建筑施工事故非线性灰色伯努利模型预测

为提高建筑施工事故灰色预测模型精度,在传统GM(1,1)模型基础上,建立非线性灰色伯努利模型(NGBM),并采用粒子群优化(PSO)算法对参数进行优选。以2001—2011年全国建筑事故死亡人数统计数据为基础,运用该模型对2012—2013年的相应人数进行预测,并与GM(1,1)模型和灰色Verhulst模型的结果相对比。结果表明,NGBM拟合精度最好,平均相对误差仅为2.65%,验证了模型的可行性和准确性。     

基于PSO优化BP神经网络的钻井动态风险评估方法

传统钻井动态风险评估严重依赖于专家主观判断、结果大多是定性或半定量,无法满足深井复杂地层钻井安全需求。针对该问题,研究建立了基于PSO优化BP神经网络的钻井动态风险评估方法。通过对录井资料的监测分析,实时判断井下风险发生的类型并定量计算风险发生概率,可以在风险发生的早期给出预警信息,及时指导风险调控措施的开展。海上BD气田的实例分析表明,基于构建的动态风险评估模型得到的风险预测结果与实际风险发生情况相符合,说明建立的模型是合理可行的。该模型对于钻井作业过程中动态风险评估具有一定的参考价值。     

基于PSO算法的BP神经网络对水体叶绿素a的预测

BP神经网络(Back Propagation Network)在水体富营养化评价及预测中已广泛应用,但传统BP算法的收敛速度慢并易陷入局部最优. 提出了一种基于微粒群(PSO)算法的BP神经网络模型,利用PSO对神经网络的权值进行修正,优化神经网络结构及算法全局收敛性. 选择最能代表明湖水质状况的5号采样点作为研究对象,把2009年4月—2010年3月的月样本插值为周样本,对明湖ρ(Chla)的短期变化趋势进行了预测,并用6号采样点数据来验证网络的泛化能力. 比较分析基于PSO算法的新模型与传统BP算法模型的预测精度表明,新模型有效克服了传统算法的缺点,提高了网络的预测能力和学习能力.      

基于改进PSO-BPNN的输油管道内腐蚀速率研究

为解决输油管道易腐蚀,且腐蚀程度难以测量的问题,提出使用改进的粒子群算法（PSO）优化误差反向传播神经网络（BPNN）对输油管道内腐蚀速率进行预测。改进的PSO算法提升了自身搜索到全局最优的能力,可为BPNN提供最优初始权值和阈值,从而有效避免BPNN易陷入局部最优的问题发生。以某条输油管线为例,分别运用标准的BPNN模型、PSO-BPNN以及改进的PSO-BPNN对该管线内腐蚀速率进行预测。结果表明:基于改进的PSO-BPNN的预测结果平均相对误差为5.57%,预测精度较BPNN和PSO-BPNN有明显提升。使用改进的PSO-BPNN预测输油管道的腐蚀速率可为管道的检测维修提供可靠的理论和技术支撑。     

Optimal sizing and siting of renewable energy resources in distribution systems considering time varying electrical/heating/cooling loads using PSO algorithm

Distributed Generation (DG) sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to their environmental friendliness and also the limited sources of fossil fuels. In general, the optimal location and size of DG units have profoundly impacted on the system losses in a distribution network. In the present article, the Particle Swarm Optimization (PSO) algorithm is employed to find the optimal location and size of DG units in a distribution system. The optimal location and size of DG units are determined on the basis of a multi-objective strategy as follows: (i) the minimization of network power losses, (ii) the minimization of the total costs of Distributed Energy Resources (DERs), (iii) the improvement of voltage stability, and (iv) the minimization of greenhouse gas emissions. The related distribution system was assumed to be composed of the fuel cells, wind turbines, photovoltaic arrays, and battery storages. The electrical, cooling, and heating loads were also considered in this article. The heating and cooling requirements of the system consist of time varying water heating load, space heating load, and space cooling load. In this study, the waste and fuel cell were used to produce the required heating and cooling loads in the distribution system. In addition, the absorption chiller was used to supply the required space cooling loads. A detailed performance analysis was carried out on 13 bus radial distribution system to demonstrate the effectiveness of the proposed methodology.     

Nonlinear molecular based modeling of the flash point for application in inherently safer design

New chemical process design strategies utilizing computer-aided molecular design (CAMD) can provide significant improvements in process safety by designing chemicals with required target properties and the substitution of safer chemicals. An important aspect of this methodology concerns the prediction of properties given the molecular structure. This study utilizes one such emerging method for prediction of a hazardous property, flash point (FP), which is in the center of attention in safety studies. Using such a reliable data set comprising 1651 organic and inorganic chemicals, from 79 diverse material classes, and robust dynamic binary particle swarm optimization for the feature selection step resulted in the most efficient molecular features of the FP investigations. Apart from the simple yet precise five-parameter multivariate model, the FP nonlinear behavior was thoroughly investigated by a novel hybrid of particle swarm optimization and support vector regression. Besides, 195 missing experimental FPs of the DIPPR data set are predicted via the presented procedure.     

Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network optimization

The optimization of a water distribution network (WDN) is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm (EDA) using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence. Two water distribution network benchmark examples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum (6.081 M$) for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum (1.921M€) than the current literature reported best minimum (1.923M€). The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.     

基于粒子群算法的燃气管线泄漏疏散能力分析

为研究燃气管线泄漏事故人员疏散能力,统计分析近年国内燃气泄漏典型事故,得出第三方违章作业施工为燃气泄漏事故比例最高的直接原因及燃气泄漏事故伤亡人数发展趋势,在此基础上,引入粒子群PSO算法,以某施工引起的燃气管线泄漏事故为实例,研究领导者的领导能力和应急处置能力对员工之间信息交流及人员应急疏散能力的影响。分析实例显示现场领导者学习因子偏低,指挥能力差,直接影响事故现场的应急疏散自救行为,甚至改变疏散路径。并进一步分析模拟得出,学习因子提高后,现场领导者和员工的信息更新与交换效率大幅提升,应急疏散能力随之提高,进而提出实用性对策措施和建议,为提升燃气管线泄漏事故应急疏散能力提供技术参考。     

故障树法和改进PSO-PNN网络的电梯故障诊断模型

针对电梯故障问题,提出一种将故障树分析法、改进的粒子群优化算法和概率神经网络相结合的方法用于电梯的故障诊断。以电梯的安全回路系统为例,用故障树法对回路进行分析,获得训练样本与故障类型;使用粒子群算法对概率神经网络的平滑因子进行优化,在优化过程中,针对粒子群算法存在易陷入局部最优的缺陷,提出对惯性权重的改进策略;采用相对误差对诊断效果做出评估,并与传统的概率神经网络和基本粒子群算法优化的概率神经网络在各种故障类型输出和最大相对误差等方面进行比较,结果表明:该模型能够有效诊断电梯故障。     

基于粗糙集——粒子群神经网络的建设项目安全预测研究

回顾施工项目安全管理和安全管理研究现状,建立建设项目安全管理指标体系。利用人工神经网络非线性函数逼近能力,对项目风险因素程度预测。针对该网络当数据量大时,其结构复杂、收敛慢,易陷入局部最优的缺点,引入粗糙集对影响建设项目安全目标的不确定性因素进行约简,找出最小不确定性风险因素集,大大简化网络输入信息的表达空间维数。并结合粒子群算法收敛速度快、全局最优的寻优能力强的优点,建立基于粗糙集——粒子群神经网络的建设项目安全预测系统。通过实例验证该系统的科学性和有效性。