Please wait a minute...
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

Front. Environ. Sci. Eng.    2015, Vol. 9 Issue (4) : 694-701    https://doi.org/10.1007/s11783-014-0732-3
RESEARCH ARTICLE
Modification of the activated sludge model for chemical dosage
Shuai MA1,Siyu ZENG1,Xin DONG1,*(),Jining CHEN1,Gustaf OLSSON2
1. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
2. Department of Industrial Electrical Engineering and Automation, Lund University, Lund SE-22100, Sweden
 Download: PDF(228 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Full-scale experiments have been carried out to adapt the activated sludge model ASM2d to include the influence of metal dosage (Fe3+ and Al3+) for phosphorus removal. Phosphorus removal rates, nitrification rates, as well as pH and sludge settling performance, were evaluated as functions of the metal dosages. Furthermore, models relating certain parameters to the dosage of chemicals have been derived. Corresponding parameters in the ASM2d and the secondary settler models, included in the Benchmark Simulation Model No 1 (BSM1), have been modified to take the metal influence into consideration. Based on the effluent limits and penalty policy of China, an equivalent evaluation method was derived for the total cost assessment. A large number of 300-day steady-state and 14-day open-loop dynamic simulations were performed to demonstrate the difference in behavior between the original and the modified BSM1. The results show that 1) both in low and high mole concentrations, Fe3+ addition results in a higher phosphorus removal rate than Al3+; 2) the sludge settling velocity will increase due to the metal addition; 3) the respiration rate of the activated sludge is decreased more by the dosage of Al3+ than Fe3+; 4) the inhibition of Al3+ on the nitrification rate is stronger than that of Fe3+; 5) the total operating cost will reach the minimum point for smaller dosages of Fe3+, but always increase with Al3+ addition.

Keywords chemical precipitation      benchmark simulation model      phosphorus removal      respiratory rate      sludge settling      activated sludge model     
Corresponding Author(s): Xin DONG   
Online First Date: 11 June 2014    Issue Date: 25 June 2015
 Cite this article:   
Siyu ZENG,Xin DONG,Jining CHEN, et al. Modification of the activated sludge model for chemical dosage[J]. Front. Environ. Sci. Eng., 2015, 9(4): 694-701.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-014-0732-3
https://academic.hep.com.cn/fese/EN/Y2015/V9/I4/694
pollutant α /(CNY·kg-1) βj CLj/(mg·L-1)
NH3 2.1 0.8 5
TN 2.1 1.6 15
TP 2.1 0.25 0.5
Tab.1  Coefficients and discharge limits used for EF calculation
Fig.1  Phosphorus removal rates of Fe3+ and Al3+ in the XHM plant (a) and modified PRR lines (b). The parameter modification is based on the average values of the 3 sets of fitted parameters. The top X-axis indicates the mass concentrations corresponding to dosing ratio
Fig.2  Instantaneous respiratory rates of activated sludge from aeration tanks and fitted results of XHM (a) and LC (b) plants. The metal-RR lines are based on the average fitted parameters
parameter Fe3+ in XHM Al3+ in XHM Fe3+ in LC Al3+ in LC
aRR /(L·mg-1) 7.5×10-2 (1.6×10-2) 6.3×10-2(9.2×10-3) 4.3×10-2(4.0×10-3) 8.3×10-2(1.6×10-2)
bRR 6.1×10-1(9.2×10-3) 3.8×10-1(2.7×10-2) 2.7×10-1(1.5×10-2) 1.4×10-1(1.7×10-2)
Tab.2  Fitted parameters of the RR exponential model
Fig.3  SV30 experiment data and fitting results of Fe3+ (a) and Al3+ (b). The metal-SV30 lines are based on the average fitted parameters
