental design and data analysis

The Design-Expert software (version 11.0.0.1) was used for optimization and data analysis of the experiments. The standard response surface methodology design known as central composite design was used to design experiments, model the data, and finally predict the responses (Mousavi et al. 2017; Aydar 2018). Moreover, RSM can optimize the process parameters with a minimum number of tests (Aydar 2018; Nayeri et al. 2019). ANOVA (analysis of variance) was used for data analysis and to develop a mathematical model (Momeni et al. 2018). Besides, the probability value (P-value) at a 95% confidence interval was used to evaluate the significance of model terms (Corral Bobadilla et al. 2019; Mousavi & Ibrahim 2016). The CCD is based on independent parameters such as: A: initial turbidity, B: alum dosage, C: rapid mixing, D: slow mixing, E: pH, and F: settling time at three levels (low, center points, and high level) (Table 1). Furthermore, the results of the experiments based on 86 runs are summarized in Table 2. The residual value of turbidity (Y1) based on NTU and removal efficiency served as output responses (Equation (2)). The predicting of optimal conditions was carried out based on the following developed model (Equation (2)) (Mousavi et al. 2019; Shahbazi et al. 2020):
formula
(2)
Table 2

Experimental design for turbidity removal

Run no.Parameters
Final turbidity (NTU)Removal %Run no.Parameters
Final turbidity (NTU)Removal %
A (NTU)B (mg/L)C (rpm)D (rpm)EF (min)A (NTU)B (mg/L)C (rpm)D (rpm)EF (min)
180 150 200 40 30 75.5 57.1 44 350 50 120 30 50 98 
350 50 120 50 10 7.3 97.91 45 350 250 120 30 50 99.14 
180 150 200 40 30 50 71.59 46 10 250 280 50 50 5.8 47.27 
180 150 200 40 30 46 74.44 47 10 50 280 30 10 3.4 66 
350 250 280 30 10 98.28 48 10 50 280 30 10 9.9 10 
350 250 120 50 10 69.4 8.17 49 180 150 200 40 30 64.4 63.4 
350 250 120 50 50 27 92.28 50 350 50 280 30 10 97.71 
180 150 200 35 30 7.37 95.94 51 10 250 280 30 50 50 
350 50 120 50 50 20 94 52 350 250 280 50 10 33.1 90.54 
10 10 250 120 30 50 3.5 98.11 53 180 150 200 45 30 68 61.33 
11 350 50 280 50 50 99.14 54 350 250 280 50 10 57.5 83.57 
12 180 150 200 40 30 55 69.44 55 350 50 280 50 10 64.5 81.57 
13 10 250 120 50 10 25.4 –154 56 180 100 200 40 30 61.8 64.88 
14 10 50 120 30 10 50 57 350 50 280 30 50 20.6 94.11 
15 350 250 280 30 50 3.88 98.89 58 180 150 200 40 30 60 66.66 
16 10 50 280 50 50 4.1 62.72 59 180 150 200 40 30 16.1 95.01 
17 180 150 200 40 20 21.7 87.7 60 350 250 280 50 50 23 93.42 
18 350 50 120 30 10 34 90 61 180 200 200 40 30 50 72.22 
19 350 250 280 30 10 71 79.71 62 10 250 120 30 10 18.2 –82 
20 350 50 120 50 10 62 80.28 63 265 150 200 40 30 18 93.2 
21 95 150 200 40 30 21 78.89 64 10 250 280 50 10 22.1 –121 
22 10 250 120 50 50 16.6 –66 65 10 50 120 30 50 4.54 54.04 
23 180 150 200 40 30 65.4 62.8 66 350 250 120 30 10 12 96 
24 10 250 120 50 50 5.18 52.9 67 10 50 280 30 50 6.37 36.3 
25 10 250 280 30 50 2.46 77.63 68 350 50 120 50 50 25.6 92.68 
26 10 250 120 30 10 21.9 –119 69 180 150 160 40 30 24 86.66 
27 10 50 120 50 50 7.25 34.18 70 10 50 280 30 50 2.46 77.63 
28 180 150 200 40 40 10.1 94.32 71 180 150 200 40 30 64 64.44 
29 10 50 120 50 10 17.19 –71.9 72 350 250 120 30 50 14 96 
30 350 50 280 30 50 3.2 99.08 73 10 250 280 30 10 11.4 –14 
31 350 250 280 30 50 17.7 94.94 74 350 250 120 50 50 6.1 98.25 
