煤油气共生矿井瓦斯含量主控因素分析及工作面瓦斯治理

针对崔家沟煤矿煤油气共生、瓦斯含量分布复杂、瓦斯涌出不均的特点,在分析瓦斯含量影响因素的基础上,采用灰熵关联分析法确定影响瓦斯含量的主控因素,通过顶板岩层含油强度研究2303工作面瓦斯含量分布规律,提出2303工作面瓦斯治理方案并进行应用。结果表明:顶板岩层含油强度与瓦斯含量的灰熵关联度最大为0.993 3,是影响崔家沟煤矿4-2号煤层瓦斯含量的主控因素;根据顶板含油强度可将2303工作面划分为2个瓦斯含量分布单元;采取高位钻孔与顶板走向长钻孔相结合的工作面瓦斯治理方案可有效降低高瓦斯含量分布单元回采时回风流中的瓦斯浓度,能有效确保工作面的安全高效回采。     

Influence assessment of inlet parameters on thermal risk and productivity: Application to the epoxidation of vegetable oils

The influence of inlet parameters on the production and thermal risk of complex chemical systems can be cumbersome to evaluate. To determine the optimum safe operating conditions, one needs to solve complex differential equations derived from energy and material balances. This robust approach cannot be made on-site, and it is essential to propose simplest tools to evaluate rapidly the performance and safety of some operating conditions. This is the aim of this paper that establishes explicit relationships between the production and thermal risk parameters, and the inlet parameters. In addition, it also proposes a Pareto chart that can be used to make the tradeoff between safety and performance. Such relationships and chart were developed for the production of epoxidized cottonseed oil under isoperibolic and semi-batch mode. The kinetic model developed by Zheng et (Zheng et al., 2016). was used. First, a numerical approach, i.e., least square method, was used to find explicit relationships between thermal risk parameters, production parameters and six inlet parameters. The use of such an approach allows a better understanding of this process. Second, safety and performance indicators are proposed and discussed to evaluate the operating conditions thanks to a simple and intuitive schema. Besides, this approach can be used to find the optimum conditions more rapidly.     

Quantitative risk analysis of toxic gas release caused poisoning—A CFD and dose–response model combined approach

Some major toxic gas release accidents demonstrate the urgent need of a systematic risk analysis method for individuals exposed to toxic gases. A CFD numerical simulation and dose–response model combined approach has been proposed for quantitative analysis of acute toxic gas exposure threats. This method contains four steps: firstly, set up a CFD model and monitor points; secondly, solve CFD equations and predict the real-time concentration field of toxic gas releases and dispersions; thirdly, calculate the toxic dose according to gas concentration and exposure time; lastly, estimate expected fatalities using dose–response model. A case study of hydrogen sulfide releases from a gas gathering station has been carried out using a three dimension FLUENT model. Acute exposure fatalities have been evaluated firstly with a simplified ideal model which assumes workers stay at original exposure location without moving. Then a comparison has been made with a more realistic model which assumes workers start evacuating according to a prearranged course as soon as hydrogen sulfide detection system alarms. These two models represent the worst and best emergency response effects, respectively, and the analysis results demonstrate significant differences. Results indicate that the CFD and dose–response combined approach is a good way for estimating fatalities of individuals exposed to accidental toxic gas releases.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

基于模糊随机可靠性的边坡稳定性评价

影响边坡稳定性的因素中包含有很多的不确定性,这些不确定因素既具有随机性也具有模糊性。基于模糊分析理论,建立了模糊随机可靠性分析模型,提出了边坡模糊随机可靠性分析的点估计法,并运用该方法对一露天矿边坡稳定的的可靠性进行分析,通过对影响边坡稳定的各个力学参数进行模糊处理,然后应用统计矩点估计方法估计边坡的安全系数均值和可靠度,得出边坡的失稳概率。这种方法由于考虑到了各个力学参数的模糊性,比较符合客观实际,因而更具有实际指导意义。     