Fig.4  Steady-state ((a) Fe3+, (c) Al3+) and dynamic open-loop ((b) Fe3+, (d) Al3+) simulation results of effluent nutrient concentrations using the modified BSM compared with the original BSM. The arrows indicate the minimum cost points
1 Henze M, Gujer W, Mino T, van Loosdrecht M. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Task Group on Mathematical Modelling for Design and Operation of Biological Wastewater Treatment, IAWPRC Scientific and Technical Reports No. 9. London: IWA Publishing, 2000
2 Batstone D J, Keller J, Angelidaki I, Kalyuzhnyi S V, Pavlostathis S G, Rozzi A, Sanders W T M, Siegrist H, Vavilin V A. The IWA Anaerobic Digestion Model No 1 (ADM1). Water Science & Technology, 2002, 45(10): 65–73
pmid: 12188579
3 Jeppsson U, Alex J, Batstone D J, Benedetti L, Comas J, Copp J B, Corominas L, Flores-Alsina X, Gernaey K V, Nopens I, Pons M N, Rodríguez-Roda I, Rosen C, Steyer J P, Vanrolleghem P A, Volcke E I P, Vrecko D. Benchmark simulation models, quo vadis? Water Science & Technology, 2013, 68(1): 1–15
https://doi.org/10.2166/wst.2013.246 pmid: 23823534
4 Olsson G. ICA and me—A subjective review. Water Research, 2012, 46(6): 1585–1624
https://doi.org/10.1016/j.watres.2011.12.054 pmid: 22284982
5 Shen W, Chen X, Corriou J P. Application of model predictive control to the BSM1 benchmark of wastewater treatment process. Computers & Chemical Engineering, 2008, 32(12): 2849–2856
https://doi.org/10.1016/j.compchemeng.2008.01.009
6 Corriou J P, Pons M N. Model predictive control of wastewater treatment plants: Application to the BSM1 benchmark. Computer Aided Chemical Engineering, 2004, 18: 625–630
https://doi.org/10.1016/S1570-7946(04)80170-6
7 Stare A, Vrecko D, Hvala N, Strmcnik S. Comparison of control strategies for nitrogen removal in an activated sludge process in terms of operating costs: a simulation study. Water Research, 2007, 41(9): 2004–2014
https://doi.org/10.1016/j.watres.2007.01.029 pmid: 17346768
8 Vreck D, Gernaey K V, Rosen C, Jeppsson U. Benchmark simulation Model no 2 in Matlab-simulink: towards plant-wide WWTP control strategy evaluation. Water Science & Technology, 2006, 54(8): 65–72
https://doi.org/10.2166/wst.2006.773 pmid: 17163014
9 Maere T, Verrecht B, Moerenhout S, Judd S, Nopens I. BSM-MBR: a benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Research, 2011, 45(6): 2181–2190
https://doi.org/10.1016/j.watres.2011.01.006 pmid: 21329957
10 Gernaey K V, Jorgensen S B. Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes. Control Engineering Practice, 2004, 12(3): 357–37310.1016/S0967-0661(03)00080-7
11 Guo G, Wang Y, Wang C, Wang H, Pan M, Chen S. Short-term effects of excessive anaerobic reaction time on anaerobic metabolism of denitrifying polyphosphate-accumulating organisms linked to phosphorus removal and N2O production. Frontiers of Environmental Science & Engineering, 2013, 7(4): 616–624
https://doi.org/10.1007/s11783-013-0505-4
12 Ma B, Wang S, Zhu G, Ge S, Wang J, Ren N, Peng Y. Denitrification and phosphorus uptake by DPAOs using nitrite as an electron acceptor by step-feed strategies. Frontiers of Environmental Science & Engineering, 2013, 7(2): 267–272
https://doi.org/10.1007/s11783-012-0439-2
13 Shijian G, Yongzhen P, Congcong L, Shuying W. Practical consideration for design and optimization of the step feed process. Frontiers of Environmental Science & Engineering, 2013, 7(1): 135–142
https://doi.org/10.1007/s11783-012-0454-3
14 Cao G, Wang S, Peng Y, Miao Z. Biological nutrient removal by applying modified four step-feed technology to treat weak wastewater. Bioresource Technology, 2013, 128(0): 604–611