32 10 50 120 50 10 2.25 77.27 75 10 50 120 30 10 60 
33 350 50 280 30 10 47 89.57 76 180 150 240 40 30 6.68 96.28 
34 10 50 280 50 10 72.72 77 10 250 280 30 10 13 –30 
35 180 150 200 40 30 61.8 64.88 78 10 50 120 30 50 4.28 56.68 
36 10 50 280 50 10 5.3 51.8 79 10 50 280 50 50 8.3 24.54 
37 350 50 280 50 50 22.1 93.68 80 180 150 200 40 30 58 67.22 
38 10 250 120 50 10 21 –101 81 10 50 120 50 50 2.3 79.09 
39 180 150 200 40 30 13.3 92.52 82 350 250 120 50 10 26.2 95.51 
40 10 250 280 50 50 20 –100 83 350 50 280 50 10 7.1 97.97 
41 350 50 120 30 50 13 96.2 84 10 250 280 50 10 19.7 –97 
42 10 250 120 30 50 17.7 –77 85 350 250 120 30 10 50.7 85.51 
43 350 50 120 30 10 7.1 97 86 350 250 280 50 50 7.6 97.88 
Run no.Parameters
Final turbidity (NTU)Removal %Run no.Parameters
Final turbidity (NTU)Removal %
A (NTU)B (mg/L)C (rpm)D (rpm)EF (min)A (NTU)B (mg/L)C (rpm)D (rpm)EF (min)
180 150 200 40 30 75.5 57.1 44 350 50 120 30 50 98 
350 50 120 50 10 7.3 97.91 45 350 250 120 30 50 99.14 
180 150 200 40 30 50 71.59 46 10 250 280 50 50 5.8 47.27 
180 150 200 40 30 46 74.44 47 10 50 280 30 10 3.4 66 
350 250 280 30 10 98.28 48 10 50 280 30 10 9.9 10 
350 250 120 50 10 69.4 8.17 49 180 150 200 40 30 64.4 63.4 
350 250 120 50 50 27 92.28 50 350 50 280 30 10 97.71 
180 150 200 35 30 7.37 95.94 51 10 250 280 30 50 50 
350 50 120 50 50 20 94 52 350 250 280 50 10 33.1 90.54 
10 10 250 120 30 50 3.5 98.11 53 180 150 200 45 30 68 61.33 
11 350 50 280 50 50 99.14 54 350 250 280 50 10 57.5 83.57 
12 180 150 200 40 30 55 69.44 55 350 50 280 50 10 64.5 81.57 
13 10 250 120 50 10 25.4 –154 56 180 100 200 40 30 61.8 64.88 
14 10 50 120 30 10 50 57 350 50 280 30 50 20.6 94.11 
15 350 250 280 30 50 3.88 98.89 58 180 150 200 40 30 60 66.66 
16 10 50 280 50 50 4.1 62.72 59 180 150 200 40 30 16.1 95.01 
17 180 150 200 40 20 21.7 87.7 60 350 250 280 50 50 23 93.42 
18 350 50 120 30 10 34 90 61 180 200 200 40 30 50 72.22 
19 350 250 280 30 10 71 79.71 62 10 250 120 30 10 18.2 –82 
20 350 50 120 50 10 62 80.28 63 265 150 200 40 30 18 93.2 
21 95 150 200 40 30 21 78.89 64 10 250 280 50 10 22.1 –121 
22 10 250 120 50 50 16.6 –66 65 10 50 120 30 50 4.54 54.04 
23 180 150 200 40 30 65.4 62.8 66 350 250 120 30 10 12 96 
24 10 250 120 50 50 5.18 52.9 67 10 50 280 30 50 6.37 36.3 
25 10 250 280 30 50 2.46 77.63 68 350 50 120 50 50 25.6 92.68 
26 10 250 120 30 10 21.9 –119 69 180 150 160 40 30 24 86.66 
27 10 50 120 50 50 7.25 34.18 70 10 50 280 30 50 2.46 77.63 
28 180 150 200 40 40 10.1 94.32 71 180 150 200 40 30 64 64.44 
29 10 50 120 50 10 17.19 –71.9 72 350 250 120 30 50 14 96 
30 350 50 280 30 50 3.2 99.08 73 10 250 280 30 10 11.4 –14 
31 350 250 280 30 50 17.7 94.94 74 350 250 120 50 50 6.1 98.25 
32 10 50 120 50 10 2.25 77.27 75 10 50 120 30 10 60 
33 350 50 280 30 10 47 89.57 76 180 150 240 40 30 6.68 96.28 
34 10 50 280 50 10 72.72 77 10 250 280 30 10 13 –30 
35 180 150 200 40 30 61.8 64.88 78 10 50 120 30 50 4.28 56.68 
36 10 50 280 50 10 5.3 51.8 79 10 50 280 50 50 8.3 24.54 
37 350 50 280 50 50 22.1 93.68 80 180 150 200 40 30 58 67.22 
38 10 250 120 50 10 21 –101 81 10 50 120 50 50 2.3 79.09 
39 180 150 200 40 30 13.3 92.52 82 350 250 120 50 10 26.2 95.51 
40 10 250 280 50 50 20 –100 83 350 50 280 50 10 7.1 97.97 
41 350 50 120 30 50 13 96.2 84 10 250 280 50 10 19.7 –97 
42 10 250 120 30 50 17.7 –77 85 350 250 120 30 10 50.7 85.51 
43 350 50 120 30 10 7.1 97 86 350 250 280 50 50 7.6 97.88 

