某油田稠油采出液生产分离器机械消泡技术研究

目的 针对某油田进行机械消泡技术研究,进而为促进分离器内稠油的快速消泡提供解决办法.方法 利用Fluent软件模拟不同导流板参数下分离器中气液分离效果及泡沫变化情况,并采用动态解析法发泡方式,利用高压溶气消泡测试系统对不同类型和型号的金属规整填料和两种化学消泡剂,进行消泡测试以及组合消泡实验.结果 导流板放置角度为45°时,分离效率和泡沫聚并效果均较好,而导流板放置距离影响不大;随着消泡填料的高度增加,消泡效果随之增加.结论 分离器入口导流板最佳放置角度为45°,孔板波纹填料SM250*12 cm为优选的机械消泡构件.实际生产采用机械消泡方法即可满足消泡需求,要求更好的消泡效果时,可将机械消泡与化学消泡剂消泡两种方式结合使用.     

日光温室夏季休闲期间的土壤管理对可溶性氮的影响

日光温室夏季休闲期间大水漫灌和高温闷棚是普遍的土壤处理措施,该过程灌水多、温度高,对氮素循环影响大.为了探明休闲期间土壤管理对氮素保持与损失的影响,通过田间试验揭示夏季休闲期间大水漫灌、高温闷棚对不同灌溉施肥模式(滴灌、漫灌)和不同有机物料还田处理(单施有机肥、有机肥配施小麦秸秆、有机肥配施玉米秸秆)土壤可溶性氮的影响.结果表明:作物收获后,滴灌和漫灌各处理平均w(矿质氮)分别为103.9和68.6 mg/kg,大水漫灌使滴灌0~30 cm土层w(矿质氮)显著降低30%,漫灌w(矿质氮)变化不大.日光温室夏季休闲期w(SON)(SON为可溶性有机氮)为16.3~69.1 mg/kg,SON相对含量为15%~48%.大水漫灌使滴灌和漫灌w(SON)分别显著增加2.9和2.5倍;高温闷棚使滴灌和漫灌w(SON)显著降低107.1和72.4 kg/hm2,降幅分别为41%和34%,同时w(矿质氮)分别显著增加117.9和126.7 kg/hm2,土壤氮素矿化速率分别为1.7和1.8 mg/(kg·d).与单施有机肥相比,长期有机肥配施玉米或小麦秸秆可显著增加滴灌w(矿质氮),但对w(SON)无影响.综上,休闲期间的土壤管理对土壤表层氮素含量的影响较大,其中大水漫灌容易造成滴灌残留氮素的大量损失,而随后的高温闷棚加速了SON的矿化.      

杨树人工林碳循环对淹水的响应

本文采用涡度相关方法对安徽怀宁杨树人工林的碳通量进行监测,得到微气象数据和通量数据。通过坐标旋转、密度校正和数据插补处理提高数据质量。选取2005年9月淹水期间的数据。分六个阶段进行分析。得到以下结果：（1）在半小时尺度上,在淹水期表观量子效率α随着土壤含水量SWC上升而降低;在未淹水期最大。为-0．0023μmolphoton^-1;退水期最低,为-0．00122mgCO2μmolphoton^-1,退水后恢复到-0,00168mgCO2μmolphoton^-1;α在淹水中2期降低速率最大,在整个淹水期间α的恢复速率是大于降低速率的。（2）生态系统总初级生产力（GEP）在淹水后不断降低．耒淹水期为8．0gCm^-2day^-1．退水后为4．7gCm^-2day^-1,在淹水中2期降幅是最大的达到了16％。（3）生态系统呼吸（Reco）在完全被水淹的第一天降到最低,仅为2．2gCm^-2day^-1;由于温度的影响其变化规律比较复杂。在淹水中2期的变化幅度是最大的;退水后Reco为4．2gCm^-2day^-1恢复到来淹水前的87％。（4）淹水期净生态系统CO2交换量（NEE）的变化表现为先上升后下降的趋势,在淹水中1期最大为-4．2gCm^-2day^-1。退水后最低。为0．6gCm^-2day^-1是未淹水前的17％。     

New policies to deal with climate change and other drivers impacting on resilience to flooding in urban areas: the CORFU approach

