微生物絮凝剂产生菌的筛选及其絮凝活性研究

[目的]筛选出具有高絮凝活性的微生物产生菌,并观察研究微生物产生菌的外现形貌,并确定微生物产生菌的最佳培养条件.[方法]分别从土壤、活性污泥、食品厂污水和垃圾渗滤液中筛选出具有絮凝活性的菌株若干,然后在不同的培养基条件下通过富集培养、平板分离、纯化培养和复筛,筛选出具有高絮凝活性的菌种.在显微镜下观察菌种的形貌.最后考察了培养温度、培养时间和摇床转速对絮凝效果的影响,对培养条件进行了优化.[结果]从土壤、活性污泥、食品厂污水和垃圾渗滤液中筛选出具有絮凝活性的菌种8株,然后在不同的培养基条件下通过富集培养、平板分离、纯化培养和复筛等,筛选出絮凝活性超过90%菌种2株,在显微镜下初步鉴定为杆菌.以絮凝活性最高的F-4为例,该菌产絮凝剂的最佳培养条件为:培养时间56 h、培养温度30℃、摇床转速150 r/min.[结论]在该条件下,F-4菌株对高岭土悬浊液的絮凝率达到93.1%.     

一株絮凝剂产生菌TS-1的分离与鉴定

[目的]筛选鉴定出高絮凝活性的微生物絮凝剂产生菌:[方法丁通过培养基培养分离纯化出高絮凝活性的菌株并通过培养特征观察和生理生化特征测定鉴定筛选出的菌株.[结果]通过用稀释涂布平板法和平板划线法从土壤中分离、纯化和初筛后,从供试土样中共分离到14株具有絮凝性能的细菌,经复筛后得到5株絮凝活性较高(絮凝率>70%)的絮凝剂产生菌,其中1株经多次传代培养后絮凝活性仍很稳定(絮凝率始终>90%),其标记为TS-1.TS-1菌为具有荚膜的革兰氏阳性杆菌,并且菌体内无类脂物质如聚β-羟基丁酸.TS-1为解淀粉芽孢杆菌(Bacillus amyloliquefaciens),故将该菌定名为Bacillus TS-1.[结论]该试验筛选出的菌株可用于淀粉工业废水的絮凝处理和生化处理.     

微生物絮凝剂廉价制备的研究进展

微生物絮凝剂(Microbial flocculants,MBF)因其具有高效、无毒、无二次污染、用途广泛等特点而备受人们关注,被称为第3代絮凝剂.针对微生物絮凝剂在制备过程中由于菌体培养基成本过高而难以推广工业化生产的问题,介绍了国内外关于利用廉价有机物或有机废水作为替代碳源、氮源及直接作为替代培养基制备微生物絮凝剂的研究情况,并分析了微生物絮凝剂研制的发展前景.     

产絮凝剂双菌复合发酵体系的研究以及在水处理中的应用

从土壤和活性污泥中筛选到三株絮凝剂产生菌,将其进行双菌混合培养的正交试验,构建出了比单一菌所产絮凝剂絮凝活性更高的复合菌群LH,通过16SrRNA序列分析,初步判定二菌分别为Micrococcus sp.和Paenibacill usvelaei菌.对其性质的初步研究发现,复合菌LH所产絮凝剂MBF-LH为胞外代谢产物,其热稳定性好.LH产絮凝剂的最适碳源、最适培养温度、最适pH值和通气量分别为蔗糖、30℃、pH8和120r/min恒温振荡培养.培养基中添加KCl、CaSO₄、K₂HPO₄能明显提高发酵液絮凝率.絮凝剂MBF-LH处理废水时具有用量少、絮凝率高的特点.     

