The application of the Genetic Algorithm （GA） for the influent flow optimized distribution in the four stage pilot plant of Step-Feed Biological Nutrient Removal （BNR） System was discussed. Under decided process p...The application of the Genetic Algorithm （GA） for the influent flow optimized distribution in the four stage pilot plant of Step-Feed Biological Nutrient Removal （BNR） System was discussed. Under decided process parameter and influent water conditions, the objective function of optimization was designed to minimize the difference between estimated and required effluent concentrations at the four stage pilot plant of Step-Feed BNR System, the optimized parameter for influent distribution ratios of the four stages is 37.2%, 27.4%, 23.2% and 12.2% respectively. According to the optimizations results and raw wastewater pilot-scale experiment, the average removal efficiencies for pollutants are higher.展开更多
Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total ...Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total suspend solids(TSS),and pH.Previous studies have been conducted to predict influent flow rate,and it was proved that data-driven models are effective tools.However,most of these studies have focused on batch learning,which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly.Online learning,which has distinct advantages of dealing with stream data,large data set,and changing data pattern,has a potential to address this issue.In this study,the performance of conventional batch learning models Random Forest(RF),K-Nearest Neighbors(KNN),and Multi-Layer Perceptron(MLP),and their respective online learning models Adaptive Random Forest(aRF),Adaptive K-Nearest Neighbors(aKNN),and Adaptive Multi-Layer Perceptron(aMLP),were compared for predicting influent flow rate at two Canadian WWTPs.Online learning models achieved the highest R2,the lowest MAPE,and the lowest RMSE compared to conventional batch learning models in all scenarios.The R2 values on testing data set for 24-h ahead prediction of the aRF,aKNN,and aMLP at Plant A were 0.90,0.73,and 0.87,respectively;these values at Plant B were 0.75,0.78,and 0.56,respectively.The proposed online learning models are effective in making reliable predictions under changing data patterns,and they are efficient in dealing with continuous and large influent data streams.They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.展开更多
连续流分段进水生物脱氮工艺（C SF B N R）是一种串联多个缺氧和好氧区域,充分利用污水中有机碳源进行有效脱氮的污水处理技术。介绍了C SF B N R的原理,重点分析了分段数量、进水流量分配比例、缺氧区和好氧区容积比、污泥回流比和进...连续流分段进水生物脱氮工艺（C SF B N R）是一种串联多个缺氧和好氧区域,充分利用污水中有机碳源进行有效脱氮的污水处理技术。介绍了C SF B N R的原理,重点分析了分段数量、进水流量分配比例、缺氧区和好氧区容积比、污泥回流比和进水C O D/T N等对工艺的影响。增大C SF B N R的分段数量、污泥回流比和容积比可以提高脱氮率,适宜的分段数、容积比和污泥回流比分别是2～4、1：4～1：1和75%～1 00%。优化后的C SF B N R处理C/N低至5的污水可以达到国家一级A排放标准。通过工程应用实例,证实了C SF B N R的脱氮率高于传统生物脱氮工艺。展开更多
该研究为满足农村污水处理设施管理方便、能耗低的要求,针对农村生活污水碳氮比低的特点,在传统多段A/O工艺中引入生物膜,并取消首段缺氧池和污泥回流,研发了改进型分段进水多段A/O工艺。根据反硝化反应电子转移基本关系式,推导了改进...该研究为满足农村污水处理设施管理方便、能耗低的要求,针对农村生活污水碳氮比低的特点,在传统多段A/O工艺中引入生物膜,并取消首段缺氧池和污泥回流,研发了改进型分段进水多段A/O工艺。根据反硝化反应电子转移基本关系式,推导了改进型分段进水多段A/O工艺的原水流量分配关系式,进而根据等容积负荷原则确定了各级好氧池和缺氧池的容积,同时对系统脱氮率进行了预测。流量分配系数与进水C/N比值和反硝化1 g NO_3^--N所需的COD量有关。系统的理论最大脱氮率除了与这两者有关以外,还与工艺分段数正相关。通过模拟不同碳氮比生活污水的连续运行实验,表明理论模型可以很好地指导工艺设计。进水COD/TKN分别为3.75和7时,系统TN平均去除率分别为50.5%和60.0%,出水能够稳定达到《城镇污水处理厂污染物排放标准》一级B标准。展开更多
文摘The application of the Genetic Algorithm （GA） for the influent flow optimized distribution in the four stage pilot plant of Step-Feed Biological Nutrient Removal （BNR） System was discussed. Under decided process parameter and influent water conditions, the objective function of optimization was designed to minimize the difference between estimated and required effluent concentrations at the four stage pilot plant of Step-Feed BNR System, the optimized parameter for influent distribution ratios of the four stages is 37.2%, 27.4%, 23.2% and 12.2% respectively. According to the optimizations results and raw wastewater pilot-scale experiment, the average removal efficiencies for pollutants are higher.
