Performance of a Horizontal Flow Constructed Reed Bed Filter for Municipal Wastewater Treatment: The Case Study of the Prototype Installed at Gaston Berger University, Saint-Louis, Senegal

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.

Wastewater treatment performance and microbial community structure in the constructed wetland under double-pressure of low temperature and Ag NPs exposure

To investigate the effects of silver nanoparticles(Ag NPs)and low temperature double-pressure on the wastewater treatment efficacy and the microbial community structure of constructed wetlands,a pilot-scale vertical flow constructed wetland was set up to treat synthetic wastewater under laboratory conditions.By measuring the effluent concentration of ammonia nitrogen(NH_(4)^(+)-N),total nitrogen(TN),total phosphorus(TP),chemical oxygen demand(COD),and the diversity,richness,and community structure of microorganisms of the upper and lower soil layers in the wetland,the nutrient removal effect of the constructed wetland and the changes in the microflora of the soil layer were studied.The results reveal that the correlation coefficients between the removal rates of TN and NH_(4)^(+)-N and the temperature are 0.463 and 0.692,respectively,indicating a significant positive correlation.From the warm to the cold season,both the diversity and richness of microorganisms in the lower soil layer of wetlands are inhibited under the double-pressure of Ag NPs and low temperature,and the abundances of the denitrogenation functional bacteria such as Candidatus nitrososphaera,Sulfuritalea,Anaeromyxobacter,Candidatus solibacter,Nitrospira,and Zoogloea are altered.Low temperature and Ag NPs exposure can thus affect the wastewater treatment performance of constructed wetlands,possibly because of the seasonal changes of the microflora.

OPTIMIZED DESIGN OF NATURAL ECOLOGICAL WASTEWATER TREATMENT SYSTEM BASED ON WATER ENVIRONMENT MODEL OF DYNAMIC MESH TECHNIQUE

A Natural Ecological Wastewater Treatment System （NEWTS） is usually built on natural terrain with necessary topography modification to improve water flowing route and pattern, and then the topography modified NEWTS should also have a reasonable water storage volume and hydraulic retention time so as to achieve the anticipated water purification effect. In this study, the dynamic mesh technique based on the finite element method and element storativity coefficients was presented to develop a two-dimensional hydrodynamic and water quality model, which was used to optimize the design of NEWTS under the dynamic land-water boundary due to various water storage volume. The models were employed in the optimized design of NEWTS from a large abandoned coal mine, which purifies the polluted water flowing into a large water storage lake, as part of the East Route South-to-North Water Transfer Project in China. Specifically, the natural topography modification scheme was presented, and further, a reasonable water storage volume and hydraulic residence time was obtained, based on the reasonable estimation of roughness coefficient and pollutant removal rate of the NEWTS with phragmites communis.

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations.In this study,we aimed to develop an integrated method for predicting effluent COD and NH3 levels.We employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 d.Then we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 points.Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this data.This innovative model synergized sewage imagery with water quality data,enhancing prediction accuracy.As a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data.Consequently,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.

Reuse rate of treated wastewater in water reuse system

A water quality model for water reuse was made by mathematics induction. The relationship among the reuse rate of treated wastewater（R）, pollutant concentration of reused water（ Cs ）, pollutant concentration of influent（ C0）, removal efficiency of pollutant in wastewater（ E）, and the standard of reuse water were discussed in this study. According to the experiment result of a toilet wastewater treatment and reuse with membrane bioreactors, R would be set at less than 40%, on which all the concerned parameters could meet with the reuse water standards. To raise R of reuse water in the toilet, an important way was to improve color removal of the wastewater.

淡水池塘养殖尾水处理技术研究综述

为实现水产养殖业节能减排、低碳高效和可持续发展,必须解决传统池塘养殖尾水处理问题。根据典型池塘养殖尾水污染物特性,全面总结国内外淡水池塘养殖尾水的处理方法和技术特点;对比了“三池两坝”“集装箱+生态塘”“生态湿地”等典型池塘养殖尾水处理模式;分析了竖流沉淀、旋流分离、滤筛、气浮等物理净化技术以及臭氧-微纳米浮选、絮凝、微生物制剂、活性污泥、新型生物膜反应器过滤、水生植物等化学及生物净化技术;指出了该领域的热点研究方向和关键技术问题,包括发展“节能、减排、生态、高效”集约化池塘养殖模式、研发养殖尾水专用处理装备、提高净化效率和减少占地面积等,可为池塘养殖尾水处理技术及相关装备研发提供一定的理论参考。