In the context of urban flood management, resilience is equal to resisting, recovering, reflecting and responding. The variety of causes of flooding and their consequences underpin the need for increased and internationally coordinated efforts to enhance technologies and policies for dealing with floods. This paper addresses this issue and presents some novel research ideas related to resilience to flooding in urban areas, which are under development within the EU FP7 project ‘Collaborative research on flood resilience in urban areas’ (CORFU). The approach adopted in this project aims to quantify the cost-effectiveness of resilience measures and integrative and adaptable flood management plans for different scenarios of relevant drivers: urban development, socio-economic trends and climate changes. It is believed that the way in which the different models are being put together, combined with the variability of conditions in case study areas in Asia and in Europe, will ultimately enable more scientifically sound policies for the management of the consequences of urban flooding.     

Using thermal analysis with kinetic calculation method to assess the thermal stability of azo compound on construction materials

The application of construction polymers in engineering and alternative materials has always occupied a place in the market. In the production process of polymer resins, initiators can be used to lower the polymerization reaction energy threshold, which can improve reaction efficiency and reduce energy loss. However, as a commonly used energetic substance in the polymerization process, azos have caused related process hazards due to their exothermic characteristics. Because of this, it is essential to examine and analyze the thermal hazard characteristics of emerging azo substances, such as 2-cyanopropan-2-imemicarbazide (CABN). Although previous literature performs the calculation on related thermal hazard parameters of CABN, there is still exists a void for discussion in estimating the reaction model to avoid analogous hazards and enhance the existing thermal analysis. Based on the past literature, the reaction model is improved with thermogravimetric analysis as evidence. The revised thermal hazard parameters are calculated as the basis of control and mitigation measures, the kinetic model is used to estimate the modified safety parameters, and in the judgment of the runaway reaction, the critical temperature of the runaway is found by analyzing the influence of slight changes in ambient temperature on the reaction temperature. The results show that the critical temperature that causes CABN to enter the runaway reaction is delayed, and the hazard is lower than in the storage situation. Therefore, the thermal hazard to CABN mainly focuses on the safety environment and measures during storage.     

浅谈污水处理过程中配制聚合物溶液粘度研究

45℃反应30 d内,120 mg/L稳定剂稳定处理后中十二站污水和未经处理的北1-J3-P49井口采出污水配制的聚合物溶液的粘度保留率89.2～107%,并随反应时间的延长粘度保留率值有增高的趋势。添加120 mg/L稳定剂聚驱具有代表性北1-Ⅱ污水站污水配制的1200 mg/L聚合物溶液,5～1000 S-1上、下行剪切速率条件下的抗剪切性能优于污水直接的聚合物溶液,且随反应时间的延长,添加稳定剂污水配制的聚合物溶液抗剪切性能相对污水直接配制的聚合物溶液更加显著,稳定剂稳定聚合物溶液效果良好。仅添加120 mg/L稳定剂污水配制的聚合物溶液,30 d的粘度保留率89.2～107%,稳定剂稳定污水配制聚合物溶液的效果良好,且5～1000 S-1上、下行剪切速率条件下的抗剪切性优于污水直接的聚合物溶液,可以推测添加稳定剂污水配制的聚合物溶液的实际调驱效果好于污水直接配制的聚合物驱替液。     

Risk,vulnerability, and pragmatic inevitability: the conflict–disaster nexus and urban governance in Johannesburg,South Africa

This paper spotlights post-disaster relief provision in Johannesburg, South Africa, following the floods of 2016 in a bid to explore how local government and non-governmental actors in the country conceive of compounding vulnerability and conflict within urban disaster governance. It reveals the diverse strategies employed to navigate violent conflict during the cyclical occurrence of disaster and reconstruction that the predominantly migrant population experiences in the Setswetla informal settlement, adjacent to the Alexandra township in northern Johannesburg. Rendered visible in moments of disaster and recovery are the spatial politics and multidimensional nature of conflict. These phenomena unfold across various levels of urban governance and in the affected community and effectively construct a disaster citizenship that makes risk reduction and community cohesion impossible in the eyes of disaster managers. This research, based on a set of expert interviews, integrates conflict and disaster studies to shed light on how the conflict–disaster interface materialises, and is operationalised, in an urban setting.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.