Combustion products toxicity risk assessment in an offshore installation

Products of a hydrocarbon fire accident have both chronic and acute health effects. They cause respiratory issues to lung cancer. While fire is the most frequent phenomenon among the offshore accidents, predicting the contaminants’ concentration and their behavior are key issues. Safety measures design, such as ventilation and emergency routes based only on predicted contaminants’ concentration seems not to be the best approach. In a combustion process, various harmful substances are produced and their concentration cannot be added. The time duration that any individual spends in different locations of an offshore installation also varies significantly. A risk-based approach considers the duration a person is exposed to contaminants at various locations and also evaluates the hazardous impacts. A risk-based approach has also an additivity characteristic which helps to assess overall risk.Through the current study, an approach is proposed to be used for risk assessment of combustion products dispersion phenomenon in a confined or semi-confined facility. Considering CO, NO₂ and CH₄ as the contaminants of concern, the dispersion of the substances over the layout of the facility after a LNG fire is modeled. Considering different exposure times for three major parts of the facility including the processing area, office area and the accommodation module, the risk contours of CO, NO₂ and CH₄ over the entire facility are developed. The additivity characteristic of the risk-based approach was used to calculate the overall risk. The proposed approach helps to better design safety measures to minimize the impacts and effective emergency evacuation planning.     

Silo vulnerability: structural aspects

This document sets out a diagnostic approach regarding the mechanical resistance of silos to an internal explosion that could occur within them. Even though prevention is logically a priority, risk analysis is about studying contingencies and vulnerability. Presented herein are risk assessment methods — including methods for quantifying the effects of an explosion on structure, as well as methods for assessing structure vulnerability. The data required for the vulnerability research approach are defined. The location of sampling holes, which may prove necessary for diagnosis purposes, is more fully developed for tower silos. A stability analysis for the different silo components is established. This approach is useful for risk analysts and allows them to highlight the sensitive elements in order to integrate the protection aspect into an existing facility or a facility to be constructed.     

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.     

Sudden-onset disaster resilience considering functionality improvement planning: An upstream oil and gas company

The present paper presents a quantitative approach to evaluate organizational resilience in sudden-onset disasters considering preparedness actions (PAs). The resilience triangle concept is extended and gradual improvement of the level of functionality (LF) strategy is examined as PAs. Robustness and rapidity measures are considered as loss of LF and recovery time to indicate the overall disaster resilience. Besides, the resourcefulness and redundancy measures are considered pre-determined response plans and PAs aimed to improve disaster resilience. Two mathematical models are developed by applying these measures. Thereafter, graphical analytics are conducted to demonstrate the effectiveness of PAs and provide a better outlook for decision-makers. Then, mathematical analyses are conducted to show how the PAs affect resilience measures. It is mathematically proved that PAs exert considerable impacts on recovery time, loss of LF, and robustness. To illustrate the application of the proposed approach, it is applied to an upstream oil and gas company in the field of exploration and production. The results suggest that the approach is significantly effective in disaster response, planning, and mitigation.     

Predictive Models of Lumbar Loadings When Handling Boxes

Back problems resulting from the compression forces on the intervertebral disks during manual material-handling tasks are an important problem affecting workers in various industries. The quantification of these forces using intradiscal pressure or biomechanical modeling is complex, time consuming, and costly, and these methods cannot be readily used in the workplace to estimate loadings on the lower back. The objective of this study was to develop a predictive model that would allow the estimation of lumbar loadings for lifting and lowering boxes using easily measured anthropometric variables and variables related to the task. A dynamic and planar segmental model and a model of internal forces at L5/S1 were used to determine the compression forces on the lower back. Two predictive models, a field model and a laboratory model, were developed to estimate the compression forces when lifting or lowering 3.3 kg to 22.0 kg boxes between heights of 15 cm and 185 cm. Both models were validated by an examination of the residuals. Their predictive performance was also compared, with the laboratory model offering a slightly better prediction than the field model. Thus, these equations represent a practical tool for a better planning of handling tasks in the working environment with the purpose of reducing the back injuries of workers.     

