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摘要: 以抑制出水氨氮浓度、总氮浓度峰值和降低能耗为目标，提出污水处理决策优化控制方法.首先利用神经网络建立出水氨氮和总氮预测模型；其次使用多目标进化算法得到溶解氧浓度和硝态氮浓度设定值；最后，根据出水氨氮和总氮浓度预测结果选择控制策略（优化控制和抑制控制）.以仿真基准模型（BSM1）为平台，采用提出的决策优化控制方法进行控制，实验结果表明，该控制方法有效抑制了出水氨氮和总氮浓度峰值，出水超标时间和能耗明显少于所对比决策控制方法.Abstract: In order to inhibit the peak of ammonia nitrogen (SNH, e) and total nitrogen (SNtot, e) concentrations in effluent and reduce energy consumption, we present in this paper a decision and optimization control method. Firstly, we establish the prediction models of SNH, e and SNtot, e with neural network. Secondly, we optimize the set points of dissolved oxygen concentration and nitrate nitrogen concentration with multiobjective evolutionary algorithm. Lastly, select control strategy (optimal control strategy or inhibitory control strategy) based on the outcome of prediction models. Evaluation is carried out with the Benchmark Simulation Model No.1. The results show that the proposed method restrains the peaks of SNH, e and SNtot, e effectively while the percentages of time of SNH, e and SNtot, e violations are less than those of the compared inhibitory control methods, and that the energy consumption using the proposed method is less than that using the counterpart inhibitory control method significantly.
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Key words:
- Wastewater treatment /
- optimal control /
- inhibitory control /
- prediction model
1) 本文责任编委 赵千川 -
图 1 14天不同区域氨氮浓度曲线
Fig. 1 Ammonia nitrogen concentration curve in different regions within 14 days
图 8 $S_{\rm {NO, 2}}$设定值及跟踪曲线
Fig. 8 Optimization and tracking results of $S_{\rm {NO, 2}}$
图 9 $S_{\rm {O, 5}}$设定值及跟踪曲线
Fig. 9 Optimization and tracking results of $S_{\rm {O, 5}}$
图 11 决策优化控制与优化控制$S_{\rm {NH, e}}$变化曲线
Fig. 11 The curves of $S_{\rm {NH, e}}$ with decision and optimization control system and optimization control system
图 12 决策优化控制与优化控制$S_{\rm {Ntot, e}}$变化曲线
Fig. 12 The curves of $S_{\rm {Ntot, e}}$ with decision and optimization control system and optimization control system
图 13 $S_{\rm {O, 5}}$设定值及跟踪曲线
Fig. 13 Optimization and tracking results of $S_{\rm {O, 5}}$
图 14 $S_{\rm {NO, 2}}$设定值及跟踪曲线
Fig. 14 Optimization and tracking results of $S_{\rm {NO, 2}}$
表 1 不同区域到入水氨氮浓度最大时刻滞后时间(h)
Table 1 The time lag from inflow to different regions (h)
第二分区 第五分区 出水 第1天 0.2375 1.1000 4.4375 第2天 0.2375 1.3250 4.5750 第3天 0.2375 1.1625 4.6000 第4天 0.2375 1.3500 4.6875 第5天 0.2375 1.1500 4.6500 第6天 0.4875 1.5625 5.2875 第7天 0.4875 1.6750 5.2750 第8天 0.2375 1.1250 4.4875 第9天 0.2375 1.3250 4.5750 第10天 0.2375 1.1625 4.6000 第11天 0.2375 1.3500 4.6875 第12天 0.2375 1.1500 4.6500 第13天 0.4875 1.5625 5.2875 第14天 0.4875 1.6750 5.2750 
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表 2 预测模型测试均方根误差(10次实验平均值)
Table 2 Test RMSE of prediction model (mean value of ten test results)
方法 氨氮预测模型RMSE 总氮预测模型RMSE 决策优化控制 0.4241 0.3506 Santín[10] 0.9771 0.7515
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表 3 SO,5模糊跟踪控制器模糊规则
Table 3 Fuzzy rules of $S_{\rm {O, 5}}$ fuzzy controller
E EC NB NS ZO PS PB NB PB PB PB PM ZO NM PB PB PM PS ZO NS PM PM PS ZO NS ZO PM PS ZO NS NS PS PS ZO NS NM NM PS ZO NS NM NB NB PB ZO NM NB NB NB
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表 4 出水水质限制
Table 4 Effluent quality limits
水质 上限值(mg/l) $S_{\rm {NH, e}}$ 4 $S_{\rm {Ntot, e}}$ 18 TSS 30 BOD$_5$ 10 COD 100
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表 5 不同算法控制效果比较(出水指标、能耗和水质)
Table 5 Performance comparison for different control algorithms (effluent parameters, $OCI$ and $EQ$)
算法 EC $OCI$ (kWh/d) $EQ$ (kg poll.Units/d) $S_{\rm NH}$ (mg/l) $S_{\rm Ntot}$ (mg/l) TSS (mg/l) BOD$_5$ (mg/l) COD (mg/l) F1 2.3037 16.8006 12.6219 2.6763 47.5114 3 909.5 6 080.9 NSGA2-DLS 2.4389 17.5298 12.6200 2.6791 47.5189 3 688.1 6 203.9 DPSO[18] 3.2387 14.9184 12.6227 2.6894 47.5503 3 702.3 6 180.3 APSO[19] 3.1398 14.5995 12.9990 2.7660 48.0766 3 700.4 6 198.7 ESN[20] 2.8723 15.6014 12.5917 2.6784 47.5114 3 756.8 6 134.5
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表 6 不同$S_{\rm{NH, e}}$和$S_{\rm {Ntot, e}}$峰值抑制方法效果对比
Table 6 Performance comparison for different $S_{\rm {NH, e}}$ and $S_{\rm {Ntot, e}}$ peak suppression methods
方法 $OCI$
(KWh/d)$EQ$ (kg poll.Units/d) $P$($S_{\rm{NH, e}}$)
(%)$P$($S_{\rm {Ntot, e}}$)
(%)决策优化控制 5 526.7 5 797.1 0 0 F2 6 268.6 5 350.1 0.74 0.77 Jeppsson 9 447.24 5 577.97 0.41 1.18 Nopens 9 348 5 447 0.92 * Flores-Alsina 8 024.5 5 022.5 0.2 0.25 Santín 6 289.59 5 318.95 0.15 0.0046
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