文摘Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total suspend solids(TSS),and pH.Previous studies have been conducted to predict influent flow rate,and it was proved that data-driven models are effective tools.However,most of these studies have focused on batch learning,which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly.Online learning,which has distinct advantages of dealing with stream data,large data set,and changing data pattern,has a potential to address this issue.In this study,the performance of conventional batch learning models Random Forest(RF),K-Nearest Neighbors(KNN),and Multi-Layer Perceptron(MLP),and their respective online learning models Adaptive Random Forest(aRF),Adaptive K-Nearest Neighbors(aKNN),and Adaptive Multi-Layer Perceptron(aMLP),were compared for predicting influent flow rate at two Canadian WWTPs.Online learning models achieved the highest R2,the lowest MAPE,and the lowest RMSE compared to conventional batch learning models in all scenarios.The R2 values on testing data set for 24-h ahead prediction of the aRF,aKNN,and aMLP at Plant A were 0.90,0.73,and 0.87,respectively;these values at Plant B were 0.75,0.78,and 0.56,respectively.The proposed online learning models are effective in making reliable predictions under changing data patterns,and they are efficient in dealing with continuous and large influent data streams.They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.
文摘连续流分段进水生物脱氮工艺（C SF B N R）是一种串联多个缺氧和好氧区域,充分利用污水中有机碳源进行有效脱氮的污水处理技术。介绍了C SF B N R的原理,重点分析了分段数量、进水流量分配比例、缺氧区和好氧区容积比、污泥回流比和进水C O D/T N等对工艺的影响。增大C SF B N R的分段数量、污泥回流比和容积比可以提高脱氮率,适宜的分段数、容积比和污泥回流比分别是2～4、1：4～1：1和75%～1 00%。优化后的C SF B N R处理C/N低至5的污水可以达到国家一级A排放标准。通过工程应用实例,证实了C SF B N R的脱氮率高于传统生物脱氮工艺。
文摘该研究为满足农村污水处理设施管理方便、能耗低的要求,针对农村生活污水碳氮比低的特点,在传统多段A/O工艺中引入生物膜,并取消首段缺氧池和污泥回流,研发了改进型分段进水多段A/O工艺。根据反硝化反应电子转移基本关系式,推导了改进型分段进水多段A/O工艺的原水流量分配关系式,进而根据等容积负荷原则确定了各级好氧池和缺氧池的容积,同时对系统脱氮率进行了预测。流量分配系数与进水C/N比值和反硝化1 g NO_3^--N所需的COD量有关。系统的理论最大脱氮率除了与这两者有关以外,还与工艺分段数正相关。通过模拟不同碳氮比生活污水的连续运行实验,表明理论模型可以很好地指导工艺设计。进水COD/TKN分别为3.75和7时,系统TN平均去除率分别为50.5%和60.0%,出水能够稳定达到《城镇污水处理厂污染物排放标准》一级B标准。
