Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Environ. Sci. Eng.    2019, Vol. 13 Issue (5) : 75    https://doi.org/10.1007/s11783-019-1159-7
RESEARCH ARTICLE
Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology
Ziming Zhao1,2, Wenjun Sun1,3(), Madhumita B. Ray2, Ajay K Ray2, Tianyin Huang4, Jiabin Chen4
1. School of Environment, Tsinghua University, Beijing 100084, China
2. Department of Chemical and Biochemical Engineering, Western University, London Ontario N6A 5B9, Canada
3. Research Institute for Environmental Innovation (Suzhou), Tsinghua University, Suzhou 215163, China
4. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
 Download: PDF(1600 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

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.
Keywords Algae      Coagulation-flocculation      Response surface methodology      Artificial neural networks     
Corresponding Author(s): Wenjun Sun   
Issue Date: 16 October 2019
 Cite this article:   
Ziming Zhao,Wenjun Sun,Madhumita B. Ray, et al. Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology[J]. Front. Environ. Sci. Eng., 2019, 13(5): 75.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-019-1159-7
https://academic.hep.com.cn/fese/EN/Y2019/V13/I5/75
Parameters Range Mean
pH 7.08–8.45 7.48
Temperature (°C) 25–28 27
Cell density (106 cell/mL) 4.2–5.8 4.6
Turbidity (NTU) 198–252 223
DOC (mg/L) 10.2–13.5 12.41
UV254 absorbance (m-1) 0.083–0.094 0.089
Tab.1  Water quality characteristics of Lake Yangcheng
Independent variables Coded and actual levels
-a/-1.682 -1 0 1 +a/1.682
X1 Alum dose (mg Al/L) 4.57 5.67 7.29 8.91 10.02
X2 pH 4.66 5.00 5.50 6.00 6.34
X3 Initial cell density (106 cell/mL) 2.32 3.00 4.00 5.00 5.68
Tab.2  Analytical factors and levels for RSM experimental design
Fig.1  Architecture of the three layers backpropagation artificial neural network (BPNN).
Fig.2  Algal species distribution on the raw water.
Fig.3  Effect of alum dosage on coagulation performance for the water samples with cell density of 4.55 × 106 cell/mL without pH adjustment (error bars represent the standard deviation from duplicate experiments)
Fig.4  Effect of pH on coagulation performance for the water samples with cell density of 4.5 × 106 cell/mL under the coagulation dosage 7.30 mg Al/L (error bars represent the standard deviation from duplicate experiments).
Fig.5  Effect of initial cell density on coagulation performance under the coagulation dosage 7.30 mg Al/L and pH of 5.5 (error bars represent the standard deviation from duplicate experiments).
Run Experimental variables Removal percentage (%)
Alum dosage/X1 (mg Al/L) pH/X2 Initial cell density/X3(E+06 cell/mL) Cell density Turbidity DOC UV254
1 5.67 5 3 93.90 81.25 48.63 28.29
2 8.91 5 3 92.84 92.12 46.35 20.61
3 5.67 6 3 89.50 78.69 41.64 19.24
4 8.91 6 3 91.54 91.26 40.86 28.85
5 5.67 5 5 86.05 85.05 38.88 27.18
6 8.91 5 5 96.86 91.47 44.02 15.01
7 5.67 6 5 83.24 87.06 40.15 24.36
8 8.91 6 5 94.67 92.81 47.12 20.11
9 4.57 5.5 4 91.69 76.71 37.67 25.85
10 10.02 5.5 4 92.17 89.37 47.63 23.99
11 7.29 4.66 4 93.73 97.42 45.88 28.09
12 7.29 6.34 4 90.12 87.68 45.29 25.43
13 7.29 5.5 2.32 93.13 97.25 48.19 26.01
14 7.29 5.5 5.68 92.74 93.69 43.40 23.96
15 7.29 5.5 4 96.92 95.94 50.75 30.43
16 7.29 5.5 4 97.53 95.93 51.55 29.95
17 7.29 5.5 4 97.35 96.21 51.24 30.13
18 7.29 5.5 4 97.78 96.02 51.40 30.42
19 7.29 5.5 4 96.98 96.00 50.96 30.61
20 7.29 5.5 4 97.06 95.94 51.04 30.04
Tab.3  CCD experimental design and experimental results
ANOVA Response
Turbidity DOC UV254 Cell density
R2 0.9089 0.9521 0.8931 0.8895
p-value 0.0006 <0.0001 0.0002 <0.0001
Std. dev. 2.73 1.35 1.90 1.65
Mean 90.89 46.13 25.93 93.29
C.V.% 3.00 2.81 7.31 1.77
PRESS 564.49 104.23 216.66 157.11
AP 11.13 15.82 10.99 11.58
Tab.4  ANOVA results for regression models
Fig.6  Surface plots of removal efficiency with the interaction of coagulant dosage and pH with initial cell density of 4.0 × 106 cell/mL, (a) Cell removal; (b) Turbidity removal; (c) DOC removal; (d) UV254 removal.
