# Optimizing the Flocculation Effect of Cationic Polyacrylamide Prepared with UV-Initiated Polymerization by Response Surface Methodology

^{1}

^{2}

^{3}

^{4}

^{*}

^{†}

## Abstract

**:**

^{−1}, 7.28, and 5.95 min, respectively, and the turbidity of the treated wastewater was reduced to 6.24 NTU.

## 1. Introduction

^{−1}, respectively [5]. The flocculation of CPAM is a comprehensive process including physical, chemical and even biological actions, and its flocculation effect is affected by many factors, such as the performance of the flocculant, process design, and wastewater quality [6]. The flocculant exhibits the best effect only when each factor is set reasonably. Consequently, it is necessary to study the factors affecting the flocculation effect of CPAM and their influencing rule. Many factors affect the flocculation effect of CPAM, but those that can be controlled manually mainly include the intrinsic viscosity, cationic degree and usage amount of CPAM, the treated wastewater pH, the stirring and settling time of flocculation, etc., and their reasonable setting is critical to the flocculation effect of CPAM [7].

## 2. Materials and Methods

#### 2.1. Materials

#### 2.2. Polymer Preparation

## 3. Single-Factor Flocculation Test, Results and Discussion

#### 3.1. Single Factor Flocculation Test Design

_{2}O

_{3}·2SiO

_{2}·2H

_{2}O. The particles were mostly less than 5 μm in size. After kaolin was mixed with water, wastewater was formed that contained suspended solids, and had the property of negatively charged colloid. The wastewater used in the flocculation test was prepared with kaolin and purified water, and the concentration of kaolin in the wastewater was 2000 mg·L

^{−1}. The original turbidity of the suspension exceeded the upper limit of the turbidity meter (HACH, Loveland, CO, USA). The test process was as follows: a beaker was used to hold 500 mL of kaolin wastewater, its pH value was adjusted to the predetermined value by adding HCl or NaOH solution, the predetermined amount of specific CPAM product was added to the wastewater, the wastewater was stirred with a ZR4-6 coagulation experiment blender (Shenzhen Zhongrunshui Industrial Technology Development Co., Ltd., Shenzhen, China) at a stirring speed of 300 rpm for the predetermined time, the solution was settled for the predetermined time, and the turbidity of the supernatant was measured with a turbidity meter. The flocculation effect of CPAM was evaluated, and the influences of the intrinsic viscosity and cationic degree of CPAM, the wastewater pH, the stirring time and the settling time on the flocculation efficiency of CPAM were analyzed according to the measurement results.

#### 3.2. Results and Discussion of the Single-Factor Flocculation Test

#### 3.2.1. Impact of Wastewater pH on the Flocculation Effect of CPAM

^{−1}, 5 min and 30 min, respectively, and the pH values of wastewater samples were adjusted according to the predetermined gradient values.

#### 3.2.2. Impacts of the Dosage and Intrinsic Viscosity of CPAM on Its Flocculation Effect

^{−1}, respectively. When comparing the variation trend for wastewater turbidity, it was found that the flocculation effect of the CPAM products from good to poor was in the order of CPAM-9.51-28.3, CPAM-8.12-27.9 and CPAM-5.82-28.1. The flocculation test conditions and treated wastewater were identical, and the three CPAM products had almost the same charge neutralization effect because of their nearly equal cationic degree. Therefore, the only reason for the different flocculation results was their different intrinsic viscosities [9]. Normally, the greater the intrinsic viscosity of CPAM is, the stronger its adsorption bridging, and the lower the turbidity wastewater treated, which was also confirmed by the results shown in Figure 3.