石化废水处理厂特征污染物变化及水质安全研究

石化废水污染控制与水质安全是水环境领域污染防治的重要课题,但是目前关于石化废水处理工艺中特征污染物的组成变化及水质安全性尚未深入探讨。研究了某石化废水处理厂在废水处理过程中废水特征污染物的浓度变化和毒性削减效应。结果表明,预处理阶段COD和石油类去除率分别为36.38%和95.16%,出水生物毒性有效降低。生化处理单元COD和氨氮去除率分别达到63.19%和43.21%,废水中酚类、酯类、胺类等有机物含量显著降低。石化废水经过深度处理后,COD、BOD5、石油类、氨氮、总氮分别为(32.54±4.93)、(2.19±1.09)、(0.04±0.01)、(2.32±0.31)、(27.08±1.45)mg/L。由于酚类和含氮杂环有机物得到有效去除,最终出水毒性单位(TU)值为0.32,无急性生物毒性。该石化废水处理厂所采用的废水处理工艺能够有效去除废水中多种特征污染物,出水无显著急性生物毒性,对水环境生态安全影响较小。

废水处理中水质监测参数的实时预测研究

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。

基于优化机器学习的炼化企业污水场均质池出水水质预测研究

炼化企业生产工艺流程复杂且装置繁多,炼化污水水质水量波动大,污染物组成复杂,导致下游污水处理系统频繁受到冲击,难以及时响应调控。以华南某炼化企业污水处理系统均质池出水真实水质数据为基础,对水质参数进行相关性分析和特征降维,分别构建了多参数水质预测模型和时间序列水质预测模型。研究结果表明:电导率(Cond)与COD存在一定相关性(PCC和SCC分别为0.493和0.513),水质参数与COD相关性排序为Cond>pH>NH_(3)-N>TP;SVR和BP-ANN多参数预测模型均未取得理想的预测效果,决定系数R^(2)均低于0.5;SVR和BP-ANN时间序列模型预测准确率较多参数模型大幅提高,决定系数R~2平均提升45%,均高于0.7,预测值与实测值拟合度高;模型现场验证结果表明,当上游污水水质发生波动时,模型对水质波动趋势预测较为准确,可以有效的指导现场对工艺参数进行调控。

疫情影响下长沙市某污水处理厂水量水质统计分析

为保障城市污水处理厂的安全运行和达标排放,文中选取长沙市某污水处理厂近6年进水水量和进出水水质数据进行统计和分析。结果表明:该污水处理厂的日均进水量在2018年—2023年3月近6年期间,于2020年达到最低,主要是受2020年年初新冠疫情所采取的封控措施影响;当年的进水水质受疫情和降雨量的双重影响,除2022年外,2020年BOD_(5)、COD_(Cr)和SS浓度较其他年份均大幅下降,而氨氮、TN和TP浓度降低相对较小。2022年进水BOD_(5)和SS低于其他年份可能是受2022年相对严重的疫情和干旱天气的影响,管道中污染物沉积进行厌氧水解导致。以疫情初始的2020年数据为研究对象,该年内各水质指标的进水浓度波动范围较大,总体上在冬季相对较高,在1月底—2月底和夏季偏低。实测出水水质浓度均达到设计出水水质标准,其中SS的去除率最高,而TN的去除率最低,改良式序列间歇反应器(MSBR)能够去除大部分的TN和氨氮。进水BOD_(5)/COD_(Cr)、BOD_(5)/TN和BOD_(5)/TP的平均值分别为0.58、4.95和46.34,1月底—2月底受疫情影响出现短暂的急剧变化,但总体进水可生化性较好、碳源充足,可采用生物除磷工艺。在碳源不足时,为保证出水达标,可考虑化学辅助除磷。研究结果可为疫情影响下类似污水处理厂的设计和运行提供参考。

基于BiLSTM深度学习模型的污水处理厂水质预测

针对污水处理厂部分水质指标难以实时测定的问题,本研究利用BiLSTM神经网络建立软测量模型,获取预测数据。实验表明,该模型对出水COD与出水TN的预测均有较高的准确度。预测数据可为污水处理厂运营者的维护与管理提供科学依据,并且可以提前预知水质异常等状况。

南京市主城区污水处理厂进水水质的时序特征

以2019年—2021年南京市主城区7个污水处理厂逐日进水水质的监测数据为研究对象,系统地分析污染物浓度的时序特征。运用经验模态分解识别了水质的波动周期,并运用季节与趋势分解以及Mann-Kendall检验诊断了水质的变化趋势。主要结果如下:(1)主城区污水处理厂进水氨氮、TN、TP、COD_(Cr)的平均质量浓度分别为24.74、29.47、3.08、214.85mg/L;(2)进水污染物浓度呈现出显著的季节差异,其中7月的浓度较低,而12月的浓度较高;(3)主城区污水处理厂进水COD_(Cr)的上升趋势较为强烈,其中4个污水厂的COD_(Cr)浓度均呈现出显著的上升趋势;(4)在逐日尺度上各污水处理厂进水的氨氮、TN、TP、COD_(Cr)均呈现出3 d的周期性振荡。相关结果为研究区提高污水处理运营效能提供了重要的数据支撑。

