城市供水管网抗震可靠性分析与设计优化

考虑管线震害发生的随机性,利用管线在地震作用下反应的二阶矩特征信息,采用带渗漏管网抗震可靠性分析的一次二阶矩方法,以华中地区某市主干供水管网为例,进行了基本烈度条件下的可靠度分析,结果表明,在低、中烈度条件下,供水管网中各节点均处于可靠状态;在高烈度条件下,节点主要处于可靠状态,局部处于严重不可靠、甚至出现断水状态。进一步地,基于微粒群优化算法,以管网年费用折算值为优化目标、管网拓扑结构与管径为优化参数、管网节点可靠度为约束条件,建立了新的供水管网抗震设计优化模型,并将供水管网所在区域的道路信息引入管网优化设计中,提出了管网拓扑的自动生成策略,从而形成了城市供水管网系统智能化抗震设计方法。数值算例表明,基于可靠度的抗震优化设计方法为风险-投资之间的合理均衡提供了理论基础,决策者可以据此选择所期望的最佳方案。     

Opportunities for redistributed manufacturing and digital intelligence as enablers of a circular economy

The linear production of consumer goods is characterised by mass manufacture by multinational enterprises and globally dispersed supply chains. The current centralised model has created a distance between the manufacturer and end user, limiting the opportunity for intelligent circular approaches for production and consumption. Through a mixed method approach, opportunities of circularity are explored for the consumer goods sector. The study presents four lenses to analyse three enterprises through a multi-case study approach to explore the potential of digital intelligence and redistributed manufacturing (RDM) as enablers of circular business models. In addition, the study examines whether Discrete Event Simulation can be used to evaluate the circular scenarios identified through quantifying flows of material that determine traditional economic value (cost/tonne). The mixed method approach demonstrates that, a qualitative systemic analysis can reveal opportunities for circularity, gained through implementing ‘digital intelligence’ and distributed models of production and consumption. Furthermore, simulations can provide a quantified evaluation on the effects of introducing circular activities across a supply chain.     

混凝土结构使役环境中温度智能模拟LabVIEW软件设计

本项目结合湖北省科技计划自然科学基金项目“混凝土结构使役环境的智能模拟”,在WTST-150试验机硬件平台基础上,以LabVIEW8．5为开发平台,设计了一套混凝土结构使役环境中温度智能模拟LabVIEW软件,主要包括能初始化程序、温度采集与显示子程序、温度PID控制子程序、温度存储和查询子程序。经投入实验验证,该软件能实现实时曲线显示、温度控制、试验状态和保存试验数据、数据查询、手动加热、分配温度通道、PID控制参数整定、制冷、读入温度曲线等功能。具有实时在线循环检测、采集数据、响应快速等优点,并具有良好的故障诊断能力和可维护性以及超强的纠错能力。本软件完善了WTST-150试验机的温度控制系统,使其更加符合实际工况。     

A new intelligent electronic nose system for measuring and analysing livestock and poultry farm odours

This paper introduces a new portable intelligent electronic nose system developed especially for measuring and analysing livestock and poultry farm odours. It can be used in both laboratory and field. The sensor array of the proposed electronic nose consists of 14 gas sensors, a humidity sensor, and a temperature sensor. The gas sensors were especially selected for the main compounds from the livestock farm odours. An expert system called “Odour Expert” was developed to support researchers’ and farmers’ decision making on odour control strategies for livestock and poultry operations. “Odour Expert” utilises several advanced artificial intelligence technologies tailored to livestock and poultry farm odours. It can provide more advanced odour analysis than existing commercially available products. In addition, a rank of odour generation factors is provided, which refines the focus of odour control research. Field experiments were conducted downwind from the barns on 14 livestock and poultry farms. Experimental results show that the predicted odour strengths by the electronic nose yield higher consistency in comparison to the perceived odour intensity by human panel. The “Odour Expert” is a useful tool for assisting farmers’ odour management practises. Supported by Ontario Pork, Natural Sciences and Engineering Research Council (NSERC), and Ontario Ministry of Agriculture and Food (OMAF) of Canada.     