RESULTS AND DISCUSSION

Data analysis and modeling

The results of the ANOVA are shown in Table 3. According to the table, it is clear that terms such as A, B, D, E, F, AB, AE, AF, BF are significant (P-value < 0.05). The quality of model fitness (Equation (3)) was investigated by the R2 coefficient (Noordin et al. 2004; Almasi et al. 2017), in which R2 and adjusted R2 were 0.8547 and 0.7871, respectively. The high adequate precision (greater than 4) and low coefficient of variation are attributed to the high accuracy and reliability of the proposed models (Zangeneh et al. 2016; Nadarajan et al. 2018). Herein, adequate precision and CV values were 17.20 and 52.41, respectively. Based on the results, the quadratic model was more suitable for the dataset. Furthermore, the F-value of the model (12.64) confirmed the significance of the model. Based on Equation (3), the initial turbidity, settling time, pH, coagulant dosage, and rapid mixing had a positive and significant effect on turbidity removal.

Table 3

Results of ANOVA

SourceSum of squaresdfMean squareF-valueP-value
Model 2.794 × 105 27 10,347.62 12.64 <0.0001 
1.224 × 105 1.224 × 105 149.45 <0.0001 
33,250.73 33,250.73 40.61 <0.0001 
2,509.85 2,509.85 3.07 0.0853 
6,595.31 6,595.31 8.06 0.0062 
16,944.07 16,944.07 20.70 <0.0001 
18,108.18 18,108.18 22.12 <0.0001 
AB 25,767.47 25,767.47 31.47 <0.0001 
AC 968.30 968.30 1.18 0.2813 
AD 2,635.28 2,635.28 3.22 0.0780 
AE 6,681.43 6,681.43 8.16 0.0059 
AF 8,625.30 8,625.30 10.53 0.0019 
BC 1,031.86 1,031.86 1.26 0.2662 
BD 2,861.18 2,861.18 3.49 0.0666 
BE 685.13 685.13 0.8368 0.3641 
BF 9,786.65 9,786.65 11.95 0.0010 
CD 50.41 50.41 0.0616 0.8049 
CE 895.21 895.21 1.09 0.3001 
CF 1,407.94 1,407.94 1.72 0.1949 
DE 2,791.27 2,791.27 3.41 0.0699 
DF 253.92 253.92 0.3101 0.5797 
EF 567.63 567.63 0.6933 0.4085 
A2 7.91 7.91 0.0097 0.9220 
B2 589.86 589.86 0.7205 0.3995 
C2 125.79 125.79 0.1536 0.6965 
D2 75.09 75.09 0.0917 0.7631 
E2 9.64 9.64 0.0118 0.9140 
F2 110.32 110.32 0.1347 0.7149 
Residual 47,486.60 58 818.73   
Pure error 844.86 93.87   
Cor total 3.269 × 105 85    
SourceSum of squaresdfMean squareF-valueP-value
Model 2.794 × 105 27 10,347.62 12.64 <0.0001 
1.224 × 105 1.224 × 105 149.45 <0.0001 
33,250.73 33,250.73 40.61 <0.0001 
2,509.85 2,509.85 3.07 0.0853 
6,595.31 6,595.31 8.06 0.0062 
16,944.07 16,944.07 20.70 <0.0001 
18,108.18 18,108.18 22.12 <0.0001 
AB 25,767.47 25,767.47 31.47 <0.0001 
AC 968.30 968.30 1.18 0.2813 
AD 2,635.28 2,635.28 3.22 0.0780 
AE 6,681.43 6,681.43 8.16 0.0059 
AF 8,625.30 8,625.30 10.53 0.0019 
BC 1,031.86 1,031.86 1.26 0.2662 
BD 2,861.18 2,861.18 3.49 0.0666 
BE 685.13 685.13 0.8368 0.3641 
BF 9,786.65 9,786.65 11.95 0.0010 
