Perspectives on Optimal Setting Methods for Municipal Wastewater Treatment Processes
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摘要: 城市污水处理过程优化运行的目标是保证出水水质达标, 降低运行成本. 为了实现该目标, 需要动态更新污水处理过程操作变量的最优设定值. 由于城市污水处理过程具有多变量、多冲突、多目标、多约束、动态、时变等特点, 如何设计精确的污水处理过程运行指标模型, 如何优化过程操作变量的最优设定值, 是实现城市污水处理过程优化运行亟待解决的难题. 本文梳理了城市污水处理过程优化设定方法的研究进展. 首先, 介绍了城市污水处理过程特性和过程优化设定问题; 其次, 分别概述了基于机理和基于数据驱动的城市污水处理过程运行指标建模方法; 然后, 分别讨论了城市污水处理过程单运行指标和多运行指标的操作变量设定值寻优算法; 最后, 展望了城市污水处理过程优化设定问题的未来研究方向.Abstract: In municipal wastewater treatment processes (MWWTPs), the object of optimal operation is to guarantee water quality standard and reduce operation cost. To achieve these goals, it is necessary to update the optimal set-point values of operation variables in MWWTPs in real time. However, the MWWTP is a complex system, which has multi-variables, multi-conflicts, multi-objectives, multi-constraints, dynamics and time-varying delays. Therefore, to realize the optimal operation of MWWTPs, two important problems are how to design the accurate operation indices models of MWWTPs and how to optimize the set-point values of operation variables. In this paper, some existing optimal setting methods of MWWTPs are reviewed. Firstly, the characteristics of MWWTPs and the optimal setting problem are introduced. Secondly, the mechanism-based and data-driven operation indices modeling methods of MWWTPs are described, respectively. Then, the optimization algorithms for single operation variables and multiple operation variables are given, respectively. Finally, some future work about the optimal setting methods of MWWTPs are discussed.
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图 2 基于数据驱动的城市污水处理过程运行指标模型设计框架
Fig. 2 Data-driven based operation indices modeling framework of MWWTPs
图 3 城市污水处理过程操作变量设定值寻优算法设计框架
Fig. 3 Optimization algorithms designing framework for set-point values of operation variables in MWWTPs
表 1 基于机理的城市污水处理过程运行指标建模方法比较
Table 1 Comparison of mechanism-based operation indices modeling methods in MWWTPs
文献 过程运行指标 建模方法 是否实际应用 优缺点 Pallavhee等[31] 出水 COD、出水氨氮 ASM1 否 在工程设计、工艺改造、在线监测方面
有着广泛应用. 但是涉及反应过程复杂、
参数众多且辨识困难、模型阶次较高,
不易直接进行污水处理过程运行指标
的设计、优化和控制.Mannina等[32] 出水 COD、出水 TSS、出水总氮 ASM1、ASM2、GIUE 是 彭永臻等[33] 过程运行费用 活性污泥反应机理 否 El Shorbagy等[34] 脱氮过程能耗 ASM1 否 Sun等[35] 好氧池曝气能耗 ASM2 是 Chen等[36] 出水水质、能耗 ASM2d 否 Shen等[49] 出水水质 BSM1 否 适应于评价优化算法及控制策略.
但是忽略外界干扰因素, 难以准确
反映真实污水处理过程.Nopens等[50] 出水水质 BSM2 否 Maere等[51] 曝气能耗 BSM1 是 王藩等[52] 出水水质、能耗 BSM1 否 Sweetapple等[53] 出水水质、操作成本、温室气体排放 BSM2 否 Plósz[54] 出水水质 反硝化过程动力学 否 能够简单描述污水处理过程化学、
物理、生化机理, 但是难以保证
模型准确性.De Gussem等[55] 曝气能耗 流体动力学、ASM2d 是 Yang等[56] 出水水质、曝气能耗 流体动力学 是 
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表 2 基于数据驱动的城市污水处理过程运行指标建模方法比较
Table 2 Comparison of data-driven based operation indices modeling methods of MWWTPs
文献 过程运行指标 建模方法 是否实际应用 优缺点 Yoo等[63] 出水 BOD 主元分析方法 是 具有计算简单、快速的优点.
但是要建立表征因果关系的准确模型,
模型精度不够理想.Dürrenmatt等[64] 出水水质 最小二乘回归分析、随机树 是 Zeng等[65] 泵送能耗、泵送系统流量 混合整数非线性回归、贪婪电磁像算法 否 Filipe等[66] 泵送能耗 自回归分析 是 Asadi等[67] 出水水质、曝气能耗 聚类分析、多元自适应回归分析 是 Manu等[75] 出水水质 支持向量机、自适应模糊推理 否 有较强的非线性系统拟合能力.