#### 3.2.3. Impact of the Dosage and Cationic Degree of CPAM on Its Flocculation Effect

^{−1}, the wastewater turbidity decreased to the lowest value (13.6 NTU). That of CPAM-8.03-15.8 was the worst, and when its dosage was 8 mg·L

^{−1}, the wastewater turbidity decreased to the lowest value; however, it still reached 32.9 NTU. These differences were mainly caused by the different cationic degrees of the three CPAMs. Normally, the greater the cationic degree of CPAM is, the stronger the charge neutralization, and the lower the turbidity wastewater treated [19], which was also confirmed by the test results. Figure 3 also shows that the three CPAM products led to colloid destabilization; the most obvious was CPAM-8.14-40.2 with the highest cationic degree, and the least obvious was CPAM-8.03-15.8 with the lowest cationic degree, which indicated that the higher the cationic degree of CPAM is, the easier it is to destabilize the colloid. Therefore, for CPAM with a high cationic degree, it is critical to add an appropriate dosage during its flocculation process.

#### 3.2.4. Impact of Stirring Time on Flocculation Properties of CPAM

^{−1}, and the stirring times were set according to the predetermined gradient times.

#### 3.2.5. Impact of the Settling Time on CPAM Flocculation Efficiency

## 4. RSM Flocculation Test, Results and Discussion

#### 4.1. RSM Flocculation Test Design

#### 4.2. Results and Discussion of the RSM Flocculation Test

#### 4.2.1. Discussion of RSM Test Results

#### 4.2.2. Model Fitting

_{0}+ A

_{1}Z

_{1}+ A

_{2}Z

_{2}+ A

_{3}Z

_{3}+ A

_{12}Z

_{12}+ A

_{13}Z

_{13}+ A

_{23}Z

_{23}+ A

_{11}Z

_{1}

^{2}+ A

_{22}Z

_{2}

^{2}+ A

_{33}Z

_{3}

^{2},

_{1}, Z

_{2}and Z

_{3}refer to the first-order terms of variables, i.e., the CPAM dosage (mg·L

^{−1}), the wastewater pH and the stirring time (minutes), respectively; Z

_{1}

^{2}, Z

_{2}

^{2}and Z

_{3}

^{2}refer to their quadratic terms; and Z

_{12}, Z

_{13}and Z

_{23}refer to the corresponding terms of interaction effects between two variables, respectively. A

_{0}was a constant term; A

_{1}, A

_{2}and A

_{3}refer to the primary linear coefficients of the CPAM dosage (mg·L

^{−1}), the wastewater pH and the stirring time (minutes), respectively; A

_{11}, A

_{22}and A

_{33}represent their secondary term coefficients, respectively; and A

_{12}, A

_{13}and A

_{23}represent the interaction term coefficients among variables, respectively.

**R**

^{2}and adjusted

**R**

^{2}. Additionally, the interaction effects of the factors (Z

_{12}, Z

_{13}and Z

_{23}) on the response value were analyzed using three-dimensional plots and two-dimensional contour graphs [35].

_{1}− 7.425Z

_{2}+ 0.375Z

_{3}− 0.075Z

_{12}+ 1.075Z

_{13}+ 0.575Z

_{23}+ 8.453Z

_{1}

^{2}+ 5.803Z

_{2}

^{2}+ 10.603Z

_{3}

^{2}

_{1}, Z

_{2}, Z

_{13}, Z

_{23}, Z

_{1}

^{2}, Z

_{2}

^{2}, and Z

_{3}

^{2}were all significant model terms and had significant impacts on wastewater turbidity. The “p values Prob > F” of the model were less than 0.0500, which implied that the model was significant. The “Lack of Fit F value” of 1.46 implied that the lack of fit was not significant relative to the pure error and indicated that the equation was reliable [30,32]. The “Pred R-Squared” of 0.9907 was in reasonable agreement with the “Adj R-Squared” of 0.9977, which indicated that Equation (2) was well fitted and could be used to predict the turbidity of wastewater flocculated with CPAM-8.14-40.2. The predicted turbidity values of all flocculating tests are listed in Table 3.

