In Saint-Louis, Senegal, a constructed wetland with horizontal flow reed beds (FHa and FHb) has demonstrated significant efficacy in treating municipal wastewater. Analyzing various treatment stages, the system showed...In Saint-Louis, Senegal, a constructed wetland with horizontal flow reed beds (FHa and FHb) has demonstrated significant efficacy in treating municipal wastewater. Analyzing various treatment stages, the system showed only a slight temperature variation, from an influent average of 26.3°C to an effluent of 24.7°C. Electrical conductivity decreased from 1331 mS/cm to 974.5 mS/cm post-primary treatment, with suspended solids (SS) dramatically reduced from 718.9 mg/L to 5.7 mg/L in the final effluent. Biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) saw a notable decrease, from initial levels of 655.6 mg/L and 1240 mg/L to 2.3 mg/L and 71.3 mg/L, respectively. Nitrogenous compounds (N-TN) and phosphates () also decreased significantly, indicating the system’s nutrient removal capacity. Microbiological analysis revealed a reduction in fecal coliforms from 7.5 Ulog/100ml to 1.8 Ulog/100ml and a complete elimination of helminth eggs. The presence of Phragmites and Typha was instrumental in enhancing these reductions. The system’s compliance with the Senegalese standards for disposal into natural environments, WHO recommendations for unrestricted water reuse in irrigation, and the European legislation for water reuse was established. The effluent quality met the stringent criteria for various classes of agricultural reuse, illustrating the system’s potential for sustainable water management. This wetland model presents a robust solution for water-stressed regions, ensuring environmental protection while supporting agricultural needs. The study calls for ongoing research to further refine the system for optimal, reliable wastewater treatment and water resource sustainability.展开更多
水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域...水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域水质数据为样本,使用线性插值修补缺失数据和剔除的异常数据。使用灰色关联分析计算不同水质指标间的相关性,选择高相关性的水质指标以确定输入变量,并使用门控循环单元(Gated Recurrent Unit, GRU)预测不同的水质指标。将GRA-GRU的预测结果与反向传播神经网络(Back Propagation Neural Network, BPNN)、循环神经网络(Recurrent Neural Network, RNN)、长短期记忆神经网络(Long Short Term Memory, LSTM)、GRU及灰色关联分析-长短期记忆神经网络(Grey Relational Analysis-Long Short Term Memory, GRA-LSTM)进行对比分析,结果显示GRA-GRU在不同水质指标预测上具有较好的适应性,可以有效降低预测误差。其中,与其他模型相比,GRA-GRU预测的化学需氧量在均方根误差上分别降低了3.617%、0.681%、0.478%、1.505%和0.471%。展开更多
针对污水处理厂生化池中参数监测智能化水平不高、人力耗费较大的问题,提出基于麻雀算法-长短期记忆神经网络(Sparrow Search Algorithm-Long Short Term Memory Network,SSA-LSTM)的水质参数预测模型。以污水处理过程中好氧区溶解氧(Di...针对污水处理厂生化池中参数监测智能化水平不高、人力耗费较大的问题,提出基于麻雀算法-长短期记忆神经网络(Sparrow Search Algorithm-Long Short Term Memory Network,SSA-LSTM)的水质参数预测模型。以污水处理过程中好氧区溶解氧(Dissolved Oxygen,DO)、好氧区混合液悬浮固体(Mixed Liquid Suspended Solids,MLSS)质量浓度、缺氧区DO、缺氧区氧化还原电位(Oxidation-Reduction Potential,ORP)、厌氧区DO和厌氧区ORP 6个关键指标为数据样本,进行实例研究。将SSA-LSTM的预测结果与长短期记忆神经网络(Long Short-Term Memory Network,LSTM)、粒子群算法(Particle Swarm optimization-Long Short Term Memory Network,PSO-LSTM)、深度森林以及支持向量机进行对比分析,结果显示:SSA-LSTM在6个参数上的均方误差(EMSE)和决定系数(R2)均表现出更好的预测性,预测精度最高。展开更多
文摘In Saint-Louis, Senegal, a constructed wetland with horizontal flow reed beds (FHa and FHb) has demonstrated significant efficacy in treating municipal wastewater. Analyzing various treatment stages, the system showed only a slight temperature variation, from an influent average of 26.3°C to an effluent of 24.7°C. Electrical conductivity decreased from 1331 mS/cm to 974.5 mS/cm post-primary treatment, with suspended solids (SS) dramatically reduced from 718.9 mg/L to 5.7 mg/L in the final effluent. Biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) saw a notable decrease, from initial levels of 655.6 mg/L and 1240 mg/L to 2.3 mg/L and 71.3 mg/L, respectively. Nitrogenous compounds (N-TN) and phosphates () also decreased significantly, indicating the system’s nutrient removal capacity. Microbiological analysis revealed a reduction in fecal coliforms from 7.5 Ulog/100ml to 1.8 Ulog/100ml and a complete elimination of helminth eggs. The presence of Phragmites and Typha was instrumental in enhancing these reductions. The system’s compliance with the Senegalese standards for disposal into natural environments, WHO recommendations for unrestricted water reuse in irrigation, and the European legislation for water reuse was established. The effluent quality met the stringent criteria for various classes of agricultural reuse, illustrating the system’s potential for sustainable water management. This wetland model presents a robust solution for water-stressed regions, ensuring environmental protection while supporting agricultural needs. The study calls for ongoing research to further refine the system for optimal, reliable wastewater treatment and water resource sustainability.
文摘水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域水质数据为样本,使用线性插值修补缺失数据和剔除的异常数据。使用灰色关联分析计算不同水质指标间的相关性,选择高相关性的水质指标以确定输入变量,并使用门控循环单元(Gated Recurrent Unit, GRU)预测不同的水质指标。将GRA-GRU的预测结果与反向传播神经网络(Back Propagation Neural Network, BPNN)、循环神经网络(Recurrent Neural Network, RNN)、长短期记忆神经网络(Long Short Term Memory, LSTM)、GRU及灰色关联分析-长短期记忆神经网络(Grey Relational Analysis-Long Short Term Memory, GRA-LSTM)进行对比分析,结果显示GRA-GRU在不同水质指标预测上具有较好的适应性,可以有效降低预测误差。其中,与其他模型相比,GRA-GRU预测的化学需氧量在均方根误差上分别降低了3.617%、0.681%、0.478%、1.505%和0.471%。
