Prediction models for multiphase-flow-induced corrosion of API X80 steel in CO2/H2S environment

In the offshore oil and gas industry, mainly focusing on the use of rigid or flexible pipes of subsea infrastructure applied to risers or flowlines, one of the greatest difficulties is the interpretation of the combined effects of the various correlated phenomena (hydrodynamic effects of intermittent flow, the effects of corrosivity of the environment in addition to variations in pressure, temperature, and dynamic loading). On the basis of this scenario, defining the degree of severity of each of the correlated system variables becomes of fundamental importance for establishing reliable criteria for selecting materials for subsea application. The established flow pattern directly affects the corrosion rate (or the pipe material mass loss), but the balance of other variables including possible changes in the physical and transported fluid chemical properties may increase the damage up to an order of magnitude, which is a piece of information normally not foreseen in design criteria. Therefore, to improve the understanding of the corrosion study influenced by multiphase flow, a testing loop was designed and assembled at the Corrosion and Protection Laboratory of the Institute for Technological Research, in which API X80 steel coupons were positioned in locations with a 0° and 45° inclinations. Tests were conducted by varying the partial pressure of the gaseous phase containing blends of CO₂ and H₂S with N₂ balance, mixed with the liquid phase containing light oil and heavy oil in water with salinity (NaCl)-simulating oil well conditions with 80% water cut. The main objective of this study is to establish models that can predict the corrosion intensity in conditions close to those obtained experimentally. To achieve results, the multiple regression and Box–Cox transformation methods were applied. These models will make possible damage prediction and optimization of matrix parameters for the multiphase-loop test.     

梁结构振动支承约束反力控制

支承或连接构件对梁结构的动力学性能有至关重要影响,必须保证其在振动过程中不发生破坏或者失效。通过合理设计和布局附加弹性支承可以实现对这些重要连接构件所承受约束反力的控制。应用微分变换法推导含附加支承的梁结构支承约束反力及其对于附加支承位置和刚度的灵敏度表达式,并通过优化设计附加支承位置和刚度实现具有弹性约束端的简支梁结构各支承约束反力的平衡,可提高结构的动力学性能。     

承插型盘扣式支撑架在地铁车辆段工程中的应用

盘扣式支撑架具有承载力强、方便安全、结实耐用等优点,被广泛应用于地铁施工。本文以广州市轨道交通十八号线万顷沙车辆段为例,对承插型盘扣式支撑架在地铁车辆段工程中的具体应用进行了详细说明,以期为同类工程提供参考。     

高应力软岩巷道锚注一体化联合支护技术应用

介绍了国家专利注锚剂、中空注浆锚索等产品,以国家能源麦朵山煤矿11采区2煤辅助运输巷为研究对象,采用数值模拟、理论分析、现场测试及井下试验相结合的综合研究方法,对11采区2煤辅助运输巷采用锚注一体联合支护技术,来实现对岩体裂隙注浆,使浆料与岩体结为一体,在围岩与支护体共同作用过程中,实现强岩增荷的作用,维护巷道的稳定性,实现工作面的安全生产。     

Generalized linear model based monitoring methods for high-yield processes

Emerge in technology brought well-organized manufacturing systems to produce high-quality items. Therefore, monitoring and control of products have become a challenging task for quality inspectors. From these highly efficient processes, produced items are mostly zero-defect and modeled based on zero-inflated distributions. The zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) distributions are the most common distributions, used to model the high-yield and rare health-related processes. Therefore, data-based control charts under ZIP and ZINB distributions (i.e., Y-ZIP and Y-ZINB) are proposed for the monitoring of high-quality processes. Usually, with the defect counts, few covariates are also measured in the process, and the generalized linear model based on the ZIP and ZINB distributions are used to estimate their parameters. In this study, we have designed monitoring structures (i.e., PR-ZIP and PR-ZINB) based on the ZIP and ZINB regression models which will provide the monitoring of defect counts by accounting the single covariate. Further, proposed model-based charts are compared with the existing data-based charts. The simulation study is designed to access the performance of monitoring methods in terms of run length properties and a case study on the number of flight delays between Atlanta and Orlando during 2012–2014 is also provided to highlight the importance of the stated research.     

放顶煤液压支架在土耳其IMABT煤矿现场的应用

通过对土耳其IMBAT煤矿地质条件的分析研究，结合多种选型计算方法的验证，最终选择了合适的设备，使该矿的开采效率得到较大提升。同时也扩大了我国的煤炭开采装备应用范围。根据在土耳其IMBAT煤矿现场多年的服务经验，将理论与实践相结合，就综采放顶煤液压支架在土耳其IMABT煤矿现场的应用进行了论述。     

高预应力高强度锚索支护在深井高应力巷道的应用

以红四煤矿深井高应力巷道支护为例,在研究地应力状态、围岩强度、岩石成分的前提下,通过数值模拟方式确定了高预应力高强度高刚度锚杆锚索联合支护方案。通过对试验巷道变形移近量监测发现,两帮移近变形呈稳定状态的时间提前,巷道顶底板变形状况有所改善。     

Development of a statistical model to classify driving stress levels using galvanic skin responses

Many studies have demonstrated the strong relationships between physiological responses and driving stress, but they have done little to build a model that could be used to identify a driver's stress accurately in real time. The objective of this study is to develop a model that accurately classifies driving stress by monitoring physiological responses—specifically galvanic skin response (GSR). GSR data were collected from nine drivers with licenses obtained in the US in real road driving situations with two stress conditions—rest period (low stress) and highway or city driving (high stress). The validation drive was performed by one driver with licenses obtained in South Korea in real long‐term road driving situations with two stress conditions—rural area (low stress) and highway or highway under construction (high stress). Those two conditions were used to build a binary logistic regression model to classify low stress or high stress based on a driver's measured hand GSR. The overall classification accuracy of the developed model was found to be 85.3%, and the accuracy of cross validation, with a testing dataset, was found to be 83.2%. A simple logit model was developed to identify drivers' stress by incorporating their GSR data. The developed model can be embedded in a wearable device equipped with GSR sensors for drivers to detect their stress level in real time.     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.