_{3}and Z

_{12}were both greater than 0.0500, which implied that the stirring time and the interaction between CPAM dosage and wastewater pH both showed insignificant impacts on the wastewater turbidity. Therefore, the model, Equation (2), could be further improved by removing the intercepts of insignificant terms from the coded model, but only Z

_{12}can be removed, not Z

_{3}, because Z

_{13}, Z

_{23}, and Z

_{3}

^{2}exhibited significant impacts on the results of the flocculation tests. After optimization, a better fitting model was obtained, and its final equation in terms of actual factors is shown in Equation (3) as follows:

_{1}− 7.425Z

_{2}+ 0.375Z

_{3}+ 1.075Z

_{13}+ 0.575Z

_{23}+ 8.453Z

_{1}

^{2}+ 5.803Z

_{2}

^{2}+ 10.603Z

_{3}

^{2}

#### 4.2.3. Response Surface Analysis

^{−1}and 5 to 7 min, respectively, the wastewater turbidity had a minimum value. Similarly, as shown in Figure 8, when the CPAM dosage was fixed, with increasing wastewater pH and stirring time, the wastewater turbidity showed a trend of first increasing and then decreasing; when the wastewater pH and stirring time were in the range of 6 to 8 and 5 to 7 min, respectively, the wastewater turbidity had a minimal value.

#### 4.2.4. Flocculating Optimization and Model Validation

^{−1}, 7.28, and 5.95 min, respectively, and the predicted turbidity of the treated wastewater was 6.18 NTU. To confirm the reliability of the prediction model, two runs of additional experiments were conducted under the flocculation conditions obtained from the model optimization, and the settling time was 30 min. The experimental results are listed in Table 5 and show that the average of the measured turbidities was 6.24 NTU, which is very close to the predicted value of 6.18 NTU. The error between the measured turbidity and the predicted turbidity was only 3.4%, which indicated that the prediction model could be used to guide the flocculation of CPAM [34,41].