微生物的分离及其培养条件对溶煤效果的影响

分别从新汶矿区采集的土样、尾矿水、矿井水中分离筛选出溶煤效果比较好的7种菌种.采用这些优选菌种,在沙保劳麦芽糖培养基和改性CZAPECK DOX-medium培养基中分别对氧化程度不同的煤进行溶解试验,考察了培养基的组成及菌种紫外诱变对溶煤效果的影响.结果表明,煤的微生物溶解程度主要取决于培养基的氮含量和煤的氧化程度,煤中氧含量高有利于煤的微生物溶解;菌种诱变后溶煤时间明显缩短,溶煤率增大.通过菌种生长数码照片和透射电镜图片,观察到微生物的形态以及煤在微生物表面的溶解情况.在确定最佳溶煤时间后,通过原煤和煤溶物的工业分析、元素分析和红外光谱分析,考察了溶煤前后元素的变化和煤溶物的结构变化.     

脂肪酶产生菌的筛选·产酶条件及酶特性研究

[目的]筛选脂肪酶高活性菌株作为脂肪酶生产菌株和脂肪酶基因的供体菌.[方法]从油脂厂等地采集26份含菌样品,采用溴甲酚紫平板对样品进行初筛,以产脂肪酶发酵培养基摇瓶复筛,获得1株产脂肪酶活性较高的菌株09-7-1,经鉴定该菌株为黑曲霉(Aspergillus niger).采用正交试验对影响该菌株产酶的因素进行了研究,并探讨了该菌株所产脂肪酶的性质.[结果]该菌株最佳产酶条件:碳源为0.5%的可溶性淀粉,氮源为0.2%的酵母膏,培养温度为32℃,发酵液pH为5.2,该菌株最初发酵液中酶活力为13.164 U/ml,优化后酶活力达24.112 U/ml,是最初酶活力的1.83倍.该菌株所产脂肪酶最适作用温度为30℃,最适作用pH为7.0;酶液在60℃保温90 min后,活性损失较少,pH为5.5～10.0内稳定.[结论]该研究可为脂肪酶生产和基因研究奠定基础.     

蚕蛹壳几丁质降解菌的分离筛选及其产酶条件优化

[目的]对产几丁质酶菌株发酵产酶活性工艺进行优化.[方法]将筛选得到的1株产几丁质酶菌株G-254进行驯化培养,通过单因素试验考察了不同碳源、氮源和无机盐对菌株产酶活的影响;进行响应面试验,以菌株发酵所产酶活为响应值,确定最佳的发酵产酶工艺条件.[结果]菌株G-254发酵产几丁质酶最佳发酵条件为,葡萄糖8%,牛肉膏料5%,硫酸镁0.07%,在此条件下获得的几丁质酶活为6.86 U.[结论]提高了菌株发酵产几丁质酶酶活,为后续工业化发酵生产奠定了基础.     

微生物浸出法从石煤提钒尾渣中回收钒和铜的研究

中国堆存的石煤提钒尾渣量高达数十亿吨,尾渣中含有钒、铜等多种金属,高效回收尾渣中有价金属既可以实现其资源化,同时防止尾渣污染周边环境。本文采用微生物浸出技术回收石煤提钒尾渣中有价金属V和Cu,重点研究了不同初始pH、溶氧量(以摇床转速表征)、黄铁矿添加量、浸出时间等不同因素对氧化亚铁硫杆菌(T.f菌)浸出尾渣中金属钒、铜的影响。结果表明,初始pH 2～4范围内对V,Cu浸出率影响不大,较高的摇床转速有利于V,Cu的浸出;在初始pH为2、摇床转速170 r·min^-1、黄铁矿添加量15 g·L^-1的条件下,T.f菌浸出尾渣10天后,V和Cu浸出率分别达到83.4%和87.8%,相比单独酸浸体系,V和Cu浸出率分别提高了32.6%和17.92%,说明T.f菌在浸出石煤提钒尾渣过程中起了关键作用。研究结果将为推动微生物技术在石煤提钒尾渣资源化的稳定应用进程提供指导。     

