中国碳捕集、利用与封存技术研发与示范

碳捕集、利用与封存(CCUS)是一项新兴的、具有大规模减排潜力的技术,有望实现化石能源使用的CO2近零排放,被认为是进行温室气体深度减排最重要的技术路径之一.开展CCUS技术的研发和储备,将为中国未来温室气体减排提供一种重要的战略性技术选择,受到政府、企业和学术界的广泛关注.本文从CCUS技术产生和发展的背景出发,概括了该技术的特点及存在的主要同题,总结了当前中国CCUS技术政策,介绍了中国CCUS技术研发、试点示范、国际合作等的开展情况,并通过与欧美等发达国家CCUS技术发展与示范活动现状的对比,初步分析了中国CCUS技术发展方面存在的主要不足.为进一步推动我国CCUS技术的研发与示范,文章提出应加强国家层面的技术政策指导和宏观协调、引导资源有效配置;推动建设行业间的CCUS技术合作平台;重视CCUS来来技术应用软环境建设等有针对性的建议.     

基于多态模糊BN的石化动设备故障概率定量风险评估

石化行业的压缩机、泵等动设备故障模式多样,故障概率的不确定性较大,常用的风险评估方法无法实现对石化行业动设备失效概率的定量评估,限制了设备安全管理的准确性和有效性.基于贝叶斯网络(BN)可定量计算复杂系统失效概率的特点,利用故障模式与影响分析(FMEA)方法获取动设备风险事件的因果关系,将其映射为BN,并利用BN中节点...     

Value of information in product recovery decisions: a Bayesian approach

There is a widespread recognition of the need for better information sharing and provision to improve the viability of end-of-life (EOL) product recovery operations. The emergence of automated data capture and sharing technologies such as RFID, sensors and networked databases has enhanced the ability to make product information; available to recoverers, which will help them make better decisions regarding the choice of recovery option for EOL products. However, these technologies come with a cost attached to it, and hence the question ‘what is its value?’ is critical. This paper presents a probabilistic approach to model product recovery decisions and extends the concept of Bayes' factor for quantifying the impact of product information on the effectiveness of these decisions. Further, we provide a quantitative examination of the factors that influence the value of product information, this value depends on three factors: (i) penalties for Type I and Type II errors of judgement regarding product quality; (ii) prevalent uncertainty regarding product quality and (iii) the strength of the information to support/contradict the belief. Furthermore, we show that information is not valuable under all circumstances and derive conditions for achieving a positive value of information.     

A RISK-BASED MODEL FOR DROUGHT CONTINGENCY PLANNING1

ABSTRACT: A risk-based model is presented for determining action levels in real-time operation of public water supplies under drought conditions. The model, applicable to surface water supplies, is built around the almost universally adopted structure for drought management, a sequence of increasingly stringent measures to reduce demand. Action levels are determined by finding minimum storage levels that will satisfy a prescribed set of probability constraints over the remainder of a drawdown-refill cycle. Results are presented for the City of Raleigh, NC.     

CN算法应用于我国23次强震的预测研究

本文用CN算法研究了我国近期大陆东部和西部南北带及邻近区域共23次强震前的TIP,即强震发生概率增长时间。结果表明,18次强震发生在被判定的强震发生概率增长时间的TIP内。TIP警戒约占总研究时空域的30％,获得了较好的中期地震预测效果。表明CN算法可作为强震中期预测的手段之一。     

FLOOD FREQUENCY CHARACTERISTICS OF SOME ARIZONA WATERSHEDS1

The flood frequency characteristics of 18 watersheds in southeastern Arizona were studied using the log-Boughton and the log-Pearson Type 3 distribution. From the flood frequency study, a generalized envelope for Q₁₀₀ for watersheds 0.01 to 4000 mi² in area has been produced for southeastern Arizona. The generalized envelope allows comparisons to be made among the relative flood characteristics of the watersheds used in the study and provides a conservative estimate of Q₁₀₀ for ungaged watersheds in the region.     

FLOOD FREQUENCY ANALYSIS FOR SMALL WATERSHEDS IN SOUTHERN ILLINOIS1

ABSTRACT: Twenty-two gaging stations were selected for developing a regional flood frequency curve for small (area less than 2 square miles) watersheds in southern Illinois. Five probability functions were compared, and the extreme value type I function was selected to develop the regional flood curve. The curve was generated with the index flood method and also another empirical method that related the function parameters to the watershed area. Estimated peak discharges with various return periods were compared with the results obtained from multiple regression analysis.     

两种定性天气预报模型的对比分析

以南京１９６５～１９９４ 年４ 月平均气温作为预报量, 选取前期５００ ｈ Ｐａ 月平均高度场相关因子, 分别建立了事件概率回归预报模型和神经网络预报模型。通过对比分析发现, 在同等条件下,由于神经网络方法能更好地反映预报量与预报因子间的非线性关系, 并能有效避免采用事件概率回归方法预报建模时, 对预报因子分级造成信息损失的缺点。因此, 其拟合和预报效果明显优于传统的概率回归预报方法