## 5. Conclusions

^{−1}, 7.28, and 5.95 min, respectively, and the turbidity of treated wastewater was reduced to 6.24 NTU.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Imran, M.; Saeed, Z.; Pervaiz, M.; Mehmood, K.; Ejaz, R.; Younas, U.; Nadeem, H.A.; Hussain, S. Enhanced visible light photocatalytic activity of TiO
_{2}co-doped with Fe, Co, and S for degradation of Cango red. Spectrochim. Acta Part A Mol. Biomol. Spectrosc.**2021**, 255, 1196–1204. [Google Scholar] [CrossRef] - Shaheen, S.; Saeed, Z.; Ahmad, A.; Pervaiz, M.; Younas, U.; Khan, R.R.M.; Luque, R.; Rajendran, S. Green synthesis of graphene-based metal nanocomposite for electro and photocatalytic activity; recent advancement and future prospective. Chemosphere
**2023**, 311, 136982. [Google Scholar] [CrossRef] - Chi, N.; Liu, J.; Lei, M.; Feng, L. Preparation of amphiphilic cationic polyacrylamide (CPAM) with cationic microblock structure to enhance printing and dyeing sludge dewatering and condition performance. Environ. Sci. Pollut. Res.
**2023**, 30, 13079–13093. [Google Scholar] [CrossRef] [PubMed] - Yang, K.; Chen, J.; Yao, C. Cationic polyacrylamide emulsion with ultra-high concentration as a flocculant for paper mill wastewater treatment. BioResources
**2020**, 152, 3173–3189. [Google Scholar] [CrossRef] - Zhang, Z.; Zheng, H.; Sun, Y.; Zhao, C.; Zhou, Y.; Tang, X.; Zhao, C. A combined process of chemical precipitation and flocculation for treating phosphating wastewater. Desalination Water Treat.
**2016**, 57, 25520–25531. [Google Scholar] [CrossRef] - Fan, Y.; Ma, X.; Dong, X.; Feng, Z.; Dong, Y. Characterisation of floc size, effective density and sedimentation under various flocculation mechanisms. Water Sci. Technol.
**2020**, 827, 1261–1271. [Google Scholar] [CrossRef] - Sang, Y.; Lin, A.; Liu, X. Population balance modeling of cationic polyacrylamide (CPAM) induced flocculation process for lignin recovery from the pre-hydrolysis liquor of kraft pulping process. Sep. Purif. Technol.
**2019**, 221, 152–158. [Google Scholar] [CrossRef] - Zheng, H.; Sun, Y.; Guo, J.; Li, F.; Fan, W.; Liao, Y.; Guan, Q. Characterization and Evaluation of Dewatering Properties of PADB, a Highly Efficient Cationic Flocculant. Ind. Eng. Chem. Res.
**2014**, 537, 2572–2582. [Google Scholar] [CrossRef] - Zhang, Z.; Zheng, H.; Huang, F.; Li, X.; He, S.; Zhao, C. Template Polymerization of a Novel Cationic Polyacrylamide: Sequence Distribution, Characterization, and Flocculation Performance. Ind. Eng. Chem. Res.
**2016**, 55, 9819–9828. [Google Scholar] [CrossRef] - Shang, H.Z.; Liu, J.P.; Zheng, Y.B.; Wang, L.G. Synthesis, characterization, and flocculation properties of poly(acrylamide-methacryloxyethyltrimethyl ammonium chloride-methacryloxypropyl-trimethoxy silane). J. Appl. Polym. Sci.
**2009**, 111, 1594–1599. [Google Scholar] [CrossRef] - Zheng, H.; Sun, Y.; Zhu, C.; Guo, J.; Zhao, C.; Liao, Y.; Guan, Q. UV-initiated polymerization of hydrophobically associating cationic flocculants: Synthesis, characterization, and dewatering properties. Chem. Eng. J.
**2013**, 234, 318–326. [Google Scholar] [CrossRef] - Daifa, M.; Shmoeli, E.; Domb, A.J. Enhanced flocculation activity of polyacrylamide-based flocculant for purification of industrial wastewater. Polym. Adv. Technol.
**2019**, 30, 2636–2646. [Google Scholar] [CrossRef] - Fijałkowska, G.; Szewczuk-Karpisz, K.; Wiśniewska, M. Chromium (VI) and lead (II) accumulation at the montmorillonite/aqueous solution interface in the presence of polyacrylamide containing quaternary amine groups. J. Mol. Liq.
**2019**, 293, 111514. [Google Scholar] [CrossRef] - Wiśniewska, M.; Chibowski, S.; Urban, T.; Terpiłowski, K. Investigations of chromium (III) oxide removal from the aqueous suspension using the mixed flocculant composed of anionic and cationic polyacrylamides. J. Hazard. Mater.