嗜镍菌吸附Ni~（2＋）条件与机理的关系研究

为了有效解决环境中镍离子的污染问题,研究了实验室筛选的嗜镍菌吸附镍离子条件与机理的关系。实验结果表明：嗜镍菌吸附镍离子条件的确定要考虑体系的离子强度和活度系数、pH值和溶度积、菌体表面活性位点及与Ni2＋的碰撞几率、吸附趋于平衡的最短时间和最大限度地节省原材料和时间等因素。将诸因素与吸附量、吸附率结合,确定的最佳吸附条件：pH值为6、离子强度为0.01 mol/L、菌的质量浓度为2 g/L、Ni2＋的质量浓度为25 mg/L、温度为25℃,吸附2 h趋于平衡,吸附率最高达到98%。实验条件下,嗜镍菌的最大吸附量为80 mg/g,具有较好的应用价值。     

Dynamic Growth Rates of Microbial Populations in Activated Sludge Systems

Results of mathematical modeling and whole cell 16S ribosomal RNA-targeted fluorescence in situ hybridizations challenge the widely held perception that microbial populations in “steady-state” activated sludge systems share a common net growth rate that is proportional to the inverse of the mean cell residence time. Our results are significant because they encourage bioprocess engineers to appreciate the differences in growth physiology among individual microbial populations in complex mixed microbial communities such as suspended growth activated sludge bioreactor systems.     

多环芳烃厌氧降解菌的筛选与鉴定

[目的]筛选并鉴定多环芳烃厌氧降解菌.[方法]通过富集,在厌氧条件下从受焦油长期污染的土壤中筛选出多环芳烃的高效厌氧降解菌,并对其进行了生理生化试验和16S rDNA鉴定.[结果]从受焦油长期污染的土壤中分离出2株多环芳烃降解菌W2和Y3,经综合表征和16S rDNA序列分析,初步鉴定菌株W2为鞘氨醇单胞菌属(Sphingomo sp.),菌株Y3为芽孢杆菌属(Bacillus sp.).[结论]为多环芳烃的生物降解研究提供了理论依据.     

Adsorption of Perfluorinated Compounds onto Activated Carbon and Activated Sludge

The adsorption of perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) onto powdered activated carbon (PAC) was investigated in the presence and absence of effluent organic matter (EfOM) at an environmentally relevant concentration range (0.1–500??μg/L). Adsorption of PFOS and PFOA to PAC fitted the Freundlich model well (r2>0.98), and adsorption capacity of PFOS (KF = 17.48) and PFOA (KF = 10.03) in the absence of EfOM was more than one order of magnitude higher than that in the presence of EfOM (KF = 0.66 for PFOS, KF = 0.20 for PFOA), indicating that EfOM greatly reduces the adsorption capacity of PAC. Moreover, EfOM was characterized by ultrafiltration, and fractions of nominal molecular weights were obtained to investigate their effect on the PFOS and PFOA adsorption. The fraction of <1??kDa had greater effect on adsorption than the fraction of >30??kDa, indicating that the similar molecular size of target compounds was the major contributor to adsorption competition. Additionally, biosorption of PFOS and PFOA to activated sludge fitted the linear isotherm (r2>0.9) within a concentration range of 50–400??μg/L. On the basis of our data, the estimated partition coefficient, Kd, was 729??L/kg for PFOS and 154??L/kg for PFOA, respectively, suggesting that PFOS and especially PFOA have a low tendency to partition onto sludge.     

北冰洋产淀粉酶海洋细菌的筛选与鉴定及其产酶条件的优化

[目的]筛选与鉴定北冰洋产淀粉酶海洋细菌,并对筛选出的细菌进行产酶条件优化.[方法]从156株北冰洋海洋细菌中筛选出淀粉酶高产菌株Arc B84A,并对其进行了菌株形态学鉴定、16S rRNA分子鉴定以及产酶条件优化.[结果]菌株Arc B84A属于交替假单胞菌属.菌株Arc B84A的最佳产酶条件为:培养基起始pH值为7.0～8.0;最佳碳源为0.5%葡萄糖;最佳氮源为1.0%蛋白胨;TritonX-100、Tween-20、Tween-80等表面活性剂可以提高菌株淀粉酶活性,其中1.0% Tween-80的效果最为明显.[结论]该研究为海洋细菌淀粉酶的生产与应用提供了理论依据.     

