城市污水处理过程出水氨氮优化控制

doi:10.16183/j.cnki.jsjtu.2020.170

摘要/Abstract

摘要：

为了改善城市污水处理过程出水氨氮的处理效果,提出一种城市污水处理过程出水氨氮优化控制方法.首先,基于机理特性分析影响出水氨氮浓度的性能指标,建立一种基于自适应核函数的性能指标与控制变量的关联模型,并通过粒子群优化算法获取控制变量溶解氧浓度的优化设定值;其次,设计自适应模糊神经网络控制器,完成溶解氧浓度优化设定值的跟踪控制;最后,将出水氨氮优化控制方法应用于基准仿真平台BSM1.实验结果表明,该优化控制方法不仅提高了出水氨氮的去除效果,而且有效降低了能耗.

关键词: 城市污水处理过程, 出水氨氮, 优化控制, 自适应模糊神经网络控制器

Abstract:

To improve the treatment effect of effluent ammonia nitrogen in municipal wastewater treatment process, an optimal control method was proposed in this paper. First, the performance index of effluent ammonia nitrogen concentration was analyzed by using the mechanism characteristics. Then, a relationship model with the adaptive kernel function between the performance index and the control variables was established. Next, a particle swarm optimization algorithm was used to obtain the optimal solutions of dissolved oxygen concentration. After that, an adaptive fuzzy neural network controller was designed to complete the tracking control of dissolved oxygen concentration. Finally, the proposed optimal control method was applied to the benchmark simulation model No.1 (BSM1). The results demonstrated that the proposed optimal control method can not only improve the treatment effect of effluent ammonia nitrogen, but also effectively reduce the energy consumption.

Key words: municipal wastewater treatment process, effluent ammonia nitrogen, optimal control, adaptive fuzzy neural network controller

中图分类号:

TU433

韩红桂, 杨士恒, 张璐, 乔俊飞. 城市污水处理过程出水氨氮优化控制[J]. 上海交通大学学报, 2020, 54(9): 916-923.

HAN Honggui, YANG Shiheng, ZHANG Lu, QIAO Junfei. Optimal Control of Effluent Ammonia Nitrogen for Municipal Wastewater Treatment Process[J]. Journal of Shanghai Jiaotong University, 2020, 54(9): 916-923.

