恒压变频供水系统的Matlab仿真模型研究

恒压变频供水系统是一个复杂的非线性控制系统,用单纯数学分析的方法研究较为复杂且比较抽象.为了研究恒压变频供水系统的运行规律,改善系统运行状况,在分析了供水系统控制原理的基础上,借助于Matlab/Simulink搭建了基于施耐德ATV61变频器的恒压供水系统模型.利用Matlab提供的S函数模块模拟系统中的非线性环节....     

基于免疫粒子群算法的水轮机机组供水系统节能控制方法

传统供水系统节能控制方法忽略了对水轮机状态的监控,导致在降低系统能耗的同时,供水过程出现机组运行不稳定问题,为此,设计基于免疫粒子群算法的水轮机机组供水系统节能控制方法。利用T-S模糊模型构建供水系统数学模型。采用模糊神经网络结构作为新型节能控制的设计原理,设计新的节能控制器。通过免疫粒子群算法实现供水系统的整体控制,...     

基于参数自整定模糊PID的挤压式供水系统

研究挤压式供水系统优化控制问题,系统存在控制多变的参数.为保证整个系统稳定运行,通过分析系统管网压力近似数学模型可知,采用常规PID去控制挤压式供水系统,难以保证系统适应控制系统的参数变化,压力始终有较大波动,稳定性差.为解决上述问题,提出了将模糊控制与PID控制相结合的方法,设计了一种参数自整定模糊PID控制器去控制挤压式供水系统,弥补了系统稳定性差的问题.并在MATLAB仿真软件中进行了仿真验证,为供水系统稳定性控制提供了依据.     

循环水系统的变频调速

1 循环水系统与供水系统的比较 人们通常的概念是：水泵工作过程中消耗的功率与转速的立方成正比。这是因为，水泵的主要用途是供水，而对于一般供水系统来说，上述结论无疑是正确的。然而，水泵的用途是多方面的，在非供水系统中，上述结论却未必是正确的。 1.1 供水系统的特点 供水系统的基本模型如图1所示。 图1 供水系统模型图 (1) 供水特点 在供水系统中，用户所用的水是被消耗掉的。它并不回到水泵的进水口，对拖动系统毫无反馈作用。 (2) 水泵的作用 在供水系统中，水泵的作用是提高水的扬程。 (3) 水路特点 实际需要的扬程较…     

楼宇恒压供水系统设计

以生活区的恒压供水系统为例,介绍了应用PLC实现变频恒压供水系统的需求分析和实现过程.系统通过压力传感器、PLC、变频器等设备完成供水系统自动恒压控制.详细介绍了系统的工作原理、PID算法、硬件和软件设计以及PLC功能图.     

基于改进DE算法的并联泵组能效最优控制策略研究

为改变传统给水系统在节能降耗方面的不足,结合供水系统的典型特点,建立了以节能为目标的水泵并联运行优化调度模型;采用差分进化(DE)算法对模型进行求解,在确保供水系统服务用户质量的前提下,优化调度泵站开启水泵的台数和调速率,使系统整体运行的能耗最低。使用MATLAB搭建了仿真实验平台,验证了差分进化算法的有效性和实用性。     

民用飞机供水热技术仿真研究

研究民机供水系统温度加热控制的优化问题,保证用水的舒适度.针对管道加热器是非线性、时变的控制对象,为实现即热、舒适节能,提出设计采用模糊/PID开关切换和自适应模糊PID控制的两种系统模型控制器,建立仿真模型,研究模糊控制器控制规则和参数优化设计,比较分析两种系统模型控制方法与基本PID控制方法的水温控制性能.仿真结果表明,利用模糊/PID开关切换控制的民机供水温度控制系统可达到供水即热,节能省水的效果.     

数字PID变频调速在PLC恒压供水系统中应用

本文主要介绍了变频调速在恒压供水系统中的应用,系统中采用了数字滤波技术和数字PID控制技术,有效的减小了系统中的干扰,提高了系统的稳定性.     

智慧校园供水系统的可视化及其关系模型的构建

智慧校园供水系统是智慧校园的重要组成,为了提升智慧校园的服务和管理水平,文章就供水系统的用水可视化及用水量预测这两个问题展开分析和研究.利用可视化技术和回归模型对校园供水情况进行深入挖掘和分析,为智慧校园供水系统的智能化管理提供更多的信息和帮助.     

基于灰色支持向量机的城市用水量预测研究

研究城市用水量预测问题,城市需水量具有非线性和随机波动性,城市供水系统是复杂系统,传统灰色模型或支持向量机均无法进行准确预测。为更准确预测城市用水量,建立灰色支持向量机的城市用水量预测模型,以提高预测精度。首先采用灰色模型和支持向量机对城市用水量预测,然后采用多元线性回归确定模型权重系数,最后得到了灰色支持向量机的城市用水量预测结果。仿真结果表明,灰色支持向量机提高了城市用水量的预测精度,预测误差小于单一灰色模型和支持向量机。灰色支持向量机更好地反映出城市用水量变化的总体趋势,可为城市供水系统预测提供参考。     

A branch and bound algorithm for the travelling salesman and the transportation routing problems

In this paper, a branch and bound model with penalty tour building is developed for solving travelling salesman and transportation routing problems. The algorithm for determining the optimal solution of the problem is of the general form which can solve symmetric and asymmetric single and multiple travelling salesman problems (STS and MTS), and the transportation routing problems with capacity restrictions for the vehicles of same or of different capacities. The behavior of the developed algorithm is tested with randomly generated data. As an application, the routes for gas distribution is planned and carried out in several interior towns of a state, with the objective of making an efficient distribution in order to minimize the total cost of delivery with an optimal solution.     