Density and Activity Characterization of Activated Sludge Flocs

Activated sludge flocs are made up of a conglomerate of materials including microorganisms, exocellular polymers, inert particulates, slow and nonbiodegradable organic particles, and water. The goal of this study was to determine if inert/unbiodegradable aggregates had higher densities than active biomass. It was also desired to determine whether mixed liquor could be gravimetrically settled to differentially stratify flocs based on density and biological activity. In this manner, if activity stratification is possible, then less active biomass could be wasted preferentially during wastewater treatment operations (e.g., during daily wastage), thereby increasing the effective solids retention time and improving process performance. This paper reports the initial set of results, which focused on establishing density values of inert/unbiodegradable fractions of activated sludge floc, and the heterotrophic activity measurements of faster settling flocs compared to slower settling flocs. The results indicate that activated sludge from a local wastewater treatment plant had aggregate densities as low as 1.038?g/mL for slower settling floc particles and as high as 1.065?g/mL for faster settling floc particles. Primary effluent “inert+unbiodegradable particulate organic” fractions, which ultimately accumulate in mixed liquor and contribute to the inactive activated sludge floc fraction, had densities of approximately 1.24?g/mL. Mixed liquor that was digested in excess of 90 days to reduce any degradable organics revealed aggregate densities between 1.11 and 1.12?g/mL. Settling column experiments indicated that floc particles settled at rates ranging from less than 5?m/h to greater than 30?m/h. Specific oxygen uptake rates signified that the heterotrophic activity was homogeneous across all settling velocities except those flocs with a settling velocity of less than 5?m/h. These flocs exhibited a specific oxygen uptake rate of between 31 and 110% higher than the remaining floc. Determination of the mass fraction of these flocs indicates that they account for approximately 2% of the overall biomass. This low fraction limits the usefulness of differentially settling unaltered waste activated sludge to recover portions with higher activity.     

Chemical Oxygen Demand and the Mechanism of Excess Sludge Reduction in an Oxic-Settling-Anaerobic Activated Sludge Process

A modified activated sludge process, called the oxic-settling-anaerobic (OSA) process, achieved effective reduction in excess sludge production. Its key feature is the insertion of a sludge holding tank in the sludge return circuit to provide an anaerobic sludge zone. Our previous studies suggested that such excess sludge reduction might be associated with an increased sludge decay rate and the effective consumption of organic substrates generated during the retention of the thickened sludge in the sludge holding tank under a low oxidation-reduction potential (ORP) at ?250?mV. To confirm this suggestion, we analyzed the chemical oxygen demand (COD) balance in the sludge holding tank through batch experiments to simulate the sludge concentration, ORP level, and retention time in the sludge holding tank. The COD generated from the sludge reduction in the tank was utilized by organic gas (mainly CH4) production, denitrification, sulfate reduction, and phosphorus release, among which the gas production accounted for 50% of the COD utilization. We confirmed that the mechanism of the excess sludge reduction in the OSA process is through enhancement of the sludge decay rate in the anaerobic sludge zone, which increases the soluble COD level, which in turn is mainly transformed into methane gas and carbon dioxide during denitrification.     

Replacing Outliers and Missing Values from Activated Sludge Data Using Kohonen Self-Organizing Map

Modeling the activated sludge wastewater treatment plant plays an important role in improving its performance. However, there are many limitations of the available data for model identification, calibration, and verification, such as the presence of missing values and outliers. Because available data are generally short, these gaps and outliers in data cannot be discarded but must be replaced by more reasonable estimates. The aim of this study is to use the Kohonen self-organizing map (KSOM), unsupervised neural networks, to predict the missing values and replace outliers in time series data for an activated sludge wastewater treatment plant in Edinburgh, U.K. The method is simple, computationally efficient and highly accurate. The results demonstrated that the KSOM is an excellent tool for replacing outliers and missing values from a high-dimensional data set. A comparison of the KSOM with multiple regression analysis and back-propagation artificial neural networks showed that the KSOM is superior in performance to either of the two latter approaches.