黄沙坪碳酸盐型尾矿氧化剖面特征及酸化机制

湖南黄沙坪铅锌矿尾矿富含碳酸盐及金属硫化物,属于碳酸盐型尾矿。通过对该尾矿铅垂剖面的结构构造,以及尾矿的矿物成分、化学成分、Paste pH值和Carb-ANC(酸中和能力)等分析,探讨碳酸盐型尾矿的酸化特征及其机制。研究表明:黄沙坪尾矿发育层状构造,即表面为胶结硬化层(hardpan),之下依次为弱胶结层、尾砂层及底层(接近尾矿潜水面)。尾矿的酸中和矿物主要为方解石;尾矿的产酸矿物以黄铁矿和磁黄铁矿为主,但黄铁矿的抗氧化能力和产酸能力均较强于磁黄铁矿。尾矿剖面自下而上(或由早至晚),氧化作用逐渐增强,磁黄铁矿先于黄铁矿氧化(蚀变)分解并逐渐消失,此时尾矿的酸中和能力仍较强,尾矿保持近中性环境;之后,黄铁矿开始氧化产酸并大量消耗方解石,尾矿开始酸化;至尾矿表层方解石消耗殆尽,而黄铁矿仍持续释酸,尾矿酸化加剧。表层强氧化环境使纤铁矿大量沉淀并胶结尾矿颗粒形成硬层。黄沙坪尾矿由于含有大量的抗氧化能力和产酸能力均较强的黄铁矿,其酸水(+重金属)释放的潜力较大并且持续的时间也较长。     

沿海地区土地利用格局变化对近岸海域环境的影响研究——以滨海新区为例

以天津市滨海新区为例,利用遥感影像分析其1984年-2008年间土地利用变化,并讨论其驱动力及对渤海湾的影响.结果表明:从1984年-2012年间,盐地减少342.95 km2,建设用地的面积增加1 105.04 km2.发生这种转变的驱动力主要是人口增加、经济发展和土地政策调整.土地利用类型的转变导致人为活动方式发生改变,工业迅速发展,最终导致渤海湾沉积物中营养盐含量和重金属含量增加.     

The legacy of migration in response to climate stress: learning from the Gilbertese resettlement in the Solomon Islands

The long‐term threat of sea‐level rise to coral atoll and reef island communities in Kiribati, Tuvalu and other nations has raised the possibility of international migration. Historical resettlements in the Pacific may provide valuable insight into the long‐term effect of future climate change‐related migration on communities. This study evaluates the challenges faced by Gilbertese people resettled from modern‐day Kiribati to Ghizo in the Solomon Islands by the British colonial administration in the mid‐1900s. Drawing upon field interviews (n = 45) conducted in 2011 and the available historical literature, the study examines the circumstances of the initial failed resettlement in the equatorial Phoenix Islands, the subsequent relocation to Ghizo, and the recent concerns of the Gilbertese in Ghizo. Focus is placed on the struggle to recover from the 2007 tsunami that devastated the unprepared community. The analysis reveals that uncertainty about land tenure (raised by 61% of respondents) persists 60 years after resettlement, and is linked to the ability to recover from the tsunami, tensions with the Melanesian population, concerns over political representation, cultural decline, and education and employment opportunities. The Gilbertese experience can serve as a cautionary tale for policymakers considering mechanisms for facilitating climate change‐related migration.     

上推流厌氧反应器连续干发酵猪粪产沼气试验研究

为解决猪粪连续干发酵存在的氨抑制和出料难等难题,在温度(25±2)℃、有机负荷为干物质(TS)4.44 g.(L.d)-1的条件下,采用上推流式厌氧反应器(UPAR)对猪粪进行连续干式发酵试验,研究了猪粪干发酵过程的产气情况、氨抑制现象和出料流动性,并考察了上推流厌氧反应器进行猪粪干发酵的可行性.试验采用4种不同TS质量分数(20%、25%、30%、35%)的猪粪作为原料,经160 d的运行.结果表明,进料TS质量分数对发酵过程有很大的影响,4种不同进料TS质量分数稳定的池容产气率分别为2.40、1.73、0.89、0.62 L.(L.d)-1,进料TS质量分数为20%、25%和30%的产气效率明显优于进料TS=35%的产气效率.随着进料TS质量分数从20%增加到35%,氨氮质量浓度>2 300 mg.L-1会出现明显的产气抑制.在进料TS=35%时,氨氮质量浓度能达到3 800 mg.L-1,产气速率相对于进料TS=20%递减74.1%.当进料TS达到35%、出料TS质量分数达到17.1%、流速<0.002 m.s-1时,UPAR不能顺利出料.     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

地质高背景与污染叠加区不同土地利用方式下土壤重金属分布特征

黔西北喀斯特地质高背景和历史遗留土法炼锌污染叠加,导致土壤重金属污染严重.为了解不同土地利用方式对土壤重金属分布的影响,选择威宁县某在产锌粉厂小流域为研究区域,共采集表层和剖面土壤样品126件,对农用地、荒地和林地土壤重金属(Cd、As、Pb、Cu和Zn)的空间分布、来源和生态风险进行分析和评价.结果表明,研究区表层土...     

售票机的不同位置对地铁车站火灾烟气特性影响的CFD模拟

合理的空间布局对地铁车站消防安全具有十分重要的作用,而售票机的布置方位是影响地铁车站火灾烟气特性的一个主要因素。以某城市在建的地铁站站厅层为物理模型,采用FDS软件对售票机在不同位置情况下火灾的烟气流动情况进行模拟分析,通过比较邻近售票机处的疏散出口的温度、烟气浓度以及能见度,提出了售票机和疏散出口之间较为合理的距离,为实际工程提供可参考的依据。     

基于多信息检测的车辆智能防撞预警技术研究

运用多源信息检测技术对驾驶状态进行综合检测,利用架设在车辆外部的摄像机获取本车与前方车辆之间的相对距离、相对速度信息,利用架设在车辆内部的摄像机获取驾驶人状态信息,通过综合判断降低车辆防撞检测的漏警率、误警率以及冗余报警率;构建车辆行驶安全判定规则,对行车实时危险和潜在危险进行检测、分析与判断,获取可靠的防撞警示提醒;使反应时间、相对距离、相对速度3个方面都能得到优化控制,从而避免车辆碰撞事故;同时也为提高车速、增加道路通行能力、实现自动驾驶等奠定良好的基础。     

2004—2008年我国隧道施工事故统计分析

为了解我国隧道施工事故的发生规律,采用柱状图和事故发生趋势图等方法对2004—2008年隧道施工事故进行统计与分析,获得了事故次数与死亡人数的年度分布和发展趋势、不同事故类型发生事故次数、事故类型占总事故数百分比、地区分布、事故等级分布等统计特征。找出2004—2008年隧道施工事故的发生规律:事故次数和死亡人数均呈下降趋势;事故类型以坍塌、物体打击、透水、冒顶片帮为主,其中坍塌是隧道施工过程中的第一要害;事故主要集中在中西部地区,东部地区则很少;多数事故为较大事故,发生重大和特别重大事故较少,但多为恶性事故。同时根据隧道施工事故发生规律,提出隧道施工安全管理的对策与建议。