CD 50.41 50.41 0.0616 0.8049 
CE 895.21 895.21 1.09 0.3001 
CF 1,407.94 1,407.94 1.72 0.1949 
DE 2,791.27 2,791.27 3.41 0.0699 
DF 253.92 253.92 0.3101 0.5797 
EF 567.63 567.63 0.6933 0.4085 
A2 7.91 7.91 0.0097 0.9220 
B2 589.86 589.86 0.7205 0.3995 
C2 125.79 125.79 0.1536 0.6965 
D2 75.09 75.09 0.0917 0.7631 
E2 9.64 9.64 0.0118 0.9140 
F2 110.32 110.32 0.1347 0.7149 
Residual 47,486.60 58 818.73   
Pure error 844.86 93.87   
Cor total 3.269 × 105 85    
Figure 1(a) illustrates that the predicted values of the model response are related to the observed values (real). Data points are fairly similar to each other and repetitive action is distributed. According to the results, this plot indicates that there is an acceptable correlation between the data obtained and the real evidence. The variation between the expected and the actual (residual) result is used to determine the accuracy (Gasemloo et al. 2019). Based on the aforementioned reasons, Figure 1(b) shows that the residuals are distributed normally.
formula
(3)
Figure 1

Predicted vs. actual values plot (a) and normal plot distributions of the residuals (b) for the removal of turbidity.

Figure 1

Predicted vs. actual values plot (a) and normal plot distributions of the residuals (b) for the removal of turbidity.

EFFECT OF THE MAIN VARIABLES

Effect of initial turbidity and settling time

The experiments were performed to better understand the impact of varied initial turbidity (10–350 NTU) and settling time (10–50 min) on the turbidity removal. The simultaneous effect of the initial turbidity and settling time on the turbidity removal is shown in Figure 2. As can be observed, when the initial turbidity increased from 10 to 350 NTU the removal efficiency of the system increased by more than 99%. On the other hand, minimum residual turbidity was attained at the initial turbidity of 350 NTU. The lower elimination of turbidity at low initial turbidity (10 NTU) may be due to the particle size reduction, which creates smaller flocs with a lower tendency for settling (Camacho et al. 2017). Furthermore, Figure 2 shows that the maximum efficiency of 99.14% was achieved at a settling time of 50 min so that when the settling time increased to 50 min, the minimum residual of turbidity (3 NTU) was attained.

Figure 2

(a) 3D surface and (b) 2D contour plots showing the simultaneous effect of turbidity and settling time on the removal of turbidity: alum dosage (150 mg/L), rapid mixing (200 rpm), slow mixing (40 rpm) and pH = 7.

Figure 2

(a) 3D surface and (b) 2D contour plots showing the simultaneous effect of turbidity and settling time on the removal of turbidity: alum dosage (150 mg/L), rapid mixing (200 rpm), slow mixing (40 rpm) and pH = 7.

