连续流双室微生物燃料电池处理工业废水

以实际中药废水作为阳极基质、实际含镉废水作为阴极电解液,构建了连续流双室微生物燃料电池(MFC),考察了其产电性能及对两种废水的处理效果。78 d的运行数据表明:系统可实现最大输出电压417mV、最大体积功率密度11.8 W/m3,最大体积功率密度运行条件下的库伦效率为18.5%;在阳极进水有机物浓度变化较大的情况下,实现了阳极对中药废水中有机物的有效去除,平均COD去除率为81.5%;阴极对含镉废水中Cd2+的去除率为79.4%～84.8%。这表明MFC同步处理中药废水及重金属废水具有一定的可行性。     

A closer look at BLEVE overpressure

The overpressure produced by the boiling liquid expanding vapor explosion (BLEVE) is still not well understood. Various methods have been published on the overpressure modeling in the far field. They mostly differ by the modeling of the expansion energy, used to scale the distance to the source where the overpressure needs to be calculated. But these methods usually include a experimentally fitted reduction factor, and are mostly overestimating the overpressures. Today there is a growing interest in modeling the BLEVE overpressure in the near field, for studying the blast effect on critical infrastructure such as bridges and buildings. This requires a much better understanding of the BLEVE blast. This paper goes deeper in the understanding of the physical phenomenon leading to the BLEVE blast wave generation and propagation. First, mid-scale BLEVE experiments in addition to new experimental data for near field blast from a small scale supercritical BLEVE are analyzed. And second, an analysis method of the shocks observed in the experiments is presented based on fundamental gas dynamics, and allows the elaboration of a new modeling approach for BLEVE overpressure, based on the calculation of the initial overpressure and radius of the blast.     

大肠杆菌吸附-化学还原法用废含金催化剂制备金纳米线

采用大肠杆菌吸附-化学还原法,以大肠杆菌(ECCs)为模板、十六烷基三甲基溴化铵为保护剂、抗坏血酸为还原剂,由废含金催化剂制备金纳米线(AuNWs)。采用XRD,SEM,TEM等技术对AuNWs进行表征。研究了AuNWs对罗丹明6G(R6G)和4-巯基苯甲酸(4-MBA)的拉曼散射信号的增强效果。实验结果表明:在制备过程中加入微生物ECCs,可使金回收率提高约20百分点;当溶液pH小于4时,反应2 h后,有大量呈线状的AuNWs聚集沉降,金回收率可达99%1以上。表征结果显示,AuNWs呈多晶结构,晶格间距为0.23 nm。表面增强拉曼散射分析表明,AuNWs对R6G和4-MBA具有良好的拉曼光谱增强性能。     

纳米铁氧化物吸附处理重金属废水的研究进展

概述了用于吸附重金属的主要纳米铁氧化物的种类及其吸附效果,介绍了常见的纳米铁氧化物制备方法及改性方法,讨论了影响纳米铁氧化物吸附重金属的主要因素,并对纳米铁氧化物在水环境保护领域中的研究方向提出了展望:如发展绿色、高效的纳米铁氧化物制备工艺,探讨纳米铁氧化物结构调控和表面功能化对其吸附性能的影响等。     

Ti3C2纳米层状材料对废水中Cr（Ⅵ）的光催化去除性能

以Ti₃AlC₂为原料,采用HF刻蚀工艺制备出12种Ti₃C₂纳米层状材料,对其形貌进行了表征,并考察了以其作为光催化剂对废水中Cr（Ⅵ）的处理效果。实验结果表明：HF体积分数为80%、刻蚀时间为48 h时得到的MX-80-48的形貌较好;MX-80-48具有类似石墨烯的二维层状结构,纳米层厚度约20~50 nm,孔径2~10 nm,比表面积14.8 m²/g,在400~700 nm可见光范围内表现出强烈的吸光性;当Cr（Ⅵ）的初始质量浓度为40.00 mg/L、MX-80-48投加量为200 mg/L、pH=2、反应时间4 h（暗反应1 h+光照3 h）时,Cr（Ⅵ）去除率可达100%。     

水处理中微生物絮凝剂产生菌的选育及应用

综述了微生物絮凝剂产生菌筛选和培育方面的研究进展,介绍了微生物絮凝剂在给水和饮用水、乳化液的油水分离、污水处理等领域的应用,以及廉价培养基的探索实践。提出针对影响微生物絮凝剂产生菌生长代谢的环境条件开展深入研究,同时寻找或设计廉价培养基,降低生产成本,推动其工业化应用。     

SMBBR-AF-SMBBR组合工艺处理高氨氮农药废水

采用特异性移动床生物膜反应器(SMBBR)和厌氧生物滤池(AF)组合工艺处理高氨氮农药废水。考察了HRT、pH和DO等工艺条件对SMBBR-AF-SMBBR组合工艺运行稳定期COD和氨氮去除率的影响。试验结果表明,在进水COD为2 408~7 440 mg/L、ρ(NH_4~+-N)为160.21~433.84 mg/L、TN为208.27~537.65 mg/L、HRT为8d、pH为8.0、DO为4 mg/L的条件下,处理后出水平均COD为342 mg/L,COD去除率达92.3%;ρ(NH_4~+-N)小于4.0mg/L,氨氮平均去除率为89.2%;TN小于50 mg/L,平均TN去除达83.0%。出水各指标均优于原A2O工艺出水。     

氨废水资源化利用的折流式超重力床集成技术

利用折流式超重力床将氨废水处理的精馏和吸收过程集成在一台设备中,开发出一种设备小型化、流程紧凑的氨废水资源化利用集成技术。与传统技术相比,该技术在大幅节省占地面积和空间的同时,还可大幅节约设备建设所用钢材。工业规模试验结果表明,不同浓度的氨废水经该技术处理后可转化为氨质量分数大于22%的氨水资源,处理出水中氨氮质量浓度低于8.2 mg/L,尾气中未检测到氨,处理结果优于GB 31573—2015《无机化学工业污染物排放标准》。     

煤制乙二醇废水预处理装置的工业化调试

针对煤制乙二醇废水含高浓度硝酸盐氮的特点,设计了缺氧膨胀床(AEB)反应器预处理装置,并进行了工业化启动和调试运行,考察了其在反硝化连续流运行条件下的处理效果及工艺参数变化。结果表明,AEB反应器启动后,填料层生物膜挂膜快速且生长稳定。反应器在工业化调试阶段运行稳定,COD和TN的去除率和去除负荷较为稳定。在受到来水冲击后,AEB反应器处理效果稳定,出水可在短期内恢复正常。该技术的系统操控参数范围较广,易于工业化操控运行,在煤制乙二醇废水和其他含高浓度硝酸盐氮废水的处理中具有较大的推广价值。     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.