SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与KLa确定

doi:10.3969/j.issn.0438-1157.2012.12.044

化工学报 ›› 2012, Vol. 63 ›› Issue (12): 4048-4054.DOI: 10.3969/j.issn.0438-1157.2012.12.044

SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与K_La确定

朱奥, 郭建华, 王淑莹, 彭永臻

北京工业大学北京市水质科学与水环境恢复工程重点实验室, 北京市污水脱氮除磷处理与过程控制工程技术研究中心, 北京100124

收稿日期:2012-04-16 修回日期:2012-08-02 出版日期:2012-12-05 发布日期:2012-09-04
通讯作者: 彭永臻
作者简介:朱奥(1987-),男,硕士研究生。
基金资助:
国家自然科学基金项目(21177005);北京市属高等学校人才强教计划资助项目。

Dynamic DO simulation for aerobic nitrification process in SBR with constant aeration intensity: model identification and K_La determination

ZHU Ao, GUO Jianhua, WANG Shuying, PENG Yongzhen

Key Laboratory of Beijing for Water Quality Science and Water Environmental Recovery Engineering, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, China

Received:2012-04-16 Revised:2012-08-02 Online:2012-12-05 Published:2012-09-04
Supported by:
supported by the National Natural Science Foundation of China(21177005).

摘要/Abstract

摘要： 通过简化活性污泥法1号模型(activated sludge model No.1,ASM1)建立两步硝化反应的数学模型,实现了对序批式反应器(sequencing batch reactor,SBR)恒曝气量好氧过程中溶解氧(dissolved oxygen,DO)动态变化过程的数学模拟,模型辨识科学地区分了可以直接取值的参数包括产率系数、DO饱和常数(或底物饱和常数)和需要重新估计的参数。采用文献推荐参数值模拟了过程中主要状态变量的动力学过程,模拟结果呈现出了多个DO平台,这与实际反应结果数据相符,验证了所建模型的正确性。优化实验设计,获取了典型SBR恒曝气好氧硝化过程动态DO数据,通过理论分析和对数据进行二阶微分处理提出了确定总氧传递系数K_La和相对饱和溶解氧S_O^eq的简单方法,为后续参数估计奠定了基础。

关键词: DO模拟, 序批式反应器, 硝化模型, 结构辨识, 优化实验设计

Abstract: A two-step nitrification model was built by simplifying the standard activated sludge model No.1,and dynamic dissolved oxygen(DO)simulation can be done for aerobic nitrification process in a sequencing batch reactor(SBR)with constant aeration intensity.The parameters in the model could be distinguished into two groups by model identification:in a group parameter values can be directly obtained,including yield coefficient,DO saturation coefficient(or substrate coefficient),and in other group parameter values needed to be estimated by optimization algorithm.Adopting the parameter values recommended by literatures,dynamic processes were simulated for important process variables,which revealed multi-DO levels and fitted well with the real response trend in SBR operation.Optimal experimental design method was employed for obtaining dynamic DO data of aerobic nitrification process in the SBR with typical aeration intensity,from which the values of K_La and S_O^eq could be determined by theoretical analysis and second order differential treatment of these data.Then,further parameter estimation could be optimal based on the model identification and K_La determination.

Key words: DO simulation, sequencing batch reactor(SBR), nitrification model, structural identification, optimal experimental design(OED)

中图分类号:

X703

朱奥, 郭建华, 王淑莹, 彭永臻. SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与K_La确定[J]. 化工学报, 2012, 63(12): 4048-4054.

ZHU Ao, GUO Jianhua, WANG Shuying, PENG Yongzhen. Dynamic DO simulation for aerobic nitrification process in SBR with constant aeration intensity: model identification and K_La determination[J]. CIESC Journal, 2012, 63(12): 4048-4054.

参考文献 16

[1]	Marsili-Libelli S.Control of SBR switching by fuzzy pattern recognition[J].Water Research,2006,40(5):1095-1107
[2]	Yang Q,Gu S B,Peng Y Z,Wang S Y,et al.Progress in the development of control strategies for the SBR process[J].Clean-Soil Air Water,2010,38(8):732-749
[3]	Guo J,Peng Y Z,Wang S Y,Zheng Y A,et al.Long-term effect of dissolved oxygen on partial nitrification performance and microbial community structure[J].Bioresource Technology,2009,100(11):2796-2802
[4]	Gao D W,Peng Y Z,Wu W M.Kinetic model for biological nitrogen removal using shortcut nitrification-denitrification process in sequencing batch reactor[J].Environmental Science & Technology,2010,44(13):5015-5021
[5]	Iacopozzi I,Innocenti V,Marsili-Libelli S,Giusti E.A modified activated sludge model No.3(ASM3)with two-step nitrification-denitrification[J].Environmental Modelling & Software,2007,22(6):847-861
[6]	Kaelin D,Manser R,Rieger L,Eugster J,et al. Extension of ASM3 for two-step nitrification and denitrification and its calibration and validation with batch tests and pilot scale data[J].Water Research,2009,43(6):1680-1692
[7]	Holenda B,Domokos E,Redey A,Fazakas J.Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control[J].Computers & Chemical Engineering,2008,32(6):1270-1278
[8]	Rodriguez-Fernandez M,Mendes P,Banga J R.A hybrid approach for efficient and robust parameter estimation in biochemical pathways[J].Biosystems,2006,83(2/3):248-265
[9]	Checchi N,Giusti E,Marsili-Libelli S.PEAS:a toolbox to assess the accuracy of estimated parameters in environmental models[J].Environmental Modelling & Software,2007,22(6):899-913
[10]	Marsili-Libelli S,Guerrizio S,Checchi N.Confidence regions of estimated parameters for ecological systems[J].Ecological Modelling,2003,165(2/3):127-146
[11]	Britta Petersen,Krist Gernaey,Martijn Devisscher,Denis Dochain,et al.A simplified method to assess structurally identifiable parameters in Monod-based activated sludge models[J].Water Research,2003,37(12):2893-2904
[12]	Denis Dochain,Peter A Vanrolleghem.Dynamical Modelling and Estimation in Wastewater Treatment Processes[M].London:IWA,2001:115
[13]	Ir Dirk De Pauw.Optimal experimental design for calibration of bioprocess models:a validated software toolbox.Belgium:Ghent University,2005
[14]	Manser R,Muche K,Gujer W,Siegrist H.A rapid method to quantify nitrifiers in activated sludge[J].Water Research,2005,39(8):1585-1593
[15]	Henze M,Grady C P L Jr,Gujer W,Marais G V R,Matsuo T.Activated sludge model No 1:IAWPRC Sci. and Tech. Report[R].London:IAWPRC,1987
[16]	Koch G,Kuhni M,Gujer W,Siegrist H.Calibration and validation of activated sludge model No.3 for Swiss municipal wastewater[J].Water Research,2000,34(14):3580-3590

SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与K_La确定

Dynamic DO simulation for aerobic nitrification process in SBR with constant aeration intensity: model identification and K_La determination

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SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与KLa确定

Dynamic DO simulation for aerobic nitrification process in SBR with constant aeration intensity: model identification and KLa determination

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SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与K_La确定

Dynamic DO simulation for aerobic nitrification process in SBR with constant aeration intensity: model identification and K_La determination