**2019**, 368, 378–385. [Google Scholar] [CrossRef] - Szewczuk-Karpisz, K.; Fijałkowska, G.; Wiśniewska, M.; Wójcik, G. Chromium (VI) reduction and accumulation on the kaolinite surface in the presence of cationic soil flocculant. J. Soils Sediments
**2020**, 20, 3688–3697. [Google Scholar] [CrossRef] - Harif, S.; Aboulhassan, M.A.; Bammou, L. Multi-response optimization for color removal from cardboard wastewater using polyaluminum chloride and cationic polyacrylamide. Int. J. Environ. Sci. Technol.
**2023**, 20, 4281–4292. [Google Scholar] [CrossRef] - Khan, S.; Zheng, H.; Sun, Q.; Liu, Y.; Li, H.; Ding, W.; Navarro, A. Synthesis and characterization of a novel cationic polyacrylamide-based flocculants to remove Congo red efficiently in acid aqueous environment. J. Mater. Sci. Mater. Electron.
**2020**, 31, 18832–18843. [Google Scholar] [CrossRef] - Lapointe, M.; Farner, J.M.; Hernandez, L.M.; Tufenkji, N. Understanding and improving microplastic removal during water treatment: Impact of coagulation and flocculation. Environ. Sci. Technol.
**2020**, 54, 8719–8727. [Google Scholar] [CrossRef] - Liu, X.; Xu, Q.; Wang, D.; Wu, Y.; Yang, Q.; Liu, Y.; Wang, Q.; Li, X.; Li, H.; Zeng, G.; et al. Unveiling the mechanisms of how cationic polyacrylamide affects short-chain fatty acids accumulation during long-term anaerobic fermentation of waste activated sludge. Water Res.
**2019**, 155, 142–151. [Google Scholar] [CrossRef] [PubMed] - Wang, C.; Cai, Q.; Feng, B.; Feng, S.; Tian, C.; Jiang, X.; Wu, X.; Xiao, B. Improving the performance of shipboard rotary drum filters in the removal of cyanobacterial blooms by cationic polyacrylamide flocculation. Sep. Purif. Technol.
**2019**, 215, 660–669. [Google Scholar] [CrossRef] - Park, J.B.K.; Meerman, C.; Craggs, R. Continuous low dosing of cationic polyacrylamide (PAM) to enhance algal harvest from a hectare-scale wastewater treatment high rate algal pond. N. Z. J. Bot.
**2019**, 57, 112–124. [Google Scholar] [CrossRef] - Tajbakhsh, S.F.; Mohmmadipour, R.; Janani, H. One-pot production of a graft copolymer of cationic starch and cationic polyacrylamide applicable as flocculant for wastewater treatment. J. Macromol. Sci. Part A
**2022**, 59, 698–710. [Google Scholar] [CrossRef] - Zhang, Z.; Pan, S.; Huang, F.; Li, X.; Shang, J.; Lai, J.; Liao, Y. Nitrogen and Phosphorus Removal by Activated Sludge Process: A Review. Mini-Rev. Org. Chem.
**2017**, 14, 99–106. [Google Scholar] [CrossRef] - Wiśniewska, M.; Fijałkowska, G.; Szewczuk-Karpisz, K.; Urban, T.; Nosal-Wiercińska, A.; Wójcik, G. Comparison of adsorption affinity of anionic and cationic polyacrylamides for montmorillonite surface in the presence of chromium (VI) ions. Adsorption
**2019**, 25, 41–50. [Google Scholar] [CrossRef][Green Version] - Zhou, S.; Bu, X.; Alheshibri, M.; Zhan, H.; Xie, G. Floc structure and dewatering performance of kaolin treated with cationic polyacrylamide degraded by hydrodynamic cavitation. Chem. Eng. Commun.
**2022**, 209, 798–807. [Google Scholar] [CrossRef] - Agbovi, H.K.; Wilson, L.D. Flocculation optimization of orthophosphate with FeCl3 and alginate using the Box–Behnken response surface methodology. Ind. Eng. Chem. Res.
**2017**, 56, 3145–3155. [Google Scholar] [CrossRef] - Ahmad, T.; Ahmad, K.; Alam, M. Simultaneous modelling of coagulant recovery and reuse by response surface methodology. J. Environ. Manag.
**2021**, 285, 112139. [Google Scholar] [CrossRef] - Birjandi, N.; Younesi, H.; Bahramifar, N.; Ghafari, S.; Zinatizadeh, A.A.; Sethupathi, S. Optimization of coagulation-flocculation treatment on paper-recycling wastewater: Application of response surface methodology. J. Environ. Sci. Health Part A
**2013**, 48, 1573–1582. [Google Scholar] [CrossRef] [PubMed] - Wang, K.; Mao, Y.; Wang, C.; Ke, Q.; Zhao, M.; Wang, Q. Application of a combined response surface methodology (RSM)-artificial neural network (ANN) for multiple target optimization and prediction in a magnetic coagulation process for secondary effluent from municipal wastewater treatment plants. Environ. Sci. Pollut. Res.
