多锅炉母管制运行负荷分配与燃烧控制

目前国内多台链条炉母管制运行普遍采用人工手动控制,自控程度不高。结合具体工程项目,分析了多台链条炉母管制运行在控制过程中存在的难点,并针对这种结构的运行特点,提出了一种新的控制方案。该方案既考虑了单台链条炉的燃烧控制特点,又考虑了多台链条炉负荷分配问题。现场应用表明,该方案主蒸汽母管压力稳定,较人工手动控制波动明显减小。     

母管制机组的炉机负荷分配运算与协调控制

论述了母管制热电机组锅炉、汽机的负荷分配运算和协调控制原理。针对实际运行中可能遇到的多种情况，给出了在燃料量可调和不可调两种情况下的负荷分配运算及协调控制方案，显提高经济效益。     

门的自动分配算法及其实现

门自动分配是从逻辑图开始的成套印制电路板全自动设计的重要环节。本文采用直接结合度和间接结合度加权算法。在分配中,既考虑有共同信号门,又计及同时与某器件有共同信号的几个门,合理地解决了门与门,门与其他器件联系的“紧密”与“松散”的问题。还采用自动分配与手工分配多种工作方式,以增强灵活性和实用性。     

基于PCS7的并列运行锅炉燃烧控制系统

针对并列运行锅炉存在主蒸汽母管压力波动较大和各个锅炉燃烧不稳定的问题,分析了主蒸汽母管压力内、外扰动的形成原因以及影响锅炉燃烧的各种因素,提出了能保持蒸汽母管压力稳定的加前馈的串级控制方案。利用西门子公司的PCS7过程控制系统,实现了并列运行锅炉燃烧过程的自动控制。该控制系统使用Profibus现场总线进行现场站级之间的通讯,而工作站级和现场站级之间的通讯是通过工业以太网环网。生产实践证明,该控制方案设计合理、控制系统使用方便、鲁棒性强,基本满足了并列运行锅炉燃烧过程自动化的要求。     

一种基于和利时DCS的热电厂母管制锅炉负荷分配的优化控制方法

热电厂具有供电和供热的双重功能,比普通电厂拥有更高的能源利用率,广泛存在于电力、石化、冶金、造纸和热电联产等企业之中。多数企业热电厂为更灵活满足生产热负荷需求,普遍采取多炉母管制运行方式,多炉蒸汽在母管混合后统一为后续生产的热负荷,因此母管蒸汽压力的稳定性对后续工艺生产至关重要。要实现母管蒸汽压力稳定控制就需要考虑各炉的负荷的平衡分配,因多台锅炉的燃烧状态不统一,多台炉并列运行时往往存在抢负荷现象,导致锅炉之间负荷偏差较大,在生产热负荷需要求变化时不能及时响应,母管压力波动大,给后续生产运行稳定带来较大影响。本文针对此问题进行了分析探索,设计了一种基于和利时DCS母管制锅炉在一定偏差基础上负荷分配的控制方法。     

基于直接能量平衡和负荷分配的母管制机组控制

本文针对母管制机组中存在母管压力波动大的问题,基于直接能量平衡和负荷分配,提出了一种母管制机组协调优化的控制方案.同时对此方案的构思进行了论述和分析,并通过生产实践证明了此设计方案的可行性.     

基于自适应免疫克隆算法的电力经济负荷分配

为了满足电力系统功率平衡,发电成本最小,研究在电力系统经济负荷分配问题的数学模型基础上,提出了一种自适应免疫克隆算法来计算电力系统经济负荷分配.通过引入尺度变换、精英克隆变异、启发式克隆交叉和克隆选择等概念使得免疫算法在理论上保证了解的多样性.利用3机6母线电力系统进行了仿真,结果表明算法是可行的,并且与遗传算法的智能相比,改进算法全局搜索能力强,不易陷入局部最优解,同时收敛速度快.     

计算机负荷并行处理技术的智能化分配算法

随着科技的日益发展,我国的计算机也飞速发展,而要想达到计算机负荷并行处理技术的最优化问题以及高效稳定,就必须利用智能化分配算法,这是因为智能化分配算法既可以自己选择启动的策略,还可以自己选择各个节点的状态,此外,智能化分配算法可以通过分配原则把总的负荷完美分配到该进行处理的节点上,这样就使得计算机的负荷总体平均、处理花费最小以及通信花费最小。文章通过一个假设的单级树形网络来进行智能化分配算法的分析。     

基于智能化分配算法的计算机负荷并行处理技术探究

随着科技的日益发展和互联网时代的飞速发展,计算机智能化分配算法的计算机负荷并行处理技术得到了高效稳定的发展,然而,计算机的负荷并行处理技术,主要运用智能分配算法。本文主要是围绕计算机智能化分配算法,计算机负荷并行处理技术的方面展开讨论和探究。     

沿河发展型城市物流配送路径的优化模型及算法

针对沿河发展型城市物流配送路径的优化问题,建立相应的数学模型,并在传统物流配送路径优化问题的经典算法——遗传算法的基础上,提出了一种新算法。该算法通过设计一种选择性部分匹配交叉算子和选择性倒置变异算子,加快了最优解的搜索速度,从而更快地求得最短回路,再结合沿河发展型城市的实际地理环境,得到符合实际的物流配送最优路径。通过实例和仿真分析,表明了算法的有效性和优越性。     

A memetic algorithm applied to the design of water distribution networks

The optimal design of water distribution networks is a real optimization problem that consists of finding the best way to convey water from the sources to the users, satisfying their requirements. Many researchers have reported algorithms for minimizing the network cost applying a large variety of techniques, such as linear programming, non-linear programming, global optimization methods and meta-heuristic approaches. However, a totally satisfactory and efficient method is not available as yet. Many works have assessed the performance of these techniques using small or medium-sized benchmark networks proposed in the literature, but few of them have tested these methods with large-scale real networks. This paper introduces a new memetic algorithm for the optimal design of water distribution networks. In order to establish an accurate conclusion, five other approaches have also been adapted, namely simulated annealing, mixed simulated annealing and tabu search, scatter search, genetic algorithms and binary linear integer programming. The results obtained in three water distribution networks show that the memetic algorithm performs better than the other methods, especially when the size of the problem increases.     