Fig.7  Overlaid contour plot for cell density, turbidity, DOC and UV254 removal percentage by alum coagulation. Data fitted by three-factor central composite design.
Dependent responses Topology Correlation coefficient (R2) Std. dev.
Training Validation Testing All
Cell density 3: 8: 1 0.9069 0.9188 0.8652 0.8855 1.3106
Turbidity 3: 10: 1 0.9741 0.9579 0.9647 0.9713 1.6387
DOC 3: 10: 1 0.9785 0.9010 0.9938 0.9731 1.2188
UV254 3: 10: 1 0.9469 0.9431 0.8133 0.8980 1.3675
Total 3: 10: 4 0.9903 0.9705 0.9735 0.9814 1.7859
Tab.5  Performance of ANN network models
Fig.8  The plots of predicted vs. actual values of removal efficiency by BPNN: (a) cell density; (b) Turbidity; (c) DOC; (d) UV254.
1 H K Agbovi, L D Wilson (2017). Flocculation optimization of orthophosphate with FeCl3 and alginate using the box-behnken response surface methodology. Industrial & Engineering Chemistry Research, 56(12): 3145–3155
https://doi.org/10.1021/acs.iecr.6b04765
2 T S Aktas, F Takeda, C Maruo, M Fujibayashi, O Nishimura (2013). Comparison of four kinds of coagulants for the removal of picophytoplankton. Desalination and Water Treatment, 51(16–18): 3547–3557
https://doi.org/10.1080/19443994.2012.750777
3 M Al-Abri, K Al Anezi, A Dakheel, N Hilal (2010). Humic substance coagulation: Artificial neural network simulation. Desalination, 253(1–3): 153–157
https://doi.org/10.1016/j.desal.2009.11.014
4 A Amirtharajah, K M Mills (1982). Rapid-mix design for mechanisms of alum coagulation. Journal- American Water Works Association, 74(4): 210–216
https://doi.org/10.1002/j.1551-8833.1982.tb04890.x
5 M Baresova, M Pivokonsky, K Novotna, J Naceradska, T Branyik (2017). An application of cellular organic matter to coagulation of cyanobacterial cells (Merismopedia tenuissima). Water Research, 122: 70–77
https://doi.org/10.1016/j.watres.2017.05.070 pmid: 28591663
6 D Bingöl M Hercan S Elevli, E Kiliç (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
https://doi.org/10.1016/j.biortech.2012.02.084 pmid: 22425399
7 G Carty, G O’Leary, M Crowe (2002). Water Treatment Manuals: Coagulation, Flocculation and Clarification. Washington D. C.: Environmental Protection Agency, 85
8 M Campinas, M J O Rosa (2010). Evaluation of cyanobacterial cells removal and lysis by ultrafiltration. Separation and Purification Technology, 70(3): 345–353
https://doi.org/10.1016/j.seppur.2009.10.021
9 S K Engelage, W T Stringfellow, T Letain (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
https://doi.org/10.2134/jeq2009.0015 pmid: 19643756
10 M R Gadekar, M M Ahammed (2016). Coagulation/flocculation process for dye removal using water treatment residuals: Modelling through artificial neural networks. Desalination and Water Treatment, 57(55): 26392–26400
https://doi.org/10.1080/19443994.2016.1165150
11 A L Gonçalves, C Ferreira, J A Loureiro, J C Pires, M Simões (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
https://doi.org/10.1186/s40643-015-0051-y
12 A Gonzalez-Torres, J Putnam, B Jefferson, R M Stuetz, R K Henderson (2014). Examination of the physical properties of Microcystis aeruginosa flocs produced on coagulation with metal salts. Water Research, 60: 197–209
https://doi.org/10.1016/j.watres.2014.04.046 pmid: 24859233
13 E H Goslan, C Seigle, D Purcell, R Henderson, S A Parsons, B Jefferson, S J Judd (2017). Carbonaceous and nitrogenous disinfection by-product formation from algal organic matter. Chemosphere, 170: 1–9
https://doi.org/10.1016/j.chemosphere.2016.11.148 pmid: 27951445