https://doi.org/10.1016/j.biortech.2012.09.078 pmid: 23211487
15 Zhang Z, Li Y, Wei L, Lü Y, Wang M, Gao B. Effect of ferric chloride on the properties of biological sludge in co-precipitation phosphorus removal process. Chinese Journal of Chemical Engineering, 2013, 21(5): 564–568
https://doi.org/10.1016/S1004-9541(13)60511-X
16 Zhaoxu P, Yongzhen P, Zhenbo Y, Xuliang L, Xiaoling L, Randeng W. Control of sludge settleability and nitrogen removal under low dissolved oxygen condition. Frontiers of Environmental Science and Engineering, 2012, 6(6): 884–891
17 Henze M, Gujer W, Mino T, Matsuo T, Wentzel M C, Marais G V R, Van Loosdrecht M C M. Activated Sludge Model No.2d, ASM2d. Water Science & Technology, 1999, 39(1): 165–182
https://doi.org/10.1016/S0273-1223(98)00829-4
18 Liu Y, Shi H, Li W, Hou Y, He M. Inhibition of chemical dose in biological phosphorus and nitrogen removal in simultaneous chemical precipitation for phosphorus removal. Bioresource Technology, 2011, 102(5): 4008–4012
https://doi.org/10.1016/j.biortech.2010.11.107 pmid: 21215613
19 Liwarska-Bizukojc E, Bizukojc M. A new approach to determine the kinetic parameters for nitrifying microorganisms in the activated sludge systems. Bioresource Technology, 2012, 109(0): 21–25
https://doi.org/10.1016/j.biortech.2012.01.011 pmid: 22285297
20 Ak?a L, Kinaci C, Karpuzcu M. A model for optimum design of activated sludge plants. Water Research, 1993, 27(9): 1461–1468
https://doi.org/10.1016/0043-1354(93)90026-E
21 Takacs I, Patry G G, Nolasco D. A dynamic-model of the clarification thickening process. Water Research, 1991, 25(10): 1263–1271
https://doi.org/10.1016/0043-1354(91)90066-Y
22 Gernaey K V, Jeppsson U, Batstone D J, Ingildsen P. Impact of reactive settler models on simulated WWTP performance. Water Science & Technology, 2006, 53(1): 159–167
https://doi.org/10.2166/wst.2006.018 pmid: 16532746
23 Ostace G S, Baeza J A, Guerrero J, Guisasola A, Cristea V M, Agachi P S, Lafuente J. Development and economic assessment of different WWTP control strategies for optimal simultaneous removal of carbon, nitrogen and phosphorus. Computers & Chemical Engineering, 2013, 53: 164–177
https://doi.org/10.1016/j.compchemeng.2013.03.007
24 Vanrolleghem P A, Gillot S. Robustness and economic measures as control benchmark performance criteria. Water Science & Technology, 2002, 45(4 - 5 ): 117–126
pmid: 11936624
25 Yang L, Zeng S, Chen J, He M, Yang W. Operational energy performance assessment system of municipal wastewater treatment plants. Water Science & Technology, 2010, 62(6): 1361–1370
https://doi.org/10.2166/wst.2010.394 pmid: 20861551
26 Yu F, Niu K Y, Cao D, Wang J N. Design for a municipal wastewater treatment charge standard system based on cost accounting. China Environmental Science, 2011, 31(9): 1578–1584
27 Vanrolleghem P A, Jeppsson U, Carstensen J, Carlsson B, Olsson G. Integration of wastewater treatment plant design and operation-A systematic approach using cost functions. Water Science & Technology, 1996, 34(3-4): 159–171
https://doi.org/10.1016/0273-1223(96)00568-9
[1] Lingchen Kong, Xitong Liu. Emerging electrochemical processes for materials recovery from wastewater: Mechanisms and prospects[J]. Front. Environ. Sci. Eng., 2020, 14(5): 90-.
[2] Quan Zheng, Minglu Zhang, Tingting Zhang, Xinhui Li, Minghan Zhu, Xiaohui Wang. Insights from metagenomic, metatranscriptomic, and molecular ecological network analyses into the effects of chromium nanoparticles on activated sludge system[J]. Front. Environ. Sci. Eng., 2020, 14(4): 60-.