A matching algorithm for the distribution of human pancreatic islets

The success of human pancreatic islet transplantation in a subset of type 1 diabetic patients has led to an increased demand for this tissue in both clinical and basic research, yet the availability of such preparations is limited and the quality highly variable. Under the current process of islet distribution for basic science experimentation nationwide, specialized laboratories attempt to distribute islets to one or more scientists based on a list of known investigators. This local decision making (LDM) process has been found to be ineffective and suboptimal. To alleviate these problems, a computerized Matching Algorithm for Islet Distribution (MAID) was developed to better match the functional, morphological, and quality characteristics of islet preparations to the criteria desired by basic research laboratories, i.e., requesters. The algorithm searches for an optimal combination of requesters using detailed screening, sorting, and search procedures. When applied to a data set of 68 human islet preparations distributed by the Islet Cell Resource (ICR) Center Consortium, MAID reduced the number of requesters that (a) did not receive any islets, and (b) received mis-matched shipments. These results suggest that MAID is an improved more efficient approach to the centralized distribution of human islets within a consortium setting.     

Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design

The application of evolutionary algorithms (EAs) is becoming widespread in engineering optimisation problems because of their simplicity and effectiveness. The Alopex-based evolutionary algorithm (AEA) possesses the basic characteristics of heuristic search algorithms but is lacking in adequate information about the fitness landscape of the input domain, reducing the convergence speed. To improve the performance of AEA, the Gaussian copula estimation of distribution algorithm (EDA) is embedded into the original AEA in this paper. With the help of Gaussian copula EDA, precise probability models are built utilising the best solutions, which can increase the convergence speed, and at the same time, keep the population diversity as much as possible. The simulation results on the benchmark functions and the application to the Butterworth filter design demonstrate the efficiency and effectiveness of the proposed algorithm, compared with several other EAs.     

An EM algorithm for estimating the parameters of bivariate Weibull distribution under random censoring

We consider the problem of estimation of the parameters of the Marshall-Olkin Bivariate Weibull distribution in the presence of random censoring. Since the maximum likelihood estimators of the parameters cannot be expressed in a closed form, we suggest an EM algorithm to compute the same. Extensive simulations are carried out to conclude that the estimators perform efficiently under random censoring.     

人脸特征潜在分布规律的人脸注释算法

提出了一种简单有效的人脸注释改进算法。该算法将二值边缘图像的梯度映射和人的面部特征的潜在分布规律相结合。首先应用二值边缘图像的按水平方向以及按垂直方向的梯度的映射,快速确定人脸位置以及人眼的粗略位置;然后对人眼的位置使用逐步逼近法进行进一步的精确定位,再利用图像垂直方向的梯度映射确定嘴的位置;结合人眼和嘴的位置,以及人脸面部特征的潜在分布规律,确定鼻子的位置。     

An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem

In this paper, an estimation of distribution algorithm (EDA) is proposed to solve the multi-mode resource-constrained project scheduling problem (MRCPSP). In the EDA, the individuals are encoded based on the activity-mode list (AML) and decoded by the multi-mode serial schedule generation scheme (MSSGS), and a novel probability model and an updating mechanism are proposed for well sampling the promising searching region. To further improve the searching quality, a multi-mode forward backward iteration (MFBI) and a multi-mode permutation based local search method (MPBLS) are proposed and incorporated into the EDA based search framework to enhance the exploitation ability. Based on the design-of-experiment (DOE) test, suitable parameter combinations are determined and some guidelines are provided to set the parameters. Simulation results based on a set of benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EDA.     

A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem

In this paper, a hybrid estimation of distribution algorithm (HEDA) is proposed to solve the resource-constrained project scheduling problem (RCPSP). In the HEDA, the individuals are encoded based on the extended active list (EAL) and decoded by serial schedule generation scheme (SGS), and a novel probability model updating mechanism is proposed for well sampling the promising searching region. To further improve the searching quality, a Forward-Backward iteration (FBI) and a permutation based local search method (PBLS) are incorporated into the EDA based search to enhance the exploitation ability. Simulation results based on benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed HEDA.     

Investigating the run-time searching behavior of the differential evolution algorithm applied to water distribution system optimization

In recent years, the differential evolution algorithm (DEA) has frequently been used to tackle various water resource problems due to its powerful search ability. However, one challenge of using the DEA is the tedious effort required to fine-tune parameter values due to a lack of theoretical understanding of what governs its searching behavior. This study investigates DEA's search behavior as a function of its parameter values. A range of behavioral metrics are developed to measure run-time statistics about DEA's performance, with primary focus on the search quality, convergence properties and solution generation statistics. Water distribution system design problems are utilized to enable investigation of the behavioral analysis using the developed metrics. Results obtained offer an improved knowledge on how the control parameter values affect DEA's search behavior, thereby providing guidance for parameter-tuning and hence hopefully increasing appropriate take-up of the DEA within the industry in tackling water resource optimization problems.