基于泛平均运算的矿井瓦斯浓度组合预测模型

为有效分析煤矿瓦斯监测数据以实现准确、可靠的瓦斯浓度预测,基于不等权泛平均运算模型,研究瓦斯浓度时间序列组合预测的方法,提出一种新的矿井瓦斯浓度组合预测模型,并证明最优组合预测模型是其特例。采用自回归(AR)模型和径向基函数(RBF)神经网络预测模型作为组合预测模型的单项预测模型;以遗传算法和最小二乘法确定新组合预测模型的参数,实现瓦斯浓度预测单项模型的最优组合。试验分析表明:新模型在平方和误差、平均绝对误差、均方误差、平均绝对百分比误差、均方百分比误差等评价指标上,均取得比自回归模型、径向基函数神经网络模型和最优组合预测模型更低的误差。     

多类支持向量机在煤矿安全评价中的应用研究

针对矿井子系统诸多、环境复杂、影响因素多变和在现实条件下难以获得大量煤矿样本的情况,提出将对非线性、小样本问题有较高处理能力的支持向量机理论引入到机制评价中,并在归纳了支持向量分类机从一对多到一对一再到决策树模式的多渠道多层次分类原理基础上,建立了基于多分类支持向量机原理的煤矿安全多层次评价模型,同时通过提取影响煤矿安全因素的特征参数,引人类权重因子和样本权重因子,较好地解决了训练样本类别数量不平衡、数据干扰导致的错分问题,实现了对煤矿安全较高准确率和较高效率的评价.     

Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method

With the development of natural gas transportation systems, major accidents can result from internal gas leaks in pipelines that transport high-pressure gases. Leaks in pipelines that carry natural gas result in enormous financial loss to the industry and affect public health. Hence, leak detection and localization is a major concern for researchers studying pipeline systems. To ensure the safety and improve the efficiency of pipeline emergency repair, a high-pressure and long-distance circular pipe leakage simulation platform is designed and established by similarity analysis with a field transmission pipeline, and an integrated leakage detection and localization model for gas pipelines is proposed. Given that the spread velocity of acoustic waves in pipelines is related to the properties of the medium, such as pressure, density, specific heat, and so on, this paper proposes a modified acoustic velocity and location formula. An improved wavelet double-threshold de-noising optimization method is also proposed to address the original acoustic wave signal collected by the test platform. Finally, the least squares support vector machine (LS-SVM) method is applied to determine the leakage degree and operation condition. Experimental results show that the integrated model can enhance the accuracy and precision of pipeline leakage detection and localization.     

Fuzzy AHP approach to selection problems in process engineering involving quantitative and qualitative aspects

Selection problems are common in process engineering. In most cases, it is necessary to rank alternatives based on multiple criteria (e.g., cost, safety, environmental impact), which are often conflicting. In addition, some criteria may be fundamentally difficult to quantify due to data scarcity, in which case subjective assessments need to be used as a proxy. Decision analysis tools such as the analytic hierarchy process (AHP) are useful to ensure decision-making is done rationally. In this work, we propose a fuzzy AHP variant, wherein pairwise comparison of decision elements by domain experts is expressed with triangular fuzzy numbers. This approach allows the degree of confidence of the expert to be quantified explicitly; it also allows inconsistencies in judgment to be reconciled within the bounds of the fuzzy numbers to generate reasonable values for the weighting factors. We demonstrate the methodology on three case studies, involving the comparison of different types of chlor-alkali electrolytic cells, CO₂ capture techniques in cement plants and wastewater treatment options for municipal wastewater.     

Fuzzy risk modeling of process operations in the oil and gas refineries

Operating several assets has resulted in more complexity and so occurrence of some major accidents in the refining industries. The process operations risk factors including failure frequency and the consequence components like employees' safety and environment impacts, operation downtime, direct and indirect cost of operations and maintenance, and mean time to repair should be considered in the analysis of these major accidents in any refinery. Considering all of these factors, the risk based maintenance (RBM) as a proper risk assessment methodology minimizes the risk resulting from asset failures. But, one of the main engineering problems in risk modeling of the complex industries like refineries is uncertainty due to the lack of information. This paper proposes a model for the risk of the process operations in the oil and gas refineries. The fuzzy logic system (FLS) was proposed for risk modeling. The merit of using fuzzy model is to overcome the uncertainty of the RBM components. This approach also can be accounted as a benchmark for future failures. A unified risk number would be obtained to show how the criticality of units is. The case study of a gas plant in an oil refinery is performed to illustrate the application of the proposed model and a comparison between the results of both traditional RBM and fuzzy method is made.For the case study, 26 asset failures were identified. The fuzzy risk results show that 3 failures have semi-critical level and other 23 failures are non-critical. In both traditional and fuzzy RBM methods, some condenser failures had the highest risk number and some pumps were prioritized to have the lowest risk level. The unit with unified risk number less than 40 is in the non-critical conditions. Proposed methodology is also applicable to other industries dealing with process operations risks.     

随机桁架结构可靠性分析的完全概率方法

提出了一种求解随机参数桁架结构在随机荷载作用下反应的概率密度函数和结构精确可靠度的方法.通过对随机参数桁架结构在随机荷载作用下的有限元分析方法的研究,考虑了结构的物理参数,构件的几何尺寸和作用荷载幅值等的随机性.应用随机向量函数的概率分布函数表达式,通过确定积分区间、变量替换、交换积分顺序等一系列数学上的处理,获得所求结构反应的概率密度函数.由干涉理论得到了结构位移和应力的可靠度.通过算例的结果与Monte-Carlo模拟法结果比较,表明该方法具有较高的精度及良好的实用性.     

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。     

北京市产业结构调整及技术进步与节能的动态关系

为了建立环境友好的能源结构与健全节能的保障机制,运用指数分解法对北京市1996—2010年的节能量进行了计算与静态分解,在此基础上以单位GDP能耗作为北京市能源消费水平和节能降耗状况的主要指标,通过建立产业结构调整-技术进步-单位GDP能耗的VAR(vector autoregression,向量自回归)模型,以及脉冲响应和方差分解,论述三者间的动态关系;利用北京市2011─2013年实际单位GDP能耗对VAR模型进行校验,模型计算误差在5%以下. 结果表明:北京市在“九五”期间产业结构调整节能贡献大,占总节能量的55.60%,其主要依托第二、三产业间的结构调整;而在“十五”和“十一五”期间,技术进步节能量分别占同期总节能量的81.35%、85.41%. 单位GDP能耗、产业结构节能、技术进步节能对单位GDP能耗的方差贡献率分别为13%、39%和48%. 产业结构调整和技术进步对单位GDP能耗均产生周期性的影响,每个周期为5.0 a,影响逐渐减弱,2个周期后趋于零. 其中,产业结构调整后的前2.5 a对单位GDP能耗的影响较显著,其中尤以第2年最为明显;技术进步后的3.0～5.0 a对单位GDP能耗的影响显著,尤以第3年最为明显,具有2.0 a的滞后期. 因此,应结合产业结构调整和技术进步对单位GDP能耗影响的特征,对其节能效果做出科学评价,并应用于实际政策制订.      

2种基于模式识别的环状燃气管网泄漏检测方法

为了将模式识别技术应用于环状燃气管网泄漏检测并找到合适的特征提取方法,以天津城建大学实验室环状燃气管网泄漏为例,将实验的28种工况作为测试样本,与之对应的模拟工况作为训练样本,采用提取压力图像特征向量法和节点压力矩阵法分别进行环状燃气管网的泄漏检测,采用支持向量机分类器将2种方法获得的特征向量进行训练与分类检验,进而将其分类准确率进行对比分析。研究结果表明:该2种方法均可用于环状燃气管网泄漏检测,提取压力图像特征向量法因有效地降低了特征向量的维度和数据波动的干扰,其结果更优。结合SCADA和GIS系统,可将该法应用于实际水、气、油管网泄漏检测和定位,有助于降低成本,提高检测效果。     

地下洞室群现场作业风险分析与向量模型

为了厘清地下洞室群施工现场作业风险因素间的关联效应,利用AMOS软件构建施工风险影响因素结构方程模型,对已建模型进行验证性因子分析,利用计算得出的M.I值对已构建的模型进行修正。根据所得模型计算出的路径系数和方差,对地下洞室群施工风险因素关联效应进行分析。结果表明:规章制度建设及执行、围岩稳定性、进场设备优良率、工人技术等级、工人逆反心理以及消极作业分别为各风险层次中影响最大的风险因素,且各风险层次间的关联效应表现出耦合关系;现场作业风险因素可分为现场主体因素、现场客体因素和现场控制因素3大类,它们之间成向量关系,以此为基础建立向量模型,可清晰直观地展示3大因素之间的主次关系。