医药配送系统仿真模型及优化

物流配送已成为电子商务发展中的关键问题之一,也是供应链管理中的核心问题。该文利用仿真软件建立了包括制造商、医药分销中心和零售商的物流配送系统的仿真模型。通过对模型进行仿真可观察物流配送的动态运行情况,从而找到配送过程的瓶颈。在建立的仿真模型基础上及给定的约束条件下,通过构造目标函数对仿真模型进行了优化。通过优化,给出了医药分销中心最优的订购时间间隔和不同药品的最佳库存量,这为实际决策提供了正确依据。     

基于多代理辅助多目标进化优化的建筑节能智能设计方法

进化优化具有优异的全局搜索能力,已成功应用于建筑节能设计问题.然而,由于需要借助代价高昂的建筑能耗软件不断评价个体,现有建筑节能设计进化算法普遍存在运行代价高的问题.鉴于此,提出一种面向建筑节能设计的多代理辅助多目标进化优化算法,简称MS-MOEA/D.首先,依据MOEA/D的目标分解特征同时构建多个基础代理模型;然后,针对每个待评估个体,自动选择合适的基础代理模型,并使用它们的集成结果预测该个体的目标值,达到提高其预测精度的目的.同时,在进化过程中自主确定基础代理模型的更新时机和规模,以降低代理模型的管理成本;最后,将所提出MS-MOEA/D与建筑能耗模拟软件EnergyPlus相融合,建立面向建筑节能设计的多目标进化优化仿真平台,并将该平台应用于中国北京地区常见居民和办公建筑节能设计实例中.通过与7种典型多目标进化算法进行对比,结果表明, MS-MOEA/D在显著降低计算代价的基础上能够得到高竞争力的Pareto最优解集.     

Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

Bayesian optimization algorithm (BOA) is one of the successful and widely used estimation of distribution algorithms (EDAs) which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network (BN) is a well-known graphical model which is used in BOA. Learning a proper model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.     

Benefits of an item-centric enterprise-data model in logistics services: A case study

The paper uses a case example to present a novel way of building enterprise information systems. The objective is to bring forth the benefits of an item-centric systems design in environments that require real-time material visibility, such as in logistics service provision. The methodology employed is case study and metadata modeling. Managers of SE Mäkinen, a Finnish car distribution company were interviewed on the implementation and operation of their award winning enterprise system. The case example was then analyzed using a generic metadata model of item-centric systems.The main finding of the paper is that introducing an item-centric model facilitated responsive service in the distribution of automobiles. The practical implications are that when starting to develop a new enterprise system, managers in logistics services should consider an item-centric design solution as an option to the conventional location-based design for enterprise-data models.     

Global Information Broadcast: an architecture for Internet pushchannels

The Global Information Broadcast architecture establishes a publisher-subscriber distribution model for “push” applications and a framework for building them in a uniform, interoperable way. GIB uses connectionless network protocols to overcome the problems associated with push applications based on the client-server distribution model     

遗传算法在公交调度中的应用

针对泰州市公交智能化建设方面存在的技术问题,提出将遗传算法(Genetic Algorithms,GA)应用到公交调度优化中。以公交公司和乘客费用最少、社会效益最大为目标函数建立公交调度优化的数学模型,并应用遗传算法实现对模型的求解,通过对模型设置不同的参数,利用Matlab模拟实验验证模型的合理性、科学性。实验证明,优化的调度模型可降低公交公司的运营成本,提高乘客的满意度,确保社会效益和经济效益得到最大满足。     

改进神经网络在粮油配送中心选址中的应用

在建立以控制成本为核心的粮油配送中心选址模型的基础上,分析了选址过程中的影响因素;结合人工神经网络与选址影响因素之间的特点,引入了经遗传算法优化的神经网络模型。为解决遗传算法易陷入局部最优的问题,提出了一种在变异算子中增加禁忌表的方法。实例求解表明,与专家评审的选址决策相比,该算法能够有效解决粮油配送中心的选址问题。     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

Floor shape optimization for green building design

Shape is an important consideration in green building design due to its significant impact on energy performance and construction costs. This paper presents a methodology to optimize building shapes in plan using the genetic algorithm. The building footprint is represented by a multi-sided polygon. Different geometrical representations for a polygon are considered and evaluated in terms of their potential problems such as epistasis, which occurs when one gene pair masks or modifies the expression of other gene pairs, and encoding isomorphism, which occurs when chromosomes with different binary strings map to the same solution in the design space. Two alternative representations are compared in terms of their impact on computational effectiveness and efficiency. An optimization model is established considering the shape-related variables and several other envelope-related design variables such as window ratios and overhangs. Life-cycle cost and life-cycle environmental impact are the two objective functions used to evaluate the performance of a green building design. A case study is presented where the shape of a typical floor of an office building defined by a pentagon is optimized with a multi-objective genetic algorithm.