14 R Gough, P J Holliman, G M Cooke, C Freeman (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
https://doi.org/10.1016/j.swaqe.2014.12.008
15 T T Guo, Y L Yang, R P Liu, X Li (2017). Enhanced removal of intracellular organic matters (IOM) from Microcystic aeruginosa by aluminum coagulation. Separation and Purification Technology, 189: 279–287
https://doi.org/10.1016/j.seppur.2017.06.066
16 G Halder, S Dhawane, P K Barai, A Das (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
https://doi.org/10.1002/ep.12028
17 R K Henderson, S A Parsons, B Jefferson (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
https://doi.org/doi.org/10.1016/j.watres.2010.04.016
18 N Javadi, F Z Ashtiani, A Fouladitajar, A M Zenooz (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
https://doi.org/10.1016/j.biortech.2014.03.160 pmid: 24768909
19 M Khayet, C Cojocaru, M Essalhi (2011). Artificial neural network modeling and response surface methodology of desalination by reverse osmosis. Journal of Membrane Science, 368(1–2): 202–214
https://doi.org/10.1016/j.memsci.2010.11.030
20 S C Kim (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
https://doi.org/10.1016/j.jiec.2016.04.010
21 P Kundu, A Debsarkar, S Mukherjee (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
https://doi.org/10.1155/2013/268064
22 G D Lanciné, K Bamory, L Raymond, S Jean-Luc, B Christelle, B Jean (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 J Lee, P K Rai, Y J Jeon, K H Kim, E E Kwon (2017). The role of algae and cyanobacteria in the production and release of odorants in water. Environmental Pollution, 227: 252–262
https://doi.org/10.1016/j.envpol.2017.04.058 pmid: 28475978
24 G Li (2018). China’s Ecological Environment Statements Bulletin of 2017. Beijing: Ministry of Ecology and Environment of China (in Chinese)
25 L Li, S Zhang, Q He, X B Hu (2015). Application of response surface method in experimeatal design and optimization. Research and Exploration in Laboratory, 34(8): 41–45 (in Chinese)
26 J L Lin, L C Hua, S K Hung, C Huang (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
https://doi.org/http://dx.doi.org/10.1016/j.jes.2017.02.007
27 C X Ma, W R Hu, H Y Pei, H Z Xu, R T Pei (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
https://doi.org/doi.org/10.1016/j.colsurfa.2015.11.056
28 J R Ma, J M Deng, B Q Qin, S X Long (2013). Progress and prospects on cyanobacteria bloom-forming mechanism in lakes. Acta Ecologica Sinica, 33(10): 3020–3030
https://doi.org/10.5846/stxb201202140200
29 A Matilainen, M Vepsäläinen, M Sillanpää (2010). Natural organic matter removal by coagulation during drinking water treatment: a review. Advances in Colloid and Interface Science, 159(2): 189–197
https://doi.org/10.1016/j.cis.2010.06.007 pmid: 20633865
30 R H McArthur, R C Andrews (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
https://doi.org/doi.org/10.2166/ws.2015.066
31 S Merel, D Walker, R Chicana, S Snyder, E Baurès, O Thomas (2013). State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environment International, 59: 303–327
https://doi.org/10.1016/j.envint.2013.06.013 pmid: 23892224
32 M Moghaddari, F Yousefi, M Ghaedi, K Dashtian (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
https://doi.org/10.1016/j.ultsonch.2017.12.003 pmid: 29429688
33 T Olmez-Hanci, I Arslan-Alaton, G Basar (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
https://doi.org/10.1016/j.jhazmat.2010.09.018 pmid: 20934252
34 Y Oyama, T Fukushima, B Matsushita, H Matsuzaki, K Kamiya, H Kobinata (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
