Artificial Intelligence Technology for Water Pollution Control in the Yangtze River Basin
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摘要: 随着经济的快速发展和城市化进程的不断加速，促使水污染严重的长江流域需从污染物去除过程的建模与优化、污水处理过程的优化控制、水污染监测系统的构建开展水污染治理研究.传统的水污染处理技术存在污染物去除效率预测精度较低、污水优化控制成本较高、水污染监测滞后效应严重的问题.人工智能技术能够有效克服上述问题，因此通过梳理国内外学者利用人工智能技术在污水污染物去除过程的建模与优化、污水处理过程的优化控制及水污染监测系统的构建等方面的研究成果，为全面加强长江流域水污染治理能力提供科学可靠的技术指导.结果表明：①利用人工神经网络技术（径向基神经网络、多层前馈网络-人工神经网络、多层感知器神经网络）对污水污染物去除过程进行建模与优化，为精确预测长江流域重金属（Cr、Cu）、营养盐（TN、TP）、持久性有机污染物〔PBDEs（多溴二苯醚）、HCH（六氯环己烷）〕的去除率提供重要参考价值.②采用污水处理的自动控制技术与人工智能技术（递归神经网络、支持向量机、模糊神经网络等）构建污水智能控制系统，为长江流域实现高效节能的污水优化控制提供重要的技术指导.③利用在线监测仪器和人工智能技术（小波神经网络、多元线性回归-人工神经网络、叠层去噪自动编码器等）建立水污染智能监测系统，为解决长江流域水污染监测响应滞后问题提供有力的技术支持.因此，人工智能技术对长江流域提高污水污染物去除率，降低污水优化控制成本，提升水污染监测时效性具有重要的推广价值.Abstract: With the rapid development of economy and the acceleration of urbanization, research on water pollution control needs to be carried out in the heavily polluted Yangtze River Basin, including the modeling and optimization of pollutant removal processes, the optimization control of sewage treatment processes, and the construction of water pollution monitoring system. The traditional water pollution treatment technology has the problems of low prediction accuracy of pollutant removal efficiency, high cost of sewage optimization control and serious lag effect of water pollution monitoring. Artificial intelligence technology can effectively overcome the above problems. By combing the existing research results of wastewater pollutant removal modeling and optimization, wastewater treatment process optimization control and water pollution monitoring system construction using artificial intelligence technology, we can provide scientific and reliable technical guidance for comprehensively strengthening the capacity of water pollution control in the Yangtze River Basin. The results show that: (1) Using artificial neural network technology (radial basis function neural network, multilayer feedforward neural network, multilayer perceptron neural network, etc.) to model and optimize the process of wastewater pollutant removal, which provides an important reference value for accurately predicting the removal efficiency of heavy metals (Cr, Cu), nutrients (TN, TP) and persistent organic pollutants (PBDEs, HCH) in the Yangtze River Basin. (2) Using automatic sewage treatment control technology and artificial intelligence technology (recurrent neural network, support vector machine, fuzzy neural network, etc.) to establish a sewage intelligent control system, which provides important technical guidance for achieving efficient and energy-saving sewage control in the Yangtze River Basin. (3) Using on-line monitoring instruments and artificial intelligence technology (wavelet neural network, multiple linear regression artificial neural network, automatic coder of laminated noise removal, etc.) to establish a water pollution intelligent monitoring system, which provides strong technical support for solving the problem of lagging response of water pollution monitoring in the Yangtze River Basin. Consequently, artificial intelligence technology has important promotion value to improve the efficiency of sewage pollutant removal, reduce the cost of sewage optimal control, and improve the timeliness of water pollution monitoring in the Yangtze River Basin.
