Prediction of BLEVE mechanical energy by implementation of artificial neural network

In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input parameters (plus vessel volume), and the BLEVE blast energy has been estimated as output data by the ANN model. A Bayesian Regularization algorithm was chosen as the three-layer backpropagation training algorithm. Based on the neurons optimization process, the number of neurons at the hidden layer was five in the case of propane and four in the case of butane. The transfer function applied in this layer was a sigmoid, because it had an easy and straightforward differentiation for using in the backpropagation algorithm. For the output layer, the number of neurons had to be one in both cases, and the transfer function was purelin (linear). The model performance has been compared with experimental values, proving that the mechanical energy of a BLEVE explosion can be adequately predicted with the Artificial Neural Network approach.     

On the application of the window of opportunity and complex network to risk analysis of process plants operations during a pandemic

To quantify the pandemic specific impact with respect to the risk related to the chemical industry, a novel risk analysis method is proposed. The method includes three parts. Firstly, the two types of “window of opportunity” (WO) theory is proposed to divide an accident life cycle into two parts. Then, a qualitative risk analysis is conducted based on WO theory to determine possible risk factors, evolution paths and consequences. The third part is a quantitative risk analysis based on a complex network model, integrating two types of WO. The Fuzzy set theory is introduced to calculate the failure probabilities of risk factors and the concept of risk entropy is used to represent the uncertainty. Then the Dijkstra algorithm is used to calculate the shortest path and the corresponding probability of the accident. The proposed method is applied to the SCR denitrition liquid ammonia storage and transportation system. The results show that it is a comprehensive method of quantitative risk analysis and it is applicable to risk analysis during the pandemic.     

Systematic incorporation of inherent safety in hazardous chemicals supply chain optimization

Increasing globalization has made many chemical supply chains large, interdependent and complex. Process incidents often affect the reliability of a supply chain and can cause large disruptions at different segments of the industry. We propose an optimization-based framework that systematically takes into account the trade-offs between process safety and supply chain economics for decision-making. We quantify the hazard at various supply chain echelons in the form of a safety index that takes both fire and toxic hazards into account. A mixed-integer nonlinear programming (MINLP)-based model is developed to either maximize profit for specified hazard limits, or to minimize hazard in a supply chain with multiple production plants, technological options, warehouses and distribution nodes. The MINLP model is used to generate trade-off optimal solutions for various toxic and fire hazard limits. The framework is demonstrated by applying it to an end-to-end ammonia supply chain case study which resulted in several non-intuitive observations regarding hazardous supply chain design and optimization.     

应急状态下矿井瓦斯爆炸致灾因子传播快速分类器研究*

为了在矿井瓦斯爆炸灾变发生后,快速确定瓦斯爆炸冲击波的压力、温度、有毒有害气体等致灾因子在井巷网络中的传播情况。利用CFD数值模拟或爆炸实验获得瓦斯爆炸冲击波的压力、温度、有毒有害气体等致灾因子传播大数据,将影响瓦斯爆炸传播的因素以及观测点等参数作为人工神经网络的输入节点,压力、温度等致灾因子作为输出节点,建立瓦斯爆炸致灾因子传播快速预测机器学习模型,解决CFD数值模拟的建模、计算及数据分析处理等过程耗时大、不适应灾变应急的快速响应等问题。研究结果表明:在给定爆炸位置和爆炸当量的均直巷道,获得任一点的爆炸冲击波压力、温度以及有毒有害气体所需时间是瞬时的,人工神经网络平均训练误差为6.92 %,有训练样本的验证误差为5.24 %,无训练样本的验证误差为6.88 %。     

西南地区高尔夫球场人工湖水体富营养化状况及其季节变化——以成都麓山高尔夫球场人工湖为例

为评价西南地区高尔夫球场人工湖的营养状态,并探讨影响球场湖泊富营养化的原因,2010年1月至12月,以成都麓山高尔夫球场为例,对球场的4个球道人工湖(12号球道、13号球道、14号球道和16号球道)水体的水体理化性质进行监测。结果显示:人工湖的富营养化程度呈季节性变化,其在试验期内综合营养状态已达到轻度富营养的状态。水体营养盐主要来自于球场草坪的施肥,氮、磷等营养物质随降水输入人工湖,从而引起的湖泊富营养化,尤其体现在多雨的夏季。     

