基于模糊数学方法的门式起重机风险评估研究

本文采用模糊数学方法对门式起重机潜在风险因素（腐蚀、裂纹、强度及变形）导致的金属结构破坏进行风险评估。从主梁和支腿两个角度出发,建立了不同金属结构风险影响因素为子系统的多层次评判系统。该方法方便了门式起重机的综合安全风险评估,为提高其安全性能提供了方向。     

A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis

Layer of protection analysis (LOPA) is a widely used semi-quantitative risk assessment method. It provides a simplified and less precise method to assess the effectiveness of protection layers and the residual risk of an incident scenario. The outcome failure frequency and consequence of that residual risk are intended to be conservative by prudently selecting input data, given that design specification and component manufacturer's data are often overly optimistic. There are many influencing factors, including design deficiencies, lack of layer independence, availability, human factors, wear by testing and maintenance shortcomings, which are not quantified and are dependent on type of process and location. This makes the risk in LOPA usually overestimated. Therefore, to make decisions for a cost-effective system, different sources and types of uncertainty in the LOPA model need to be identified and quantified. In this study, a fuzzy logic and probabilistic hybrid approach was developed to determine the mean and to quantify the uncertainty of frequency of an initiating event and the probabilities of failure on demand (PFD) of independent protection layers (IPLs). It is based on the available data and expert judgment. The method was applied to a distillation system with a capacity to distill 40 tons of flammable n-hexane. The outcome risk of the new method has been proven to be more precise compared to results from the conventional LOPA approach.     

Building a global taxonomy of wildlife offenses

Most countries have many pieces of legislation that govern biodiversity, including a range of criminal, administrative, and civil law provisions that state how wildlife must be legally used, managed, and protected. However, related debates in conservation, such as about enforcement, often overlook the details within national legislation that define which specific acts are illegal, the conditions under which laws apply, and how they are sanctioned. Based on a review of 90 wildlife laws in 8 high-biodiversity countries with different legal systems, we developed a taxonomy that describes all types of wildlife offenses in those countries. The 511 offenses are organized into a hierarchical taxonomy that scholars and practitioners can use to help conduct legal analyses. This is significant amidst competing calls to strengthen, deregulate, and reform wildlife legislation, particularly in response to fears over zoonotic threats and large-scale biodiversity loss. It can be used to provide more nuance legal analyses and facilitate like-for-like comparisons across countries, informing processes to redraft conservation laws, review deregulation efforts, close loopholes, and harmonize legislation across jurisdictions. We applied the taxonomy in a comparison of sanctions in 8 countries for hunting a protected species. We found not only huge ranges in fines (US$0 to $200,000) and imprisonment terms (1.5 years to life imprisonment), but also fundamentally different approaches to designing sanctions for wildlife offenses. The taxonomy also illustrates how future legal taxonomies can be developed for other environmental issues (e.g., invasive species, protected areas).     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

矿井通风系统模糊可靠性研究

定义模糊可靠度为一个服从正态分布的模糊数，并以模糊数作为通风系统的各风路可靠度，进而提出了矿井通风系统的模糊可靠度计算方法。同时在置信水平上，确定矿井通风系统的普通可靠度置信区间。若＝１，则模糊可靠度计算转化为可靠度计算。     

非水相硝基苯在含水层中的迁移释放规律

通过室内模拟实验,探讨了在非水相硝基苯污染含水层的条件下,其在含水层中的迁移及释放规律。迁移规律表明,非水相硝基苯并非在含水层中直接进行垂向迁移,而是一方面在自身重力作用下向含水层下部迁移,另一方面在地下水流的作用下随地下水同向运移,整体表现为随地下水流的侧向运移,并最终迁移至含水层底部。释放规律表明,非水相硝基苯在含水层迁移的过程中会向地下水大量释放,释放出的硝基苯在水流的作用下随地下水同向运移,污染源及迁移至含水层底部的非水相硝基苯均存在再次释放。     

岷江上游生态脆弱性评价

岷江上游流域是我国典型的生态脆弱区之一,由于地质变化频繁、高差显著、气候干旱,加上人为活动影响,生态脆弱性的表现十分明显。通过对其生态环境脆弱性因素及成因机制的分析,构建了由土地生产力、地表起伏度、干燥度指数、土壤侵蚀强度、草场退化荒漠化率、物种消失率等14个指标组成的岷江上游生态脆弱性的评价指标体系;根据本地区生态环境现状、全国和四川省情况及奋斗目标,建立了Ⅰ到Ⅲ级的评价标准体系;利用模糊数学聚类方法对评价指标进行分析计算,得出了岷江上游生态环境为第Ⅲ级,即生态环境非常脆弱的结论。评价结果符合岷江上游地区的生态环境状况。     

流沙湾海域水质的综合评价与分析

以流沙湾2008年4个航次水质监测数据资料为依据,选择具有代表性的9种监测指标,分别采用模糊综合评价法和模糊贴近度法进行海水水质评价比较与分析,得出流沙湾海域水质评价等级。研究结果表明,流沙湾海域冬夏季节的水环境质量差于春秋两季,其中夏季水质最差。将整年水质监测指标平均值进行综合评价,结果表明,流沙湾各站位均符合二类水质标准。以模糊贴近度法作为主要评价方法、以模糊综合评价法作为辅助评价方法进行综合评价,更有利于流沙湾海域水质的整体分析。研究结果可作为流沙湾海域水产养殖规划和整治的参考依据,为将来合理开发利用流沙湾海域提供科学依据。     

模糊综合评价法在水质变化和水体管理中的应用

应用模糊数学中的模糊综合评价法，对徐州市的地面水体的水质变化趋势进行了探讨，结果表明模糊综合评价法能及时发现水质恶化现象，对水体的管理具有指导作用．