Senthil Kumar et al. (2016) reported that the Moringa oleifera seeds could eliminate turbidity from underground water. The results completely demonstrated that by increasing the initial turbidity from 50 to 135 NTU, the removal efficiency increased from 54.67 to 74.28%. It indicates that at higher turbidity concentrations more interaction between coagulants and colloidal particles takes place (Senthil Kumar et al. 2016). Ramavandi (2014) used the Plantago ovata as a coagulant to remove turbidity, in which the turbidity varied from 50 to 300 NTU. The study showed that at the first stage of the process with an increase in initial turbidity from 50 to 250 NTU, the turbidity removal efficiency decreased from 99 to 95%, further increase in initial turbidity from 250 to 300 NTU was the cause of increase in the removal efficiency of the process (Ramavandi 2014). Daryabeigi Zand & Hoveidi (2015) compared the efficiency of two commercial coagulants (aluminum sulfate and poly-aluminum chloride) under similar conditions (dosage of 10–20 mg/L, pH 4–8) for treatment of high turbidity (10–1,000 NTU). The results confirmed that initial turbidity had a positive and significant effect on turbidity removal so that when turbidity increased to 500 and 1,000 NTU, the turbidity removal efficiency also remained high (Daryabeigi Zand & Hoveidi 2015).

Altaher et al. (2016) represented the turbidity removal of alum with a coagulant aid. The results showed that with increasing settling time, the amount of residual turbidity decreased, indicating that settling time plays a very important role in the CF process (Altaher et al. 2016). Similar results by Sasikala & Muthuraman (2017), in which the natural coagulants were used to remove turbidity from surface water, explicitly declared that by increasing settling time in the coagulation process, the turbidity removal efficiency increased (Sasikala & Muthuraman 2017). Also, Mohammed & Shakir (2018) reported that the removal of residual turbidity increased when settling time increased to 20 min.

Effect of pH and coagulant dosage

Generally, one of the most challenging parameters in the CF processes is pH, and the only reliable way to determine the appropriate range of this parameter is by performing laboratory-scale tests (Orooji et al. 2016). On the other hand, changing the pH value influences the substance hydrolysis load, and therefore pH optimization is very important to achieve suitable efficiency in CF processes (Al-Husseini et al. 2018). In this section, the simultaneous effects of the desired pH (5–9) and coagulant dosage (50–250 mg/L) are discussed. Based on Figure 3, it can be observed that the increase in the pH from 5 to 9 could increase the turbidity removal to 99.14%. In this study, we used alum as a coagulant, and the best removal efficiency occurred at pH 9. It is better to state that the pH range in the alum is between 5.5 and 8.5 (Altaher 2012).

Figure 3

(a) 3D surface and (b) 2D contour plots showing the simultaneous effect of coagulant dosage and pH on the removal of turbidity at rapid mixing of 200 rpm, slow mixing at 40 rpm and settling time of 30 min.

Figure 3

(a) 3D surface and (b) 2D contour plots showing the simultaneous effect of coagulant dosage and pH on the removal of turbidity at rapid mixing of 200 rpm, slow mixing at 40 rpm and settling time of 30 min.

The coagulant dosage can be deemed as one of the operational parameters for determining the optimal conditions of the process (Bazrafshan et al. 2015). Moreover, it is necessary to adjust the coagulant dosage because increasing the coagulant dosage may lead to an increase in operating costs, sludge production, shorten filter life, and decrease alkalinity (Ødegaard et al. 2010). According to Figure 3, it is clear that when the coagulant dosage was boosted to 250 mg/L, the turbidity removal declined, and the best condition was observed at a low coagulant dosage (50 mg/L). Several studies have shown the effect of pH and coagulant dosage on the CF process. Mohammadi-Moghaddam et al. (2015) studied the feasibility of polyaluminum ferric chloride for the treatment of highly turbid water (250 and 500 NTU) and pH value varied from 5 to 11. Their results indicated that increasing pH (more than 9) could result in decreasing the efficiency of the system, therefore, they chose a pH range of 7–8.5 to obtain the minimum residual turbidities (≤0.6 NTU) (Mohammadi-Moghaddam et al. 2015). Hussain et al. (2019) applied pine cone extract as a natural coagulant for the treatment of turbid water, and the pH value was varied from 2 to 12. Their results showed that by increasing the pH from 2 to 7, the removal efficiency of the process decreased from 75% to almost 45%, but with a further increase in pH from 7 to 12, the removal efficiency increased significantly (Hussain et al. 2019).