https://doi.org/10.1016/j.jag.2015.02.002
35 C G Piuleac, S Curteanu, M A Rodrigo, C Saez, F J Fernandez (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 Q Shen, J Zhu, L Cheng, J Zhang, Z Zhang, X Xu (2011). Enhanced algae removal by drinking water treatment of chlorination coupled with coagulation. Desalination, 271(1–3): 236–240
https://doi.org/10.1016/j.desal.2010.12.039
37 T K Trinh, L S Kang (2011). Response surface methodological approach to optimize the coagulation–flocculation process in drinking water treatment. Chemical Engineering Research & Design, 89(7): 1126–1135
https://doi.org/10.1016/j.cherd.2010.12.004
38 J P Wang, Y Z Chen, X W Ge, H Q Yu (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
https://doi.org/10.1016/j.colsurfa.2007.02.023
39 L Wang, X Yan, J Ma, X Xu (2017). Process analysis study on algae removal from eutrophic water in Taihu lake. Journal of Changzhou University, 29(1): 41–45 (in Chinese)
40 Y Wang, K Chen, L Mo, J Li, J Xu (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
https://doi.org/10.1016/j.jiec.2013.04.033
41 Y Wang, W Hu, Z Peng, Y Zeng, K Rinke (2018). Predicting lake eutrophication responses to multiple scenarios of lake restoration: A three-dimensional modeling approach. Water (Basel), 10(8): 994–1012
https://doi.org/10.3390/w10080994
42 J A Westrick, D C Szlag, B J Southwell, J Sinclair (2010). A review of cyanobacteria and cyanotoxins removal/inactivation in drinking water treatment. Analytical and Bioanalytical Chemistry, 397(5): 1705–1714
https://doi.org/10.1007/s00216-010-3709-5 pmid: 20502884
43 X Xiao, J He, H Huang, T R Miller, G Christakos, E S Reichwaldt, A Ghadouani, S Lin, X Xu, J Shi (2017). A novel single-parameter approach for forecasting algal blooms. Water Research, 108(Supplement C): 222–231
https://doi.org/10.1016/j.watres.2016.10.076 pmid: 27847147
44 Z L Yang, B Y Gao, Q Y Yue, Y Wang (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
https://doi.org/10.1016/j.jhazmat.2010.01.127 pmid: 20188465
45 A Zamyadi, L A Coral, B Barbeau, S Dorner, F R Lapolli, M Prévost (2015). Fate of toxic cyanobacterial genera from natural bloom events during ozonation. Water Research, 73: 204–215
https://doi.org/10.1016/j.watres.2015.01.029 pmid: 25682048
46 P Zhang, Z Wu, G Zhang, G Zeng, H Zhang, J Li, X Song, J Dong (2008). Coagulation characteristics of polyaluminum chlorides PAC-Al30 on humic acid removal from water. Separation and Purification Technology, 63(3): 642–647
https://doi.org/10.1016/j.seppur.2008.07.008
47 Y Zhang, J Tian, J Nan, S Gao, H Liang, M Wang, G Li (2011). Effect of PAC addition on immersed ultrafiltration for the treatment of algal-rich water. Journal of Hazardous Materials, 186(2–3): 1415–1424
https://doi.org/10.1016/j.jhazmat.2010.12.015 pmid: 21216530
48 J N Zhao, L X Sun, Z C Tan (2010). Low-temperature heat capacities and thermodynamic properties of N-benzyloxycarbonyl-L-3-phenylalanine (C17H17NO4). Journal of Chemical & Engineering Data, 55(10): 4267–4272
https://doi.org/10.1021/je1004092
49 X Y Zheng, H L Zheng, S Y Zhao, W Chen, Z Q Yan, L H Dong (2015). Review on the removal of algae in source water by coagulation technology. Chemical Research and Application, 27(11): 1619–1624
[1] Kangying Guo, Baoyu Gao, Jie Wang, Jingwen Pan, Qinyan Yue, Xing Xu. Flocculation behaviors of a novel papermaking sludge-based flocculant in practical printing and dyeing wastewater treatment[J]. Front. Environ. Sci. Eng., 2021, 15(5): 103-.