城市污水处理厂进水水质规律分析及应用

利用城市污水处理厂的实际运行数据,分析水质变化情况及水质指标关联度,总结水质水量的变化规律,为后续污水处理提标改造提供定量依据。结果表明,该污水厂进水流量、NH_(3)-N、TN和TP等指标季节因素起决定性影响,季节因素对COD、BOD_(5)的影响较小。通过对城市污水处理厂进水水质分析,掌握污水厂进水情况,可为污水处理厂设计和运行提供基础数据,也可为其他类似工程提供可供借鉴的思路。

污水处理装置排放超标控制措施研究

针对目标污水处理装置采用加药絮凝的方式对污水进行处理,出现了出口系统水质污染物排放超标的现象,在分析进水系统和出水系统水样水质指标的基础上,考察了不同类型絮凝剂对污水处理效果的影响,优选出性能较好的复合絮凝剂FX-1,并考察了污水处理装置的长时间运行效果。结果表明:FX-1的使用能够有效降低目标污水处理装置出口端水样中的各项污染物浓度,且出水系统水样各项污染物浓度在12个月内均能得到有效控制,COD、悬浮物、氨氮、总氮、总磷和石油类含量均能达到GB 18918—2002中“一级A标准”要求,实现了目标污水处理装置高效稳定运行。

硅太阳能电池板生产含氟废水处理工艺研究

文章主要研究了硅太阳能电池板生产中的含氟废水处理工艺,包括某实际含氟废水处理项目的基本情况、含氟废水处理工艺设计分析、含氟废水处理工艺项目投资与运行情况。希望通过本次分析,可以为硅太阳能电池板生产工艺中的含氟废水处理提供一定参考,以此来满足此类废水的实际处理与排放需求。

污水处理系统问题分析与对策研究

介绍了鞍钢集团公司北部厂区污水处理系统设施。针对污水处理系统的管网优化、延长设备使用寿命等方面存在的问题,提出解决方案,优化了污水处理系统管网布置,提高了净环水水质,降低了运行成本。

南方某工业镇污水处理系统厂网改扩建设计思路

文章主要针对南方某工业镇现有污水处理系统存在的问题进行分析,基于现状污水厂的处理能力提出管网分流改造的思路,并通过实施智能分流技术、工业废水监测控制系统,提高管网的运维管理水平;对新建污水站采用优化预处理和生化处理工艺等措施,较好地解决了工业废水的处理问题。

化工园区综合废水处理技术现状与展望

开发高效、环境友好的综合废水处置技术和工艺有利于促进化工园区的发展及生态文明建设。介绍了化工园区综合废水来源、类型及水质特点,系统地总结了我国化工园区综合废水目前常用的一级预处理、二级生化处理、三级深度处理的工艺技术,前瞻了化工园区综合废水处理的先进技术。结合我国化工园区综合废水处理现状存在的问题,对未来的发展方向提出了展望。

基于集成模型的污水处理厂出水总氮预测方法

污水处理厂是控制水体污染、改善水环境质量的重要基础设施,建立可靠的污水处理厂出水水质预测模型以便及时反馈污水处理状况是污水处理领域的研究热点之一。利用Stacking集成思想结合LSTM、BPNN、SVR、XGBoost、KNN 5种算法建立了污水处理厂出水总氮的预测模型。分析单一算法预测效果发现,KNN算法与其他算法相比拟合程度偏低,RMSE、MAE和R^(2)分别为1.10、0.995和0.567,可作为Stacking模型的元学习器。通过算法间预测性能差异的比较,选择LSTM、BPNN、SVR、XGBoost算法作为基学习器。与其他基、元学习器的组合相比,以LSTM、BPNN、SVR、XGBoost为基学习器,KNN为元学习器的组合方式预测准确度最高,RMSE、MAE和R^(2)分别为1.01、0.782和0.702。与使用单一算法中预测结果最好的LSTM算法的预测结果相比,该组合的RMSE、MAE分别降低了4.77%和15.1%,R^(2)提升了10.9%,具有明显的预测优势。

城镇污水处理厂高排放标准处理工艺案例分析

为改善水环境、保护水资源,我国部分地区陆续提出了“准Ⅳ类”(COD_(Cr)、氨氮、TP等达到地表水Ⅳ类标准,TN一般由一级A标准的15 mg/L提高至10 mg/L)、“准Ⅲ类”等高于国家标准的污水处理厂排放标准,这对污水处理工艺提出了更高的要求。在梳理各地现行高排放标准、分析现有工程实例的基础上,提出执行高排放标准时,仍应采用“预处理+生物处理+深度处理”为主的工艺技术路线。其中生物处理可采用脱氮除磷效率更高的优化、改良或组合工艺,深度处理在常规“混凝、沉淀、过滤”的基础上,可选用臭氧氧化、活性炭吸附、反硝化生物滤池、膜分离等有针对性的强化工艺。确定污水处理排放标准时,应综合考虑受纳水体环境容量和建设运行成本,系统考虑水环境治理,不能仅将重点放在污水处理厂提标上。