辽宁省干线公路网络通达性研究

以辽宁省干线公路网络和14个地市、21个主要县市为研究对象,以最短距离原则为基础,选取距离、时间和通达性系数来衡量城市通达性水平的高低;以行车速度为指标,得到不同速度下通达性水平的空间格局;在100km/h的行车速度下,计算一个城市在1h、2h、3h、5h内连接的城市数目,并对通达性水平的特点和空间格局进行分析。结果显示,35个城市的通达性水平呈现出＂同心圈＂结构,＂核心—外围＂模式明显。以沈阳和鞍山为核心,由核心向外围通达性水平逐步降低,且随着行车速度的提高,通达性水平呈现出明显的上升趋势。由于通达水平的提高,在未来的几年里辽宁省将逐渐形成5个交通圈,同时实现＂一个工作日,全省通达＂的目标。     

某人工湖成库初期水环境特征研究

通过对某新建人工湖水温、DO、SD、pH、TN、TP、CODMn、Chla、藻类和水动力条件10项环境因素的特征、趋势分析,研究人工湖成库初期水环境特征。实验结果表明,成库初期,TN、TP等营养盐处于累积高峰期,通过计算N/P比和相关性分析,磷为藻类生长时期的限制性因子;人工湖基本处于准静止状态(流速小于0.1 m/s),为藻类生长提供有利条件;人工湖藻类种类和密度随时间而变动,出现高峰值,在调查阶段主要藻种为蓝、绿藻;叶绿素a含量一直处于较高水平,并分别与TP、SD、pH、DO之间存在显著的相关性。     

省域安全生产应急救援指挥平台框架体系研究

分析省级安全生产应急救援指挥的业务特点,认为它是一个平时模拟演练和事故现场应急指挥并重、平战结合的一体化过程,是一个广域多部门实时动态的联动过程;笔者提出安全生产应急救援的框架体系,并以网络为基础,以信息技术为手段,以智能设备为前端而构建一个实时直观的应急指挥有机体,包括基础支持系统、综合应用系统、数据库系统、移动指挥平台、前端展示系统,以及法律法规等体系、安全保障体系等,同时重点分析其中的网络结构、综合应用系统,以及与其他单位系统的接口。该框架体系通过广东省安全生产监督管理部门的验证,在实践中是可行的。     

基于BP神经网络的非煤地下矿山安全评价模型

针对非煤地下矿山安全评价的复杂性和非线性特性,BP神经网络对非线性动态系统较强的适应性,提出并建立了一种基于BP神经网络的非煤地下矿山安全评价模型。为提高该模型的可靠性,结合非煤地下矿山生产工艺特点,提出了一套便于统计和赋值的安全评价指标体系;确定了BP神经网络结构和评价结果表征方法;为提高BP神经网络算法的收敛速度和稳定性,对标准BP算法进行有效改进;通过实例运算验证了该模型的可行性。基于BP神经网络的安全评价模型为评价非煤地下矿山安全管理现状及水平提供了可操作的方法,为矿山有关部门提供了科学安全管理的依据。     

基于无线传感器网络的市政下水道可燃气体监测系统设计及应用

为防止下水道可燃气体积聚引发爆炸事故,利用无线传感器网络技术设计了市政下水道可燃气体监测系统.实测结果表明:气体传感器监测数据相对误差在2.0%以内,无线数据收发设备的年发送成功率达到98.0%,满足工程运用需求,系统具有较高的可靠性和稳定性.结合无线网络覆盖范围广、实时在线的特点,该系统实现了对不同区域下水道内可燃气体浓度的远程监测,为下水道的安全预警提供了有效依据.