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参考文献 28

[1]	ADAM M R, OTHMAN M H D, SAMAH R A, et al. Current trends and future prospects of ammonia removal in wastewater: A comprehensive review on adsorptive membrane development[J]. Separation and Purification Technology, 2019,213:114-132.
[2]	AOUDJ S, KHELIFA A, DROUICHE N. Removal of fluoride, SDS, ammonia and turbidity from semiconductor wastewater by combined electrocoagulation-electroflotation[J]. Chemosphere, 2017,180:379-387.
[3]	MULAS M, TRONCI S, CORONA F, et al. Predictive control of an activated sludge process: An application to the Viikinm?ki wastewater treatment plant[J]. Journal of Process Control, 2015,35:89-100.
[4]	HAN H G, QIAN H H, QIAO J F. Nonlinear multiobjective model predictive control scheme for wastewater treatment process[J]. Journal of Process Control, 2014,24(3):47-59.
[5]	ZHU Z Y, WANG R Y, LI Y M. Evaluation of the control strategy for aeration energy reduction in a nutrient removing wastewater treatment plant based on the coupling of ASM1 to an aeration model[J]. Biochemical Engineering Journal, 2017,124:44-53.
[6]	?MAND L, CARLSSON B. The optimal dissolved oxygen profile in a nitrifying activated sludge process-comparisons with ammonium feedback control[J]. Water Science and Technology, 2013,68(3):641-649.
[7]	ZULUAGA B C, RUIZ B M, OSPINA A M, et al. A dynamical model of an aeration plant for wastewater treatment using a phenomenological based semi-physical modeling methodology[J]. Computers and Chemical Engineering, 2018,117:420-432.
[8]	BOOG J, KALBACHER T, NIVALA J, et al. Modeling the relationship of aeration, oxygen transfer and treatment performance in aerated horizontal flow treatment wetlands[J]. Water Research, 2019,157:321-334.
[9]	?MAND L, CARLSSON B. Optimal aeration control in a nitrifying activated sludge process[J]. Water Research, 2012,46(7):2101-2110.
[10]	CHEN Q W, WANG Q B, YAN H L, et al. Improve the performance of full-scale continuous treatment of municipal wastewater by combining a numerical model and online sensors[J]. Water Science and Technology, 2018,78(8):1658-1667.
[11]	ODRIOZOLA J, BELTRN S, DALMAU M, et al. Model-based methodology for the design of optimal control strategies in MBR plants[J]. Water Science and Technology, 2017,75(11):2546-2553.
[12]	BISTA K R, KHATIWADA N R, KHYAJU S. Kinetics of organic matter and ammonia removal in horizontal reed beds treating wastewater in Nepal[J]. Water Science and Technology, 2015,72(11):1981-1987.
[13]	LUAN D Y, ZHANG S F, WEI X, et al. Study on mathematical model to predict aerated power consumption in a gas-liquid stirred tank[J]. Results in Physics, 2017,7:4085-4088.
[14]	ZHAO J, WANG X X, LI X Y, et al. Advanced nutrient removal from ammonia and domestic wastewaters by a novel process based on simultaneous partial nitrification-anammox and modified denitrifying phosphorus removal[J]. Chemical Engineering Journal, 2018,354:589-598.
[15]	OZTURK M C, SERRAT F M, TEYMOUR F. Optimization of aeration profiles in the activated sludge process[J]. Chemical Engineering Science, 2016,139:1-14.
[16]	ANTONIO F T, LORENA P S. Optimal model-based aeration control policies in a sequencing batch reactor[J]. Computers and Chemical Engineering, 2016,85:124-135.
[17]	KIM H, KIM Y, HOANG T Q, et al. Application of real-time feedback control strategies based on effluent NH4-N and NOX-N concentrations in an A2/O process[J]. Korean Journal of Chemical Engineering, 2013,30(8):1578-1587.
[18]	FERNáNDEZ F J, CASTRO M C, RODRIGO MA, et al. Reduction of aeration costs by tuning a multi-set point on/off controller: A case study[J]. Control Engineering Practice, 2011,19(10):1231-1237.
[19]	HAN H G, WU X L, LIU Z, et al. Design of self-organizing intelligent controller using fuzzy neural network[J]. IEEE Transactions on Fuzzy Systems, 2018,26(5):3097-3111.
[20]	张晓迪, 蔡云泽, 何星, 等. 基于神经网络的不稳定时滞对象控制[J]. 上海交通大学学报, 2014,48(7):1033-1038.
	ZHANG Xiaodi, CAI Yunze, HE Xing, et al. Neural network control unstable processes with time delay[J]. Journal of Shanghai Jiao Tong University, 2014,48(7):1033-1038.
[21]	张光明, 李柠, 李少远. 一种数据驱动的预测控制器性能监控方法[J]. 上海交通大学学报, 2011,45(8):1113-1118.
	ZHANG Guangming, LI Ning, LI Shaoyuan. A data-driven approach for model predictive control performance monitoring[J]. Journal of Shanghai Jiao Tong University, 2011,45(8):1113-1118.
[22]	ZHANG Z J, KUSIAK A, ZENG Y H, et al. Modeling and optimization of a wastewater pumping system with data-mining methods[J]. Applied Energy, 2016,164:303-311.
[23]	HUANG M Z, WAN J Q, MA Y W, et al. Control rules of aeration in a submerged biofilm wastewatertreatment process using fuzzy neural networks[J]. Expert Systems with Applications, 2009,36(7):10428-10437.
[24]	KOTZAPETROS A D, PARASKEVAS P A, STASINAKIS A S. Design of a modern automatic control system for the activated sludge process in wastewater treatment[J]. Chinese Journal of Chemical Engineering, 2015,23(8):1340-1349.
[25]	?MAND L, OLSSON G, CARLSSON B. Aeration control: A review[J]. Water Science and Technology, 2013,67(11):2374-2398.
[26]	HENZE M, GUJER W, MINO T, et al. Activated sludge models ASM1, ASM2, ASM2d and ASM3[M]. London, UK: IWA Publishing, 2000.
[27]	ZHAN J X, IKEHATA M, MAYUZUMI M, et al. An aeration control strategy for oxidation ditch processes based on online oxygen requirement estimation[J]. Water Science and Technology, 2013,68(1):76-82.
[28]	QIAO J F, ZHANG W. Dynamic multi-objective optimization control for wastewater treatment process[J]. Neural Computing and Applications, 2018,29(11):1261-1271.

天气	控制方法	AE/(kW·h^-1)	S_NH,avg/(mg·L^-1)
晴天	开环	3340	4.46
	PID	3676	2.34
	AE优化	3476	1.89
雨天	开环	3341	4.55
	PID	3665	2.56
	AE优化	3425	1.95
暴雨	开环	3340	4.52
	PID	3688	2.48
	AE优化	3443	1.97

天气	控制器	ISE	IAE
晴天	PID	5.26×10^-3	0.450
	BP	4.29×10^-4	0.071
	FNNC	3.29×10^-4	0.051
雨天	PID	5.28×10^-3	0.470
	BP	4.18×10^-4	0.074
	FNNC	3.27×10^-4	0.055
暴雨	PID	5.16×10^-3	0.430
	BP	4.25×10^-4	0.078
	FNNC	3.25×10^-4	0.054