[2] Violeta Makareviciene, Egle Sendzikiene, Ieva Gaide. Application of heterogeneous catalysis to biodiesel synthesis using microalgae oil[J]. Front. Environ. Sci. Eng., 2021, 15(5): 97-.
[3] Haoran Feng, Min Liu, Wei Zeng, Ying Chen. Optimization of the O3/H2O2 process with response surface methodology for pretreatment of mother liquor of gas field wastewater[J]. Front. Environ. Sci. Eng., 2021, 15(4): 78-.
[4] Yanxia Zhao, Huiqing Lian, Chang Tian, Haibo Li, Weiying Xu, Sherub Phuntsho, Kaimin Shih. Surface water treatment benefits from the presence of algae: Influence of algae on the coagulation behavior of polytitanium chloride[J]. Front. Environ. Sci. Eng., 2021, 15(4): 58-.
[5] Xuesong Liu, Jianmin Wang. Algae (Raphidocelis subcapitata) mitigate combined toxicity of microplastic and lead on Ceriodaphnia dubia[J]. Front. Environ. Sci. Eng., 2020, 14(6): 97-.
[6] Yan Zhang, Yuyan Zhang, Xue Li, Xiaohan Zhao, Cosmos Anning, John Crittenden, Xianjun Lyu. Photocatalytic water splitting of ternary graphene-like photocatalyst for the photocatalytic hydrogen production[J]. Front. Environ. Sci. Eng., 2020, 14(4): 69-.
[7] Caiyun Hou, Sen Qiao, Yue Yang, Jiti Zhou. A novel sequence batch membrane carbonation photobioreactor developed for microalgae cultivation[J]. Front. Environ. Sci. Eng., 2019, 13(6): 92-.
[8] Victor M. Deantes-Espinosa, Tian-Yuan Zhang, Xiao-Xiong Wang, Yinhu Wu, Guo-Hua Dao, Hong-Ying Hu. Attached cultivation of Scenedesmus sp. LX1 on selected solids and the effect of surface properties on attachment[J]. Front. Environ. Sci. Eng., 2019, 13(4): 57-.
[9] Xiaomeng Wang, Ning Li, Jianye Li, Junjun Feng, Zhun Ma, Yuting Xu, Yongchao Sun, Dongmei Xu, Jian Wang, Xueli Gao, Jun Gao. Fluoride removal from secondary effluent of the graphite industry using electrodialysis: Optimization with response surface methodology[J]. Front. Environ. Sci. Eng., 2019, 13(4): 51-.
[10] Xinfeng Wang, Lu Lin, Haifeng Lu, Zhidan Liu, Na Duan, Taili Dong, Hua Xiao, Baoming Li, Pei Xu. Microalgae cultivation and culture medium recycling by a two-stage cultivation system[J]. Front. Environ. Sci. Eng., 2018, 12(6): 14-.
[11] Kishore Gopalakrishnan, Javad Roostaei, Yongli Zhang. Mixed culture of Chlorella sp. and wastewater wild algae for enhanced biomass and lipid accumulation in artificial wastewater medium[J]. Front. Environ. Sci. Eng., 2018, 12(4): 14-.
[12] Bai-Hang Zhao, Jie Chen, Han-Qing Yu, Zhen-Hu Hu, Zheng-Bo Yue, Jun Li. Optimization of microwave pretreatment of lignocellulosic waste for enhancing methane production: Hyacinth as an example[J]. Front. Environ. Sci. Eng., 2017, 11(6): 17-.
[13] Jiao Zhang, Zhen Wei, Haifeng Jia, Xia Huang. Factors influencing water quality indices in a typical urban river originated with reclaimed water[J]. Front. Environ. Sci. Eng., 2017, 11(4): 8-.
[14] Yan GUO, Chuanfu WU, Qunhui WANG, Min YANG, Qiqi HUANG, Markus MAGEP, Tianlong ZHENG. Wastewater-nitrogen removal using polylactic acid/starch as carbon source: Optimization of operating parameters using response surface methodology[J]. Front. Environ. Sci. Eng., 2016, 10(4): 6-.
[15] Jingjing ZHAN,Qiao ZHANG,Momei QIN,Yu HONG. Selection and characterization of eight freshwater green algae strains for synchronous water purification and lipid production[J]. Front. Environ. Sci. Eng., 2016, 10(3): 548-558.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed