Investigation of pool spreading and vaporization behavior in medium-scale LNG tests

A failure of a Liquefied Natural Gas (LNG) tanker can occur due to collision or rupture in loading/unloading lines resulting in spillage of LNG on water. Upon release, a spreading liquid can form a pool with rapid vaporization leading to the formation of a flammable vapor cloud. Safety analysis for the protection of public and property involves the determination of consequences of such accidental releases. To address this complex pool spreading and vaporization phenomenon of LNG, an investigation is performed based on the experimental tests that were conducted by the Mary Kay O'Connor Process Safety Center (MKOPSC) in 2007. The 2007 tests are a part of medium-scale experiments carried out at the Brayton Fire Training Field (BFTF), College Station. The dataset represents a semi-continuous spill on water, where LNG is released on a confined area of water for a specified duration of time. The pool spreading and vaporization behavior are validated using empirical models, which involved determination of pool spreading parameters and vaporization rates with respect to time. Knowledge of the pool diameter, pool height and spreading rate are found to be important in calculating the vaporization rates of the liquid pool. The paper also presents a method to determine the vaporization mass flux of LNG using water temperature data that is recorded in the experiment. The vaporization rates are observed to be high initially and tend to decrease once the pool stopped spreading. The results of the analysis indicated that a vaporization mass flux that is varying with time is required for accurate determination of the vaporization rate. Based on the data analysis, sources of uncertainties in the experimental data were identified to arise from ice formation and vapor blocking.     

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.     

综合拉底、扩漏和出矿全过程的底部结构安全性颗粒流模拟技术研究

为探明自然崩落法从拉底崩落至出矿全过程中底部结构节理连续扩展过程与破坏过程规律，采用PFC2D软件，结合RocLab软件反演节理岩体参数，建立229 m×129 m的岩体扩大模型，对拉底崩落至出矿过程进行连续综合计算。研究结果表明:模拟结果及破坏现象与现场监测结果相近，建模与细观参数反演方法适用于该类岩体工程研究；裂纹扩展贯通导致岩体失稳破坏，破坏过程与裂纹扩展具有相对应的阶段特征，裂纹扩展分为崩落前稳定扩展期，崩落与聚矿槽开挖过程加速扩展期和后续持续扩展期；结构破坏分为蕴育过程，扰动失稳过程和宏观破坏过程；较大范围的宏观破坏主要在出矿阶段。该模拟方法与结果可为底部结构维护以及节理岩体长期稳定性研究提供参考。     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.     

The use of nanovermiculite catalyst in the study of removal of the organic load and degradation of atrazine via ozone process in RPB reactor

Abstract

The main objective of this study is the degradation of a synthetic solution of atrazine by a modified vermiculite catalyzed ozonation, in a rotating packed bed (RPB) reactor. A 0.5?L RPB reactor was used to perform the experiments, using a Central Composite Design (CCD) response surface to construct the quadratic model based on the factors: pH, catalyst concentration and reactor rotation frequency. The response variable was the removal of the organic load measured in terms of Chemical Oxygen Demand (COD). After the complete quadratic model was constructed through the response surface, the COD degradation process had an optimal removal of 41% under the following conditions: pH 8.0, rotation of 1150?rpm and catalyst concentration 0.66?g L^?1.     

State of research on the automation of HAZOP studies

For more than 30 years, multiple research groups have worked on the automation of hazard and operability (HAZOP) studies, or more specifically on the hazard identification process. So far, very few of these approaches have been used in the chemical process industry. Automatic hazard identification is a knowledge-intensive process that demands high standards with regard to the way in which knowledge is stored and made available. There are various suitable approaches to the qualitative modeling of processes and plants, which are the foundation for reasoning systems that are used for the identification of hazards. Additionally, there are quantitative methods that are based on process simulations and can be used to identify potential hazards. The investigation of the state of research demonstrates that there are sophisticated technologies for automated systems that include powerful reasoning techniques. The benefits and shortcomings of existing technologies are discussed with regard to their industrial applicability. Often, the quality of the necessary specific and generic knowledge is not sufficient to detect potential hazardous events and operational malfunctions. Computer-aided HAZOP systems should be integrated with computer-aided design- or process simulation software using common data models based on the digital representation of the process plant. In order to be used by HAZOP practitioners automated systems need to be comprehensive, serve as specialized decision support systems, and be tested and evaluated using round robin tests.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

BIM技术在钢结构建筑抗震减灾管理中的应用

为提高钢结构建筑抗震减灾性能,降低建筑坍塌风险,研究BIM技术在钢结构建筑抗震减灾管理中的应用方法是非常必要的。利用BIM技术在钢结构建筑整体建造中的动态建模、可视化、信息共享管理以及抗震减灾性能检测等优势,在建筑抗震减灾管理决策阶段,选出最优方案,针对方案中影响建筑抗震减灾性能风险制定管理策略;在设计阶段,创建三维建筑模型,调整存在风险建筑模型数据信息,并通过振荡模拟分析降低风险;在施工阶段,管理钢结构建筑材料,提升建筑构件的刚度,并依据建筑施工阶段的抗震减灾管理原则制定相应的管理方法,实现对钢结构建筑的抗震减灾管理。结果表明,BIM技术可实现抗震减灾管理中的地震波检测,检测结果精准可靠;可提升建筑构件的刚度与承载力及建筑的抗震减灾性能,降低建筑坍塌等风险,实现钢结构建筑抗震减灾管理。     

Machine learning based quantitative consequence prediction models for toxic dispersion casualty

Incidental release of toxic chemicals can pose extreme danger to life in the vicinity. Therefore, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction to evaluate possible toxic dispersion life-threatening consequences. In this work, a toxic chemical dispersion casualty database that contains 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model to estimate the affected area caused by toxic chemical release within a certain death rate. The results show that the developed QPCR model can predict the toxic dispersion casualty range with root mean square error of maximum distance, minimum distance, and maximum width less than 0.2, 0.4, and 0.3, which indicates that the constructed model has satisfying accuracy in predicting toxic dispersion ranges under different lethal consequences. The model can be further expanded to accommodate more toxic chemicals and leaking scenarios.