**2022**, 29, 36075–36087. [Google Scholar] [CrossRef] - Dbik, A.; El Messaoudi, N.; Bentahar, S.; El Khomri, M.; Lacherai, A.; Faska, N. Optimization of Methylene Blue Adsorption on Agricultural Solid Waste Using Box–Behnken Design (BBD) Combined with Response Surface Methodology (RSM) Modeling. Biointerface Res. Appl. Chem.
**2022**, 12, 4567–4583. [Google Scholar] - Ezemagu, I.G.; Ejimofor, M.I.; Menkiti, M.C.; Nwobi-Okoye, C.C. Modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network. S. Afr. J. Chem. Eng.
**2021**, 35, 78–88. [Google Scholar] [CrossRef] - Gökçek, Ö.B.; Özdemir, S. Optimization of the coagulation–flocculation process for slaughterhouse wastewater using response surface methodology. CLEAN–Soil Air Water
**2020**, 48, 2000033. [Google Scholar] [CrossRef] - Heidari, M.; Vosoughi, M.; Sadeghi, H.; Dargahi, A.; Mokhtari, S.A. Degradation of diazinon from aqueous solutions by electro-Fenton process: Effect of operating parameters, intermediate identification, degradation pathway, and optimization using response surface methodology (RSM). Sep. Sci. Technol.
**2021**, 56, 2287–2299. [Google Scholar] [CrossRef] - Kim, S.-C. Application of response surface method as an experimental design to optimize coagulation–flocculation process for pre-treating paper wastewater. J. Ind. Eng. Chem.
**2016**, 38, 93–102. [Google Scholar] [CrossRef] - Luo, S.; Wu, X.; Jiang, H.; Yu, M.; Liu, Y.; Min, A.; Li, W.; Ruan, R. Edible fungi-assisted harvesting system for efficient microalgae bio-flocculation. Bioresour. Technol.
**2019**, 282, 325–330. [Google Scholar] [CrossRef] - Ma, C.; Yu, H.; Gao, Y.; Xu, W.; Xu, T.; Wang, L.; Zhao, B.; Zhang, Z.; Xu, J. Operation parameters optimization of a hybrid dead-end/cross-flow forward osmosis system for microalgae dewatering by response surface methodology. Process Saf. Environ. Prot.
**2020**, 143, 14–24. [Google Scholar] [CrossRef] - Nourani, M.; Baghdadi, M.; Javan, M.; Bidhendi, G.N. Production of a biodegradable flocculant from cotton and evaluation of its performance in coagulation-flocculation of kaolin clay suspension: Optimization through response surface methodology (RSM). J. Environ. Chem. Eng.
**2016**, 4, 1996–2003. [Google Scholar] [CrossRef][Green Version] - Onukwuli, O.D.; Nnaji, P.C.; Menkiti, M.C.; Anadebe, V.C.; Oke, E.O.; Ude, C.N.; Ude, C.J.; Okafor, N.A. Dual-purpose optimization of dye-polluted wastewater decontamination using bio-coagulants from multiple processing techniques via neural intelligence algorithm and response surface methodology. J. Taiwan Inst. Chem. Eng.
**2021**, 125, 372–386. [Google Scholar] [CrossRef] - Rezania, N.; Hasani Zonoozi, M.; Saadatpour, M. Coagulation-flocculation of turbid water using graphene oxide: Simulation through response surface methodology and process characterization. Environ. Sci. Pollut. Res.
**2021**, 28, 14812–14827. [Google Scholar] [CrossRef] - Singh, H.M.; Tyagi, V.V.; Ahmad, S.; Kothari, R. Optimization of flocculation efficiency of Chlorella pyrenoidosa with CaCl
_{2}using the Box-Behnken design of response surface methodology: A cost effective statistical investigation. Biomass Convers. Biorefin.**2022**, 21, 1–13. [Google Scholar] [CrossRef] - Zhang, B.; Liu, L.; Lin, X.; Xu, Z.; Luo, W.; Luo, L. Response surface methodology to optimize self-flocculation harvesting of microalgae Desmodesmus sp. CHX1. Environ. Technol.
**2021**, 43, 2647–2655. [Google Scholar] [CrossRef] [PubMed]