基于RBF和PSO的双喉道气动矢量喷管优化设计

本文探索了一种能多变量综合优化的方法,即对喷管进行参数化设计后,用均匀试验设计(UED)将试验样本均匀散布在设计区间内,求出各性能参数后,利用径向基神经网络(RBF)对试验样本进行拟合,再用粒子群算法(PSO)对训练好的神经网络进行寻优,找出了更好的双喉道气动矢量喷管设计参数组合。数值模拟结果显示,优化后的双喉道气动矢量喷管的矢量角有了明显提高。试验表明这种优化方法具有很好的优化能力,可以用来对喷管几何外形进行参数优化。      

融合时空特征的PCA-PSO-SVM臭氧(O3)预测方法研究

针对目前臭氧预测方法未能考虑臭氧污染的区域性和在时间周期内的强自相关性的问题,提出一种融合时空特征的PCA-PSO-SVM臭氧组合预测模型.利用小波分析和系统聚类提取臭氧时间序列波动特征和站点空间分布相似性特征,并通过主成分分析和粒子群算法优化的支持向量机组合模型（PCA-PSO-SVM）对臭氧日最大8h平均浓度进行预测,以2016~2018年杭州市大气污染物观测数据和气象数据进行实验验证.结果表明：融合时空特征的PCA-PSO-SVM模型预测精度有较大提升,与未融合时空特征的PCA-PSO-SVM模型相比,精度提升19%.气象因素中温度对臭氧预测效果影响最大,在气象预报数据存在一定误差的情况下,提出的模型仍得到较高精度的预测效果,具备较好的鲁棒性.     

基于pso-SVM的废水厌氧处理过程软测量模型

由于厌氧消化过程的复杂性和厌氧菌的敏感性,保持厌氧消化体系的稳定和高效性是比较困难的.本文在实验室采用IC反应器构建了一套厌氧废水处理系统处理人工合成废水,基于支持向量机(SVM)提出了一种预测废水厌氧处理系统出水挥发性脂肪酸(VFA)浓度和COD去除率的软测量模型.为了提高模型的精确性和鲁棒性,加入pso算法(粒子群算法)优化SVM模型,并引入了分类策略对元数据集进行有效分类.仿真结果表明,基于pso-SVM模型的软测量模型对厌氧废水处理系统出水VFA浓度和COD去除率具有较好的预测能力,模型预测系统COD去除率及出水总VFA浓度测试样本数据相关系数分别为65.86%、85.25%;加入分类策略后,元数据集分成两类,模型预测系统COD去除率测试样本数据相关系数分别为92.34%、83.41%;模型预测系统出水总VFA浓度测试样本数据相关系数分别为99.14%、99.59%,系统预测精度明显提高.引入分类策略对元数据集进行有效分类,基于pso-SVM的软测量模型可为监控、优化和理解厌氧消化过程提供指导.     

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO₄ and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction.     

管棚预支护条件下隧道开挖面稳定的可靠度分析

为考虑土体参数的变异性对隧道开挖面稳定性的影响,基于可靠度理论,采用三维弹塑性有限元数值模拟方法,计算管棚预支护条件下隧道开挖面极限支护力;以不同工况下地层参数及其极限支护压力比作为样本,待BP神经网络训练完毕后,即可预测大量给定地层参数工况下的开挖面极限支护压力比,对其进行统计,得到概率分布特征;在理论分析的基础上,结合工程实际,建立了管棚预支护条件下隧道开挖面稳定的极限状态方程,运用粒子群优化算法,对其进行可靠度分析。研究结果表明:采用管棚预支护的隧道,开挖面支护压力存在极限值,达到该值后,增加较小幅度的支护力,就能较大程度地提高隧道开挖面稳定的可靠度。     

基于数据挖掘法的矿井瓦斯联动监测

为解决传统矿井监测技术存在的监测效果差、数据利用率低、预警能力不足等问题,提出采用多点及多因素融合的联动监测方法。采用计算机数据挖掘、多级数据融合处理技术建立基于实际预警、规则评判、历史数据检测等方法的矿井故障分析及瓦斯浓度预警数据处理模型。该技术在临汾市尧都区5个煤矿的安全生产监控中试验应用,结果表明联动分析法有助于分析矿井故障原因、提高瓦斯浓度预警能力,且数据挖掘及多级数据融合技术的应用有助于规范样本,提高分析预警规则的稳定性、适应性。