但是仅包含单个过程运行指标,
难以反映污水处理全流程动态特性.Qiao等[76] 出水总磷 深度信念网络 否 Chen等[77] 出水水质 专家知识推理、反向传播神经网络 是 蒙西等[78] 出水 BOD 类脑模块化神经网络 否 王丽娟[79] 曝气能耗 神经网络、主元分析 否 Zhang等[80] 泵送能耗 神经网络 是 Torregrossa等[81] 泵送能耗 模糊逻辑 是 Qiao等[82] 曝气能耗、出水水质 模糊神经网络 否 利于评价全流程的污水处理过程,
便于优化多个操作变量的设定值.Zhang等[83] 每日能耗、出水水质 支持向量机、反向传播算法 否 韩红桂等[84] 曝气能耗、泵送能耗、出水水质 自适应回归核函数 否
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表 3 城市污水处理过程单运行指标的操作变量设定值寻优算法比较
Table 3 Comparison of optimization algorithms for set-point values of operation variables with single operation indices in MWWTPs
文献 目标函数 约束条件 操作变量 优化方法 是否实际应用 优缺点 Amand等[86] 曝气能耗 无 硝态氮平均
日流量线性规划算法 否 算法计算过程简单,
容易实现. 但是仅涉及
单个操作变量, 难以用
于全流程优化运行.Martin等[87] 过程能耗 无 硝态氮 进化算法 否 Duzinkiewicz等[88] 曝气能耗、
泵送能耗无 溶解氧 遗传算法 否 Sharma等[89] 搅拌机能耗 无 搅拌机转速 进化算法 是 张平等[91] 曝气能耗 物料平衡方程、出水
氨氮、出水总氮溶解氧浓度、
污泥浓度混合遗传算法、惩罚
函数法、乘子法否 能够避免产生脱离现实
的解. 但是带约束条
件的优化设定问题的求
解更复杂、更困难.韩广等[92] 曝气能耗、
泵送能耗出水达标排放 溶解氧、硝态氮 梯度下降法、格朗日乘
子法、最大最小函数法否 许玉格等[93] 鼓风能耗、
泵送能耗出水达标排放 溶解氧、硝态氮 人工免疫算法 否 刘载文等[94] 鼓风能耗、
泵送能耗出水达标排放 溶解氧、硝态氮 遗传算法 否 Ruano等[95] 泵送能耗 出水 pH 值和出水氨氮 曝气流量、
内循环流量模糊逻辑推理 是
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表 4 城市污水处理过程多运行指标的操作变量设定值寻优算法比较
Table 4 Comparison of optimization algorithms for set-point values of operation variables with multiple operation indices in MWWTPs
文献 目标函数 操作变量 优化方法 是否实际应用 优缺点 Qiao等[97] 系统能耗、出水水质 溶解氧、硝态氮 加权求和法、基于梯度的
自适应动态规划算法否 便于利用现有的成熟的优
化方法进行问题求解, 但
是难以实现多个互相矛盾
的过程运行指标之间的折衷.Vega等[98] 曝气能耗、泵送能耗、出水水质 溶解氧、硝态氮 加权求和法、序列
二次规划算法否 史雄伟等[99] 出水氨氮、出水BOD、
出水COD和运行成本溶解氧、硝态氮 加权求和法、粒子群算法 否 Schlüter等[100] 过程操作性能、经济成本 冷凝器冷却流量、
外回流量动态加权求和、蚁群算法 否 Yetilmezsoy等[101] 甲烷速率、出水基质
浓度、净运营成本底物利用率 动态加权求和、非
线性规划算法是 Hakanen等[105] 系统能耗、出水水质 溶解氧、硝态氮 多目标优化算法 是 能够同时产生多个非支配
解. 但是当污水处理过程
入水负荷、入水组分浓度、
微生物活性等发生波动时,
算法搜索效率降低.Reifsnyder等[106] 曝气能耗、出水水质 手动阀开度 多准则优化算法 是 杨壮等[107] 系统能耗、出水水质 溶解氧、硝态氮 基于分解的多目标进化算法 否 Hreiz等[108] 运行成本、氮排放量 溶解氧、硝态氮 基于精英选择的多目标遗传算法 是 Zhang等[83] 出水水质、系统能耗 溶解氧、硝态氮 反向传播算法、多
目标优化方法否 能够实现操作变量设定值
的动态寻优. 但是环境剧
烈变化时, 算法寻优结果不理想.Egea等[109] 系统能耗、出水水质 曝气罐曝气系数、
内循环流量基于散点搜索的多目标优化 否 Qiao等[110] 曝气能耗、泵送能耗、出水水质 溶解氧、硝态氮 自适应多目标差分进化算法 否 De Faria等[111] 出水质量、运营成本、环境影响 外加碳源、
外加药品多目标进化算法 否
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