**Figure 7.**The impact of the interaction between CPAM dosage and stirring time on wastewater turbidity. (

**a**) Contour diagram; (

**b**) 3D surface diagram.

**Figure 8.**Impact of the interaction between stirring time and wastewater pH on wastewater turbidity. (

**a**) Contour diagram; (

**b**) 3D surface diagram.

CPAM Number | Intrinsic Viscosity (dL·g^{−1}) | Cationic Degree (%) | The Main Parameters of the Polymerization Process | ||
---|---|---|---|---|---|

Molar Ratio of AM and DMD | Dosage of V-50 Initiator (%) | Total Monomer Content (%) | |||

CPAM-8.03-15.8 | 8.03 | 15.8 | 7:3 | 0.08 | 30 |

CPAM-7.91-33.5 | 7.91 | 33.5 | 5:5 | 0.05 | 30 |

CPAM-8.14-40.2 | 8.14 | 40.2 | 4:6 | 0.03 | 30 |

CPAM-5.82-28.1 | 5.82 | 28.1 | 5:5 | 0.13 | 30 |

CPAM-8.12-27.9 | 8.12 | 27.9 | 5:5 | 0.04 | 30 |

CPAM-9.51-28.3 | 9.51 | 28.3 | 5:5 | 0.03 | 30 |

Variable Code | Variables | Variable Levels and Corresponding Values | ||
---|---|---|---|---|

−1 | 0 | 1 | ||

Z_{1} | CPAM dosage (mg·L^{−1}) | 5 | 6 | 7 |

Z_{2} | Wastewater pH | 4 | 6 | 8 |

Z_{3} | Stirring time (minutes) | 4 | 6 | 8 |

Run | CPAM Dosage (mg·L^{−1}) | Wastewater pH | Stirring Time (Minutes) | Response Value of Turbidity (NTU) | ||
---|---|---|---|---|---|---|

Actual | Predicted | |||||

Equation (2) | Equation (3) | |||||

1 | 6.0 | 6.0 | 6.0 | 8.90 | 8.82 | 8.82 |

2 | 6.0 | 4.0 | 8.0 | 32.80 | 32.45 | 32.45 |

3 | 7.0 | 4.0 | 6.0 | 33.30 | 33.50 | 32.43 |

4 | 6.0 | 6.0 | 6.0 | 8.80 | 8.82 | 8.82 |

5 | 6.0 | 4.0 | 4.0 | 33.10 | 32.85 | 32.85 |

6 | 6.0 | 6.0 | 6.0 | 8.10 | 8.82 | 8.82 |

7 | 6.0 | 8.0 | 4.0 | 16.50 | 16.85 | 16.85 |

8 | 5.0 | 8.0 | 6.0 | 13.00 | 12.80 | 12.73 |

9 | 5.0 | 6.0 | 8.0 | 24.30 | 24.25 | 24.25 |

10 | 5.0 | 6.0 | 4.0 | 25.80 | 26.65 | 25.65 |

11 | 6.0 | 6.0 | 6.0 | 9.20 | 8.82 | 8.82 |

12 | 5.0 | 4.0 | 6.0 | 27.10 | 27.50 | 27.58 |

13 | 7.0 | 6.0 | 8.0 | 32.10 | 32.25 | 32.25 |

14 | 7.0 | 8.0 | 6.0 | 18.9 | 18.50 | 18.58 |

15 | 6.0 | 6.0 | 6.0 | 9.10 | 8.82 | 8.82 |

16 | 7.0 | 6.0 | 4.0 | 29.3 | 29.35 | 29.35 |

17 | 6.0 | 8.0 | 8.0 | 18.5 | 18.75 | 18.75 |

Source | Sum of Squares | Df | Mean Squares | F Value | p Value Prob > F | Remark | |
---|---|---|---|---|---|---|---|

Model | Equation (2) | 1532.076706 | 9 | 170.2307451 | 759.9586835 | <0.0001 | significant |

Equation (3) | 1532.054206 | 8 | 191.5067757 | 963.2531945 | <0.0001 | significant | |

Z_{1}-the CPAM dosage(mg·L^{−1}) | Equation (2) | 68.445 | 1 | 68.445 | 305.5580357 | <0.0001 | |

Equation (3) | 68.445 | 1 | 68.445 | 344.2690978 | <0.0001 | ||

Z_{2}-the wastewater pH | Equation (2) | 441.045 | 1 | 441.045 | 1968.950893 | <0.0001 | |

Equation (3) | 441.045 | 1 | 441.045 | 2218.396731 | <0.0001 | ||

Z_{3}-the stirring time(minutes) | Equation (2) | 1.125 | 1 | 1.125 | 5.022321429 | 0.06 | |

Equation (3) | 1.125 | 1 | 1.125 | 5.658597925 | 0.0446 | ||

Z_{12} | Equation (2) | 0.0225 | 1 | 0.0225 | 0.100446429 | 0.7605 | |

Equation (3) | -- | -- | -- | -- | -- | ||

Z_{13} | Equation (2) | 4.6225 | 1 | 4.6225 | 20.63616071 | 0.0027 | |

Equation (3) | 4.6225 | 1 | 4.6225 | 23.25055014 | 0.0013 | ||

Z_{23} | Equation (2) | 1.3225 | 1 | 1.3225 | 5.904017857 | 0.0454 | |

Equation (3) | 1.3225 | 1 | 1.3225 | 6.651996228 | 0.0327 | ||

Z_{1}^{2} | Equation (2) | 300.8200263 | 1 | 300.8200263 | 1342.946546 | <0.0001 | |

Equation (3) | 300.8200263 | 1 | 300.8200263 | 1513.084068 | <0.0001 | ||

Z_{2}^{2} | Equation (2) | 141.7642368 | 1 | 141.7642368 | 632.8760573 | <0.0001 | |

Equation (3) | 141.7642368 | 1 | 141.7642368 | 713.054948 | <0.0001 | ||

Z_{3}^{2} | Equation (2) | 473.3179211 | 1 | 473.3179211 | 2113.026433 | <0.0001 | |

Equation (3) | 473.3179211 | 1 | 473.3179211 | 2380.725161 | <0.0001 | ||

Residual | Equation (2) | 1.568 | 7 | 0.224 | |||

Equation (3) | 1.5905 | 8 | 0.1988125 | ||||

Lack of fit | Equation (2) | 0.82 | 3 | 0.273333333 | 1.461675579 | 0.3512 | not significant |

Equation (3) | 0.8425 | 4 | 0.210625 | 1.126336898 | 0.4555 | not significant | |

Pure error | Equation (2) | 0.748 | 4 | 0.187 | |||

Equation (3) | 0.748 | 4 | 0.187 | ||||

Cor total | Equation (2) | 1533.644706 | 16 | ||||

Equation (3) | 1533.644706 | 16 | |||||

R^{2} | Equation (2) | 0.9938 | |||||

Equation (3) | 0.9907 | ||||||

R^{2}_{adj} | Equation (2) | 0.9979 | |||||

Equation (3) | 0.9977 |

Flocculation Conditions | Wastewater Turbidity (NTU) | ||||
---|---|---|---|---|---|

CPAM Dosage (mg·L^{−1}) | Wastewater pH | Stirring Time (Minutes) | Settling Time (Minutes) | Average of Measured Value | Predicted Value |

5.83 | 7.28 | 5.95 | 30 | 6.49 | 6.18 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Fu, C.; Zhang, Z.; Li, Y.; Li, L.; Wang, H.; Liu, S.; Hua, X.; Li, B.
Optimizing the Flocculation Effect of Cationic Polyacrylamide Prepared with UV-Initiated Polymerization by Response Surface Methodology. *Water* **2023**, *15*, 1200.
https://doi.org/10.3390/w15061200

**AMA Style**

Fu C, Zhang Z, Li Y, Li L, Wang H, Liu S, Hua X, Li B.
Optimizing the Flocculation Effect of Cationic Polyacrylamide Prepared with UV-Initiated Polymerization by Response Surface Methodology. *Water*. 2023; 15(6):1200.
https://doi.org/10.3390/w15061200

**Chicago/Turabian Style**

Fu, Chaochen, Zhengan Zhang, Yuying Li, Lin Li, Hongtian Wang, Shaobo Liu, Xia Hua, and Bailian Li.
2023. "Optimizing the Flocculation Effect of Cationic Polyacrylamide Prepared with UV-Initiated Polymerization by Response Surface Methodology" *Water* 15, no. 6: 1200.
https://doi.org/10.3390/w15061200