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表 1 不同人工神经网络方法在污水污染物去除率预测中的应用
Table 1. Application of different artificial neural network methods in prediction of sewage pollutant removal efficiency
人工智能方法 输入参数 输出参数 数据分区 绩效评估标准 数据来源 训练数据集/个 验证数据集/个 测试数据集/个 R2 RMSE BP-ANN Cr含量、pH、DO浓度、接触时间 Cr去除率 — — — 0.988 — 文献[27] 有机负荷率、NH4+-N浓度、pH、CODCr浓度、沼气产量、出水挥发性脂肪酸 CODCr去除率 152 33 3 0.870 — 文献[28] ANFIS Cu含量、pH、接触时间、吸附剂用量 Cu吸附率 38 6 6 0.999 0.353 文献[29] MLPNN Cu含量、pH、接触时间、吸附剂用量 Cu吸附率 38 6 6 0.989 1.248 文献[29] 萘浓度、盐度、通量率 萘去除率 116 38 38 0.943 0.042 文献[30] RBFNN pH、吸附剂用量、温度、接触时间 Cu去除率 50 — 50 0.999 0.012 5 文献[31] 注：RMSE为均方根误差，可衡量模拟值与实测值之间的偏差.下同. 
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表 2 不同人工智方法在污水优化控制中的应用
Table 2. Application of different artificial intelligence methods in wastewater optimal control
人工智能方法 影响因子 控制目标 效果 数据来源 RBFNN与MLPNN 温度及DO、NH4+-N、CODCr、NO2--N、NO3--N的浓度 逆变器输出频率 实现高脱氮效率，能够严密控制供氧 文献[33] MLPNN 温度及DO、NH4+-N、NO2--N、NO3--N、TP、TN、CODCr的浓度 水力停留时、曝气总时间以及NH4+-N、TP、CODCr的去除率 总曝气时间和水力停留时间分别减少了50%和56% 文献[34] RNN 生物反应器中DO浓度设定值和内循环流量 生物反应器和氨水中的硝酸盐浓度 降低了污水中NH4+-N浓度峰值、硝酸盐浓度及能耗成本 文献[35] SVM DO浓度、pH、好氧阶段控制pH与DO浓度的操作变量 好氧阶段时长 好氧总时长减少了9.54 d 文献[25] FNN SNO2和SO5最佳设定值和实际输出值之间的误差及其误差的变化 污水中SNO2和SO5 曝气能量减少了7.6% 文献[36] CODCr浓度、TN浓度、流量、pH、回流混合液比、缺氧区硝酸盐浓度 运行成本及污水中CODCr、TN浓度 CODCr和TN浓度在一周内分别降低了14%和10.5% 文献[37] ANFIS 流量、CODCr浓度、NH4+-N浓度 鼓风机提供的通风量 污水处理厂的运行成本降低了33%左右 文献[38] 底物、DO浓度、生物量 上升时间、沉降时间 上升时间和沉降时间分别减少了45.7%和3.5% 文献[39]
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表 3 不同人工智能方法在水污染监测系统中的应用
Table 3. Application of different artificial intelligence methods in sewage monitoring system
人工智能方法 主要参数 次要参数 样本量/个 绩效评估标准 数据来源 R2 RMSE RBFNN TP浓度 pH、温度以及DO、CODCr、TSS、NH4+-N、NO3--N的浓度 800 — 0.104 文献[41] WNN CODCr浓度 流量、好氧反应器中NH4+-N和DO的浓度 250 — — 文献[42] PSO-SDAE CODCr、TN、NH4+-N的浓度 流量、生物膜系统回流比 80 — 5.94(CODCr)、1.26(TN)、1.27(NH4+-N) 文献[26] MLR-ANN BOD5浓度 水温、降水量、pH、流量以及NH4+-N、TP、NO3--N的浓度 2 364 0.584 — 文献[43] RSONN 污泥体积指数 pH及CODCr、TN、DO的浓度 220 — 0.143 文献[44] MLPNN CODCr、TN、TSS的浓度 流量、温度及NO3--N和NH4+-N的浓度 1 120 0.90(CODCr)、0.92(TN)、0.88(TSS) — 文献[45] FNN BOD5、CODCr、TSS的浓度 pH、温度 159 0.96(BOD5)、0.95(CODCr)、0.94(TSS) 1.13(BOD5)、1.67(CODCr)、0.98(TSS) 文献[46] 注：TSS为总悬浮固体.
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