Huang et al. (2016) investigated the turbidity removal using a titanium salt family as a coagulant. The results showed that by increasing the coagulant dosage, the efficiency of turbidity removal increased (Huang et al. 2016). Chen et al. (2015) revealed that when the dosage of coagulant increased, the turbidity removal efficiency was also increased (Chen et al. 2015). Chekli et al. (2017) used different coagulants for the removal of turbidity and their results demonstrated that by increasing the dosage of coagulants, the removal efficiency of the CF process also increased to 95% (Chekli et al. 2017). Dehkordi et al. (2017) evaluated the effectiveness of three coagulants including polyaluminum chloride, alum, and ferric chloride, with a coagulant aid to remove turbidity in slopes ranging from 50 to over 20,000 NTUs. The results demonstrated that the removal efficiency of the process increased as coagulant dosage increased (Dehkordi et al. 2017).

Effect of mixing

Rapid mixing is an important parameter that has been investigated in CF processes in which chemical coagulants are combined with freshwater to promote particle destabilization (Ramphal & Sibiya 2014). It is better to mention that rapid mixing in a shorter time increases the remaining turbidity and creates larger flocs (BinAhmed et al. 2015). On the other hand, a high value of G, which is called the rapid mixing phase in the CF processes, usually facilitates the suitable dispersion of the added coagulant to the suspension and also improves contact between the particles, the soluble materials, and the coagulant molecules (Sheng et al. 2006).

The primary purpose of mixing is to keep particles in the suspended state, additionally, slow mixing can provide a velocity gradient for collisions between particles larger than 1 μm (Zhang et al. 2013). Furthermore, the slow mixing speed must be sufficient to maintain suspending particles without floc breakage (Rossini et al. 1999). Based on the obtained results (Figure 4), it was concluded that when rapid mixing increased to 280 rpm, the efficiency of turbidity removal improved, and maximum efficiency was 99.14%. Besides, as observed, by increasing the slow mixing from 30 to 50 rpm, the efficiency of turbidity removal increased slightly. Rossini et al. (1999) investigated the effect of the rapid mixing parameter in the CF process, and the results showed that the high value of the velocity gradient in the rapid mixing could result in creating lower residual turbidity (Rossini et al. 1999). Kan et al. (2002) tested the effects of varied rapid mixing intensity (25, 80, 200, 350, and 600 s–1) by PACl. The results confirmed that when rapid mixing intensity increased, the turbidity removal decreased (Kan et al. 2002). The results of a study by Zhang et al. (2013) verified that when the slow-mixing duration is within a certain range, such as t < 15 min at G = 15 or 38 s−1, the residual turbidity declined with slow-mixing duration. However, when increasing the slow-mixing duration such as t > 15 min at G = 15 or 38 s−1, the residual turbidity increases, even when the G value is as low as 4 s–1 (Zhang et al. 2013).

Figure 4

2D contour plot showing the simultaneous effect of rapid mixing and slow mixing on the removal of turbidity (initial turbidity of 180 NTU, alum dosage of 150 mg/L, pH of 7, and settling time of 30 min).

Figure 4

2D contour plot showing the simultaneous effect of rapid mixing and slow mixing on the removal of turbidity (initial turbidity of 180 NTU, alum dosage of 150 mg/L, pH of 7, and settling time of 30 min).

Optimization

The empirical results were optimized by Design-Expert software to produce an overlay plot. The amount of residual turbidity and alum dosage on the turbidity removal as responses were optimized (Figure 5). As can be seen, the optimal condition of the experiments was obtained under rapid mixing of 191 rpm, slow mixing of 43 rpm, pH 5.5, and settling time of 36 min, and high removal efficiency was between 90 and 100%.

Figure 5

Optimum overlay conversion contour plot.

Figure 5

Optimum overlay conversion contour plot.

CONCLUSIONS

In this study, a high concentration of turbidity (10–350 NTU) was removed by alum in batch experiments using the CF process. To optimize the impact of independent variables on the CF system, CCD based RSM was used. The results of the research revealed that the initial concentration of turbidity had the most significant role in the system so that 99.14% turbidity was eliminated at 350 NTU. Furthermore, under the optimum conditions of rapid mixing (191 rpm), slow mixing (43 rpm), pH (5.5), and also settling time (36 min), the removal of turbidity varied between 90 and 100%. This study highlighted that the CF process is the most reliable chemical method for removing highly turbid water.

ACKNOWLEDGEMENTS

The authors hereby express their gratitude to the Water and Wastewater Company of Kermanshah Province for financial support (Grant number: 129/95/21868), and Kermanshah University of Medical Sciences for supplying laboratory facilities.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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