顶板走向高位钻孔瓦斯抽采技术的研究及应用

为了解决由于采空区及邻近煤层瓦斯的涌人而造成的工作面上隅角瓦斯超限问题,提出了运用顶板走向高位钻孔瓦斯抽采技术,对采空区及邻近煤层瓦斯进行抽采,进而解决上隅角瓦斯超限问题的方法。利用分源预测法对工作面瓦斯涌出源进行了分析,并理论计算了采空区冒落带和裂隙带的高度范围,结合矿井具体情况,确定了合理的高位钻孔参数,并对作用效果进行了现场考察。研究表明：高位钻孔瓦斯抽采技术,能有效地解决工作面上隅角瓦斯超限问题,降低回风流中瓦斯体积分数,并提高了工作面的推进速度,有效地保证了工作面的安全回采。     

Correlations for blast effects from vented dust explosions

Empirical correlations are often used to estimate safety distances in the event of dust explosions. In Europe, there are two main correlations available in VDI 3673 and EN 14491. Whereas the VDI 3673 correlation is based on experimental investigations of vented dust explosions using large vessels, and assumes an external explosion, the EN 14491 correlation is derived from SKJELTORP et al. internal explosion tests in ammunition storage facility. This paper provides an overview of the experimental studies of vented gas and dust explosion. It aims to highlight the main findings of such studies, while defining the conditions for a secondary explosion to occur and comparing experimental data with the application of standards, in order to propose elements to choose the more appropriate correlation.     

新安煤田煤与瓦斯突出特征及防治措施

为解决新安煤田煤与瓦斯突出影响因素众多和防治难度较大等问题,运用瓦斯地质理论分析了新安煤田煤与瓦斯突出特征及控制因素,并提出了有针对性的煤与瓦斯突出防治措施。研究表明:地质构造是控制新安煤田煤与瓦斯突出分布的主要地质因素;构造应力是控制新安煤田煤与瓦斯突出的主要动力因素;新安煤田煤与瓦斯突出吨煤瓦斯涌出量较大,是增加煤与瓦斯突出发生可能性及危险性的重要因素。基于此,从突出危险性预测、防治突出措施及安全防护措施等三个方面提出了多项煤与瓦斯突出防治措施。     

固体废弃物填埋场产气率的估算

填埋气体的产生过程可分为需氧分解、需氧/酸性分解、向厌氧分解过渡、厌氧分解和稳定阶段5个.填埋场产气率的估算是填埋场气体收集和控制系统设计的重要环节.本文对填埋场气体和集气井产气量的估算方法进行研究.给出了2种较简单的填埋场气体量的估算方法.并用该方法对一个三角形布置的集气井的产气量进行了估算.结果表明,这2组公式可用于填埋场的产气率的估算.     

Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

煤与瓦斯突出预测的QGA-LSSVM模型

为快速、有效地对煤与瓦斯突出类型作出预测,运用灰色关联和因子分析模型对所选主要的判别指标进行分析提取,利用量子遗传算法(QGA)对最小二乘支持向量机(LSSVM)的参数作寻优处理,最终建立QGA-LSSVM煤与瓦斯突出预测模型。选取从砚石台矿区历史实测的数据,以96∶20的比例对该模型进行训练与测试,并将预测结果与其他预测模型的预测效果进行了比较。研究结果表明:对判别指标进行灰色关联分析可以有效去除对煤与瓦斯突出影响作用小的指标;用因子分析进行公共因子提取,可以有效减少数据信息冗余;利用QGA优化的LSSVM模型能使结果避免陷入局部最优解,用该模型可以有效预测煤与瓦斯突出类型,误判率为0。     

Improving flammable mass estimation for vapor cloud explosion modeling in an offshore QRA

