Analysis of the corrosion failure causes of shale gas surface pipelines

A shale gas gathering and transportation pipeline in a good block in Sichuan Province started leaking after less than a year of operation. To investigate the causes of corrosion of the sulfate-reducing bacteria (SRB), optical microscopy, scanning electron microscopy, and X-ray diffraction were used to analyze the corrosion and perforation of the shale gas surface pipeline in conjunction with bacterial corrosion simulation experiments. The results showed that the pipeline material (L360N) conformed to the requirements of the American Petroleum Institute 5 L standard and that extracellular polymeric substances were present in the corrosion pits. The corrosion products mainly included FeCO₃, FeS, CaCO₃, MgCO₃, and Fe mineralization. At 40°C, the uniform corrosion rate of L360N in the simulation experiment was 0.234 mm/a, and the local corrosion rate was 0.458 mm/a. SRB, saprophytes, and iron bacteria were detected in the on-site water medium and corrosion products, indicating that the main causes of shale gas pipeline corrosion are bacterial and CO₂ corrosion.     

基于传统清管球接收器的滤杂防堵工艺设计

清管球回收操作过程中,清管球接收器的泄压、排液和惰化导致泥、砂、垢等杂质积聚在清管球接收器中,并随油气物流进入闭排系统管线,造成杂质流失、闭排系统管线堵塞和压力无法完全释放,影响对海底油气管道腐蚀、析蜡、结垢及地层出砂等情况的判断,存在很大的安全隐患。通过对清管球接收器实施工艺改造,使清管球接收器具备滤杂防堵功能和自动取样功能,有效解决了闭排管线堵塞、杂质流失和安全隐患问题。该工艺研究对于海上油气田海底管线的运营和维护具有较高的借鉴意义。     

Two-stage optimization for machine learning workflow

Machine learning techniques play a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners.For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as AutoML.In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.     

From a local community to a global influence. How elBulli restaurant created a new epistemic movement in the world of haute cuisine

Location plays a major role in the building of artistic, technological or scientific movements that emerge in specific locations before achieving a worldwide reach. However, a deeper understanding is needed about the interplay of local/global knowledge dynamics in the epistemic construction of a movement. Based on an in-depth longitudinal study of a critical case, this article sheds light on these issues by analysing the case of ‘techno-emotional cuisine’, a global gastronomic movement initiated and led by chef Ferran Adrià and his team at the restaurant elBulli in Catalonia (in Northern Spain). The results suggest that the dynamics of formation of a new epistemic movement depend on the form and nature of the interactions between the local buzz and global pipelines, and on the capacity of the originating community to develop and diffuse the new rules and ‘episteme’ on a global scale while consolidating them locally.     

Hydrogen carriers: Production,transmission, decomposition,and storage

Recognizing the potential role of liquid hydrogen carriers in overcoming the inherent limitations in transporting and storing gaseous and liquid hydrogen, a complete production and use scenario is postulated and analyzed for perspective one-way and two-way carriers. The carriers, methanol, ammonia and toluene/MCH (methylcyclohexane), are produced at commercially viable scales in a central location, transmitted by rail or pipelines for 2000 miles, and decomposed near city gates to generate fuel-cell quality hydrogen for distribution to refueling stations. In terms of the levelized cost of H₂ distributed to the stations, methanol is less expensive to produce ($1.22/kg-H₂) than MCH ($1.35/kg-H₂) or ammonia ($2.20/kg-H₂). Levelized train transmission cost is smaller for methanol ($0.63/kg-H₂) than ammonia ($1.29/kg-H₂) or toluene/MCH system ($2.07/kg-H₂). Levelized decomposition cost is smaller for ammonia ($0.30–1.06/kg-H₂) than MCH ($0.54–1.22/kg-H₂) or methanol ($0.43–1.12/kg-H₂). Over the complete range of demand investigated, 10–350 tpd-H₂, the levelized cost of H₂ distributed to stations is aligned as methanol « ammonia ~ MCH. With pipelines at much larger scale, 6000 tpd-H₂, the levelized cost decreases by ~1 $/kg-H₂ for ammonia and MCH and much less for methanol. Methanol is a particularly attractive low-risk carrier in the transition phase with lower than 50-tpd H₂ demand.     

基于动态贝叶斯网络的海底管道点蚀疲劳损伤失效模型研究

目的研究海底管道在点腐蚀和腐蚀疲劳双重影响下的整个破坏过程,基于动态贝叶斯网络构建系统失效模型,对海底管道系统不同疲劳寿命下的失效概率进行预测。方法将点蚀疲劳损伤过程分为腐蚀点成核、腐蚀坑增长、短裂纹扩展和长裂纹扩展四个阶段,采用蒙特卡洛模拟方法对腐蚀点形成到短裂纹发生前的管道破坏过程进行分析,结合疲劳裂纹扩展的动态贝叶斯网络结构图,在充分考虑相关影响因素不确定性的基础上,为海底点蚀管道系统提出一种创新性的概率分析方法,对点蚀管道疲劳寿命的失效概率进行科学预测。结果结合实例分析,通过蒙特卡洛模拟方法,求解得出腐蚀坑增长转变为短裂纹扩展状态的临界裂纹尺寸为0.8mm。采用动态贝叶斯网络分析方法,对未经受维修保养的点蚀管道进行疲劳寿命预测,当管道运行到第35年时将会面临失效风险。结论所构建的模型可以对海底点蚀管道腐蚀疲劳寿命失效概率进行合理预测,通过观测相关影响参数的变化,及时更新预测结果,有助于为海底管道系统制定有效的维修策略。     

平旺地区供水管网的技术管理

介绍了大同煤矿集团公司平旺地区供水系统概况及供水管网技术管理的内容。     

地下燃气管道导向钻进非开挖敷设技术

介绍了燃气工程中采用的地下管道导向钻进非开挖敷设技术基本原理、施工工艺及优缺点     

简化为弹性地基梁的地下管线的随机反应分析

本文利用随机振动理论 ,研究了地下管线在大空间地面运动过程中的动力反应问题。在建立管线模型时 ,接头简化为刚性接头 ,同时管线简化为弹性地基梁。在分析过程中 ,不考虑管线的弯曲应力和应变 ,仅考虑轴向应力和应变。采用随机反应分析的方法 ,通过输入地面运动的功率谱 ,求出管线在轴向的应力及应变的功率谱