采用遗传算法优化气体探测器布局策略研究

石油石化装置具有结构复杂且危险性高的特点,所加工物料多为易燃易爆有毒物质,且工艺单元之间集成度高,一旦发生泄漏若无法及时探测到则易形成气液积聚和火灾爆炸后果强化,装置拥塞度高使人员逃生困难。火气系统FGS作为安全关键系统,其中的气体探测网络如何快速可靠的实现对气体泄漏事件的探测显得尤为重要。已知探测时间,通过引入遗传算法利用其全局搜索的特点克服传统分支定界法的缺点,实现立体空间不同高度下设置探测网络达到场景全覆盖和缩短探测时间,同时求出探测时间附近的多组最优解,为探测器放置提供多种布置方案。通过与传统等间距探测器布置方案比较,从多种布置方案中选择更符合实际的最佳方案。通过海上浮式生产储油船的生产夹板气体探测案例,验证了所提方法的有效性。     

基于遗传算法的运行模式分布模型及排放测算

针对交通系统易于收集到的平均速度,以及排放模型计算所需的运行模式分布参数,建立基于平均速度的运行模式分布模型,并采用遗传算法对模型进行优化.对比所建立模型、MOVES模型中的行驶周期所获取数据与真实数据之间的排放结果差异,发现本模型有82.5%的区间平均排放率预测误差低于MOVES,本模型的最大误差为50.0%,而MOVES模型为304.2%.使用本模型评价了北京市限行前后污染物排放情况,发现限行后二环路高峰小时HC、CO、NO_x总体排放依次减少了9.58%、11.41%、0.49%.与真实值相比,预测值R²方高于0.700,预测误差大幅度低于MOVES模型预测误差,并实现对交通策略下路网排放的动态评价应用.     

风窗对井下通风系统的影响及其调节与定位优化

利用回路阻力平衡关系分析了井下风窗安装前后通风系统中各分支间风流流动关系,提出了某些分支安装风窗后,通风系统各分支的风量对此变化作出反应的分析模型,给出了度量风量和阻力反应敏感性强弱的方法;假定通风系统中某些分支同时安装风窗,在保留风窗的使用对通风系统所带来的正面作用,但要抑制其负面作用的条件下,基于尽可能使所有参与调节的分支的风阻增、减值最小化的原则,提出了指定分支实施调节的调阻调节法和优选分支实施调节的调阻调节法,并建立了相关最优调节模型及其求解方法。结果表明:两种模型可以在风窗安装之前进行解算,解算结果可预知风窗安装后通风系统各分支的风流的分布情况,并确定出相应的调控策略;优选分支调阻调节模型可以用于选择最优的风窗安装位置、数量及风窗阻力,后者可以用于确定风窗窗口面积。     

基于量子遗传算法的液压支架喷雾效率的参数优化设计

为了提高综采工作面液压支架喷雾的降尘效率,通过分析影响支架喷雾降尘效率的因素,以支架喷雾效率最高为优化目标,利用量子遗传算法对喷嘴到产尘点距离,雾化角度,喷雾压力,喷嘴个数,喷嘴直径多参数进行优化。结果表明:液压支架的采煤机移动喷雾降尘效率增加了1306%,液压支架上的移架/放煤喷雾降尘效率增加了1333%。此研究对于支架喷雾降尘系统的设计,参数的合理选取有一定的参考价值。     

可散式弹链拉断力变化规律研究

目的 确保弹箱内的弹链(抱弹状态)在库房存放一段时间后依然稳定供弹,描述可散式弹链拉断力在不同温度环境下的变化规律.方法 应用环境作用动力学理论,建立可散式弹链的拉断力变化微分方程,求解可散式弹链的拉断力变化微分方程,得到可散式弹链拉断力变化规律的微分方程通解,再通过高温加速试验数据,求得可散式弹链拉断力变化规律的微分...     

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。     

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。     

基于合作博弈-云化AHP的地铁隧道施工方案选优

为了研究多专家对多方案的比选排序规则及数据特点,提出一种基于合作博弈-云化AHP算法处理该问题,并用以地铁隧道施工方案选择。方案选择对于控制施工成本,保证施工安全尤为重要。使用云模型嵌入AHP构建云化AHP模型对专家提供的不确定信息进行处理,得到在考虑实际工程背景下的方案偏好排序。使用合作博弈模型得到多个方案排序的综合排序。根据方案权重最大原则确定最优方案。分析了3位专家对3种隧道施工方案的偏好排序,其结果表明:对于给定的工程背景,盾构法施工是最优的。与现有4种模型进行对比,说明了其能更好地处理数据的不确定性,综合专家决策群意见,且数据量和计算量较小。     

An integrated model for structure optimization and technology screening of urban wastewater systems

The conventional approach to wastewater system design and planning considers each component separately and does not provide the optimum performance of the entire system. However, the growing concern for environmental protection, economic efficiency, and sustainability of urban wastewater systems requires an integrated modeling of subsystems and a synthetic evaluation of multiple objectives. In this study, a multi-objective optimization model of an integrated urban wastewater system was developed. The model encompasses subsystems, such as a sewer system, stormwater management, municipal wastewater treatment, and a wastewater reclamation system. The non-dominated sorting genetic algorithm (NSGA-II) was used to generate a range of system design possibilities to optimize conflicting environmental and economic objectives. Information from a knowledge base, which included rules for generating treatment trains as well as the performance characteristics of commonly used water pollution control measures, was utilized. The trade-off relationships between the objectives, total water pollution loads to the environment, and life cycle costs (which consist of investment as well as operation and maintenance costs), can be illustrated using Pareto charts. The developed model can be used to assist decision makers in the preliminary planning of system structure. A benchmark city was constructed to illustrate the methods of multi-objective controls, highlight cost-effective water pollution control measures, and identify the main pressures on urban water environment.