Presence of congestion and confinement in offshore modules due to limited availability of space make Vapor Cloud Explosions (VCEs) a significant contributor to risk. There are several methods available for quantifying the blast overpressure generated over distances and time. The approaches range from one-dimensional analysis using correlation models to 3-D analysis using Computational Fluid Dynamics (CFDs). The correlation models are easy to use and well-suited for assessing a number of credible VCE scenarios. However, the overpressure results predicted by correlation models depend on a good estimate of flammable mass. This paper proposes a method to improve the estimation of flammable mass. The UKOOA Ignition model developed by the Energy Institute London is used to estimate the flammable mass; and is modified to account for the effect of mitigation measures on release rate. A directional probability for wind is also added to the model. The proposed model takes into consideration the platform geometry and offshore conditions for each scenario, release location and direction, and wind direction. An offshore production platform with three deck levels is presented as an example case. The flammable mass is also computed using CFD and the results are compared to that of the proposed and the conventional methods. The results show that the flammable masses for selected scenarios are better estimated by the proposed method, being much lower than estimated by the conventional method, though larger than the CFD results. This paper presents an interim result of a project undertaken to improve QRA studies for VCE events.     

A methodology to predict shock overpressure decay in a tunnel produced by a premixed methane/air explosion

A methodology for estimating the blast wave overpressure decay in air produced by a gas explosion in a closed-ended tunnel is proposed based on numerical simulations. The influence of the tunnel wall roughness is taken into account in studying a methane/air mixture explosion and the subsequent propagation of the resulting shock wave in air. The pressure time-history is obtained at different axial locations in the tunnel outside the methane/air mixture. If the shock overpressure at two, or more locations, is known, the value at other locations can be determined according to a simple power law. The study demonstrates the accuracy of the proposed methodology to estimate the overpressure change with distance for shock waves in air produced by methane/air mixture explosions. The methodology is applied to experimental data in order to validate the approach.     

基于随机搜索优化XGBoost的瓦斯涌出量预测模型*

为防治瓦斯灾害,解决井下瓦斯涌出量在预测过程中因影响因素繁多带来的精度较低问题,提出1种基于套索(Lasso)回归与随机搜索优化极限梯度提升(XGBoost)的模型进行瓦斯涌出量预测。以沈阳某煤矿综采面瓦斯涌出量历史数据为例,综合考虑影响瓦斯涌出量的影响因素。首先利用Lasso回归提取对瓦斯涌出量有重要影响的特征数据,作为预测输入;采用随机搜索算法对XGBoost模型4种主要参数进行寻优,选取最优参建立预测模型获得预测指标并分析比较其他模型。研究结果表明:Lasso回归筛选出的影响因素结合随机搜索获得的最优参数组合优化XGBoost比其他模型预测精度更高,平均相对误差为1.53%,均方根误差为0.140 3 m3/min,希尔不等系数为0.013 2,研究结果可为现场瓦斯管理提供参考依据。     

A New Homogeneous Non-Equilibrium Model to Compute Vapor-Liquid Two-Phase Critical Pressure Ratios of Multicomponent Hydrocarbon Mixtures

The critical pressure ratio (η_c) is an essential parameter for computing the vapor-liquid two-phase critical pressure and mass flow rate of multicomponent hydrocarbon mixtures flowing through valves and leakage orifices. The Homogeneous Non-Equilibrium Diener-Schmidt (HNE-DS) model widely used to calculate η_c assumes that the fluid's volume linearly changes with the pressure (using the Clausius-Clapeyron equation), which is not suitable for multicomponent gas mixtures. In this paper, a new Homogeneous Non-Equilibrium (new-HNE) model is proposed to calculate η_c of gas mixtures. Firstly, a new critical flow compressibility factor (ω_c) is developed from its thermodynamic definition and the Peng-Robinson equation of state (EOS), overcoming the inherent limitations of the Clausius-Clapeyron equation. Then, η_c is correlated to the newly derived ω_c by fitting experimental data at various pressures and gas mass fractions of both single-component and multicomponent gas mixtures, yielding the new HNE-DS model. Results show that, for the water-steam and air-water two-phase flow, the average relative deviations (ARD) between the calculated critical pressure ratios and experimental values are equal to 2.8% and 4.93%, respectively, which represents a significant improvement in comparison with the original HNE-DS model. Moreover, this new model is extended to the applications of Liquefied natural gas (LNG)/liquefied petroleum gas (LPG) fluids, and will further contribute to the calculation of the leakage mass flow rate of fluid flowing through the orifices/valves.