[3] Alisa Salimova, Jian’e Zuo, Fenglin Liu, Yajiao Wang, Sike Wang, Konstantin Verichev. Ammonia and phosphorus removal from agricultural runoff using cash crop waste-derived biochars[J]. Front. Environ. Sci. Eng., 2020, 14(3): 48-.
[4] Xiaoya Liu, Yu Hong, Peirui Liu, Jingjing Zhan, Ran Yan. Effects of cultivation strategies on the cultivation of Chlorella sp. HQ in photoreactors[J]. Front. Environ. Sci. Eng., 2019, 13(5): 78-.
[5] Weihua Zhao, Meixiang Wang, Jianwei Li, Yu Huang, Baikun Li, Cong Pan, Xiyao Li, Yongzhen Peng. Optimization of denitrifying phosphorus removal in a pre-denitrification anaerobic/anoxic/post-aeration+ nitrification sequence batch reactor (pre-A2NSBR) system: Nitrate recycling, carbon/nitrogen ratio and carbon source type[J]. Front. Environ. Sci. Eng., 2018, 12(5): 8-.
[6] Dongliang Du, Chuanyi Zhang, Kuixia Zhao, Guangrong Sun, Siqi Zou, Limei Yuan, Shilong He. Effect of different carbon sources on performance of an A2N-MBR process and its microbial community structure[J]. Front. Environ. Sci. Eng., 2018, 12(2): 4-.
[7] Yandong Yang,Liang Zhang,Hedong Shao,Shujun Zhang,Pengchao Gu,Yongzhen Peng. Enhanced nutrients removal from municipal wastewater through biological phosphorus removal followed by partial nitritation/anammox[J]. Front. Environ. Sci. Eng., 2017, 11(2): 8-.
[8] Yuankai ZHANG,Hongchen WANG,Lu QI,Guohua LIU,Zhijiang HE,Songzhu JIANG. Simple model of sludge thickening process in secondary settlers[J]. Front. Environ. Sci. Eng., 2016, 10(2): 319-326.
[9] Fenglin LIU,Jiane ZUO,Tong CHI,Pei WANG,Bo YANG. Removing phosphorus from aqueous solutions by using iron-modified corn straw biochar[J]. Front. Environ. Sci. Eng., 2015, 9(6): 1066-1075.
[10] Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG. Simulation of long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian inference[J]. Front. Environ. Sci. Eng., 2015, 9(3): 534-544.
[11] Gang GUO, Yayi WANG, Chong WANG, Hong WANG, Mianli PAN, Shaowei CHEN. Short-term effects of excessive anaerobic reaction time on anaerobic metabolism of denitrifying polyphosphate- accumulating organisms linked to phosphorus removal and N2O production[J]. Front Envir Sci Eng, 2013, 7(4): 616-624.
[12] Ming HUA, Lili XIAO, Bingcai PAN, Quanxing ZHANG. Validation of polymer-based nano-iron oxide in further phosphorus removal from bioeffluent: laboratory and scaled-up study[J]. Front Envir Sci Eng, 2013, 7(3): 435-441.
[13] Bin MA, Shuying WANG, Guibing ZHU, Shijian GE, Junmin WANG, Nanqi Ren, Yongzhen PENG. Denitrification and phosphorus uptake by DPAOs using nitrite as an electron acceptor by step-feed strategies[J]. Front Envir Sci Eng, 2013, 7(2): 267-272.
[14] Shijian GE, Yongzhen PENG, Congcong LU, Shuying WANG. Practical consideration for design and optimization of the step feed process[J]. Front Envir Sci Eng, 2013, 7(1): 135-142.
[15] Jianhua WANG, Yongzhen PENG, Yongzhi CHEN. Advanced nitrogen and phosphorus removal in A2O-BAF system treating low carbon-to-nitrogen ratio domestic wastewater[J]. Front Envir Sci Eng Chin, 2011, 5(3): 474-480.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed