Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology
Ziming Zhao, Wenjun Sun, Madhumita B. Ray, Ajay K Ray, Tianyin Huang, Jiabin Chen
Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology
Charge neutralization and sweep flocculation were the major mechanisms.
Effect of process parameters was investigated.
Optimal coagulation conditions were studied by response surface methodology.
ANN models presented more robust and accurate prediction than RSM.
Seasonal algal blooms of Lake Yangcheng highlight the necessity to develop an effective and optimal water treatment process to enhance the removal of algae and dissolved organic matter (DOM). In the present study, the coagulation performance for the removal of algae, turbidity, dissolved organic carbon (DOC) and ultraviolet absorbance at 254 nm (UV254) was investigated systematically by central composite design (CCD) using response surface methodology (RSM). The regression models were developed to illustrate the relationships between coagulation performance and experimental variables. Analysis of variance (ANOVA) was performed to test the significance of the response surface models. It can be concluded that the major mechanisms of coagulation to remove algae and DOM were charge neutralization and sweep flocculation at a pH range of 4.66–6.34. The optimal coagulation conditions with coagulant dosage of 7.57 mg Al/L, pH of 5.42 and initial algal cell density of 3.83 × 106 cell/mL led to removal of 96.76%, 97.64%, 40.23% and 30.12% in term of cell density, turbidity, DOC and UV254 absorbance, respectively, which were in good agreement with the validation experimental results. A comparison between the modeling results derived through both ANOVA and artificial neural networks (ANN) based on experimental data showed a high correlation coefficient, which indicated that the models were significant and fitted well with experimental results. The results proposed a valuable reference for the treatment of algae-laden surface water in practical application by the optimal coagulation-flocculation process.
Algae / Coagulation-flocculation / Response surface methodology / Artificial neural networks
[1] |
Agbovi H K, Wilson L D (2017). Flocculation optimization of orthophosphate with FeCl3 and alginate using the box-behnken response surface methodology. Industrial & Engineering Chemistry Research, 56(12): 3145–3155
CrossRef
Google scholar
|
[2] |
Aktas T S, Takeda F, Maruo C, Fujibayashi M, Nishimura O (2013). Comparison of four kinds of coagulants for the removal of picophytoplankton. Desalination and Water Treatment, 51(16–18): 3547–3557
CrossRef
Google scholar
|
[3] |
Al-Abri M, Al Anezi K, Dakheel A, Hilal N (2010). Humic substance coagulation: Artificial neural network simulation. Desalination, 253(1–3): 153–157
CrossRef
Google scholar
|
[4] |
Amirtharajah A, Mills K M (1982). Rapid-mix design for mechanisms of alum coagulation. Journal- American Water Works Association, 74(4): 210–216
CrossRef
Google scholar
|
[5] |
Baresova M, Pivokonsky M, Novotna K, Naceradska J, Branyik T (2017). An application of cellular organic matter to coagulation of cyanobacterial cells (Merismopedia tenuissima). Water Research, 122: 70–77
CrossRef
Pubmed
Google scholar
|
[6] |
Bingöl DHercan MElevli S, Kiliç E (2012). Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. Bioresource Technology, 112: 111–115
CrossRef
Pubmed
Google scholar
|
[7] |
Carty G, O’Leary G, Crowe M (2002). Water Treatment Manuals: Coagulation, Flocculation and Clarification. Washington D. C.: Environmental Protection Agency, 85
|
[8] |
Campinas M, Rosa M J O (2010). Evaluation of cyanobacterial cells removal and lysis by ultrafiltration. Separation and Purification Technology, 70(3): 345–353
CrossRef
Google scholar
|
[9] |
Engelage S K, Stringfellow W T, Letain T (2009). Disinfection byproduct formation potentials of wetlands, agricultural drains, and rivers and the effect of biodegradation on trihalomethane precursors. Journal of Environmental Quality, 38(5): 1901–1908
CrossRef
Pubmed
Google scholar
|
[10] |
Gadekar M R, Ahammed M M (2016). Coagulation/flocculation process for dye removal using water treatment residuals: Modelling through artificial neural networks. Desalination and Water Treatment, 57(55): 26392–26400
CrossRef
Google scholar
|
[11] |
Gonçalves A L, Ferreira C, Loureiro J A, Pires J C, Simões M (2015). Surface physicochemical properties of selected single and mixed cultures of microalgae and cyanobacteria and their relationship with sedimentation kinetics. Bioresources and Bioprocessing, 2(1): 21–31
CrossRef
Google scholar
|
[12] |
Gonzalez-Torres A, Putnam J, Jefferson B, Stuetz R M, Henderson R K (2014). Examination of the physical properties of Microcystis aeruginosa flocs produced on coagulation with metal salts. Water Research, 60: 197–209
CrossRef
Pubmed
Google scholar
|
[13] |
Goslan E H, Seigle C, Purcell D, Henderson R, Parsons S A, Jefferson B, Judd S J (2017). Carbonaceous and nitrogenous disinfection by-product formation from algal organic matter. Chemosphere, 170: 1–9
CrossRef
Pubmed
Google scholar
|
[14] |
Gough R, Holliman P J, Cooke G M, Freeman C (2015). Characterisation of algogenic organic matter during an algal bloom and its implications for trihalomethane formation. Sustainability of Water Quality and Ecology, 6: 11–19
CrossRef
Google scholar
|
[15] |
Guo T T, Yang Y L, Liu R P, Li X (2017). Enhanced removal of intracellular organic matters (IOM) from Microcystic aeruginosa by aluminum coagulation. Separation and Purification Technology, 189: 279–287
CrossRef
Google scholar
|
[16] |
Halder G, Dhawane S, Barai P K, Das A (2015). Optimizing chromium (VI) adsorption onto superheated steam activated granular carbon through response surface methodology and artificial neural network. Environmental Progress & Sustainable Energy, 34(3): 638–647
CrossRef
Google scholar
|
[17] |
Henderson R K, Parsons S A, Jefferson B (2010). The impact of differing cell and algogenic organic matter (AOM) characteristics on the coagulation and flotation of algae. Water Research, 44(12): 3617–3624
CrossRef
Google scholar
|
[18] |
Javadi N, Ashtiani F Z, Fouladitajar A, Zenooz A M (2014). Experimental studies and statistical analysis of membrane fouling behavior and performance in microfiltration of microalgae by a gas sparging assisted process. Bioresource Technology, 162: 350–357
CrossRef
Pubmed
Google scholar
|
[19] |
Khayet M, Cojocaru C, Essalhi M (2011). Artificial neural network modeling and response surface methodology of desalination by reverse osmosis. Journal of Membrane Science, 368(1–2): 202–214
CrossRef
Google scholar
|
[20] |
Kim S C (2016). Application of response surface method as an experimental design to optimize coagulation–flocculation process for pre-treating paper wastewater. Journal of Industrial and Engineering Chemistry, 38(Supplement C): 93–102
CrossRef
Google scholar
|
[21] |
Kundu P, Debsarkar A, Mukherjee S (2013). Artificial neural network modeling for biological removal of organic carbon and nitrogen from slaughterhouse wastewater in a sequencing batch reactor. Advances in Artificial Neural Systems, 2013: Article ID 268064
CrossRef
Google scholar
|
[22] |
Lanciné G D, Bamory K, Raymond L, Jean-Luc S, Christelle B, Jean B (2008). Coagulation-Flocculation treatment of a tropical surface water with alum for dissolved organic matter (DOM) removal: Influence of alum dose and pH adjustment. Journal of International Environmental Application and Science, 3(4): 247–257
|
[23] |
Lee J, Rai P K, Jeon Y J, Kim K H, Kwon E E (2017). The role of algae and cyanobacteria in the production and release of odorants in water. Environmental Pollution, 227: 252–262
CrossRef
Pubmed
Google scholar
|
[24] |
Li G (2018). China’s Ecological Environment Statements Bulletin of 2017. Beijing: Ministry of Ecology and Environment of China (in Chinese)
|
[25] |
Li L, Zhang S, He Q, Hu X B (2015). Application of response surface method in experimeatal design and optimization. Research and Exploration in Laboratory, 34(8): 41–45 (in Chinese)
|
[26] |
Lin J L, Hua L C, Hung S K, Huang C (2017). Algal removal from cyanobacteria-rich waters by preoxidation-assisted coagulation–flotation: Effect of algogenic organic matter release on algal removal and trihalomethane formation. Journal of Environmental Sciences, 63:147–155
CrossRef
Google scholar
|
[27] |
Ma C X, Hu W R, Pei H Y, Xu H Z, Pei R T (2016). Enhancing integrated removal of Microcystis aeruginosa and adsorption of microcystins using chitosan-aluminum chloride combined coagulants: Effect of chemical dosing orders and coagulation mechanisms. Colloids and Surfaces A—Physicochemical and Engineering Aspects, 490: 258–267
CrossRef
Google scholar
|
[28] |
Ma J R, Deng J M, Qin B Q, Long S X (2013). Progress and prospects on cyanobacteria bloom-forming mechanism in lakes. Acta Ecologica Sinica, 33(10): 3020–3030
CrossRef
Google scholar
|
[29] |
Matilainen A, Vepsäläinen M, Sillanpää M (2010). Natural organic matter removal by coagulation during drinking water treatment: a review. Advances in Colloid and Interface Science, 159(2): 189–197
CrossRef
Pubmed
Google scholar
|
[30] |
McArthur R H, Andrews R C (2015). Development of artificial neural networks based confidence intervals and response surfaces for the optimization of coagulation performance. Water Science and Technology-Water Supply, 15(5): 1079–1087
CrossRef
Google scholar
|
[31] |
Merel S, Walker D, Chicana R, Snyder S, Baurès E, Thomas O (2013). State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environment International, 59: 303–327
CrossRef
Pubmed
Google scholar
|
[32] |
Moghaddari M, Yousefi F, Ghaedi M, Dashtian K (2018). A simple approach for the sonochemical loading of Au, Ag and Pd nanoparticle on functionalized MWCNT and subsequent dispersion studies for removal of organic dyes: Artificial neural network and response surface methodology studies. Ultrasonics Sonochemistry, 42: 422–433
CrossRef
Pubmed
Google scholar
|
[33] |
Olmez-Hanci T, Arslan-Alaton I, Basar G (2011). Multivariate analysis of anionic, cationic and nonionic textile surfactant degradation with the H2O2/UV-C process by using the capabilities of response surface methodology. Journal of Hazardous Materials, 185(1): 193–203
CrossRef
Pubmed
Google scholar
|
[34] |
Oyama Y, Fukushima T, Matsushita B, Matsuzaki H, Kamiya K, Kobinata H (2015). Monitoring levels of cyanobacterial blooms using the visual cyanobacteria index (VCI) and floating algae index (FAI). International Journal of Applied Earth Observation and Geoinformation, 38: 335–348
CrossRef
Google scholar
|
[35] |
Piuleac C G, Curteanu S, Rodrigo M A, Saez C, Fernandez F J (2013). Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes. Central European Journal of Chemistry, 11(7): 1213–1224
|
[36] |
Shen Q, Zhu J, Cheng L, Zhang J, Zhang Z, Xu X (2011). Enhanced algae removal by drinking water treatment of chlorination coupled with coagulation. Desalination, 271(1–3): 236–240
CrossRef
Google scholar
|
[37] |
Trinh T K, Kang L S (2011). Response surface methodological approach to optimize the coagulation–flocculation process in drinking water treatment. Chemical Engineering Research & Design, 89(7): 1126–1135
CrossRef
Google scholar
|
[38] |
Wang J P, Chen Y Z, Ge X W, Yu H Q (2007). Optimization of coagulation–flocculation process for a paper-recycling wastewater treatment using response surface methodology. Colloids and Surfaces. A, Physicochemical and Engineering Aspects, 302(1–3): 204–210
CrossRef
Google scholar
|
[39] |
Wang L, Yan X, Ma J, Xu X (2017). Process analysis study on algae removal from eutrophic water in Taihu lake. Journal of Changzhou University, 29(1): 41–45 (in Chinese)
|
[40] |
Wang Y, Chen K, Mo L, Li J, Xu J (2014). Optimization of coagulation–flocculation process for papermaking-reconstituted tobacco slice wastewater treatment using response surface methodology. Journal of Industrial and Engineering Chemistry, 20(2): 391–396
CrossRef
Google scholar
|
[41] |
Wang Y, Hu W, Peng Z, Zeng Y, Rinke K (2018). Predicting lake eutrophication responses to multiple scenarios of lake restoration: A three-dimensional modeling approach. Water (Basel), 10(8): 994–1012
CrossRef
Google scholar
|
[42] |
Westrick J A, Szlag D C, Southwell B J, Sinclair J (2010). A review of cyanobacteria and cyanotoxins removal/inactivation in drinking water treatment. Analytical and Bioanalytical Chemistry, 397(5): 1705–1714
CrossRef
Pubmed
Google scholar
|
[43] |
Xiao X, He J, Huang H, Miller T R, Christakos G, Reichwaldt E S, Ghadouani A, Lin S, Xu X, Shi J (2017). A novel single-parameter approach for forecasting algal blooms. Water Research, 108(Supplement C): 222–231
CrossRef
Pubmed
Google scholar
|
[44] |
Yang Z L, Gao B Y, Yue Q Y, Wang Y (2010). Effect of pH on the coagulation performance of Al-based coagulants and residual aluminum speciation during the treatment of humic acid-kaolin synthetic water. Journal of Hazardous Materials, 178(1–3): 596–603
CrossRef
Pubmed
Google scholar
|
[45] |
Zamyadi A, Coral L A, Barbeau B, Dorner S, Lapolli F R, Prévost M (2015). Fate of toxic cyanobacterial genera from natural bloom events during ozonation. Water Research, 73: 204–215
CrossRef
Pubmed
Google scholar
|
[46] |
Zhang P, Wu Z, Zhang G, Zeng G, Zhang H, Li J, Song X, Dong J (2008). Coagulation characteristics of polyaluminum chlorides PAC-Al30 on humic acid removal from water. Separation and Purification Technology, 63(3): 642–647
CrossRef
Google scholar
|
[47] |
Zhang Y, Tian J, Nan J, Gao S, Liang H, Wang M, Li G (2011). Effect of PAC addition on immersed ultrafiltration for the treatment of algal-rich water. Journal of Hazardous Materials, 186(2–3): 1415–1424
CrossRef
Pubmed
Google scholar
|
[48] |
Zhao J N, Sun L X, Tan Z C (2010). Low-temperature heat capacities and thermodynamic properties of N-benzyloxycarbonyl-L-3-phenylalanine (C17H17NO4). Journal of Chemical & Engineering Data, 55(10): 4267–4272
CrossRef
Google scholar
|
[49] |
Zheng X Y, Zheng H L, Zhao S Y, Chen W, Yan Z Q, Dong L H (2015). Review on the removal of algae in source water by coagulation technology. Chemical Research and Application, 27(11): 1619–1624
|
/
〈 | 〉 |