Simulation on an optimal combustion control strategy for 3-D temperature distributions in tangentially pc-fired utility boiler furnaces

The control of 3-D temperature distribution in a utility boiler fumace is essential for the safe, economic and clean operation of pc-fired fumace with multi-burner system. The development of the visualization of 3-D temperature distributions in pcfired fumaces makes it possible for a new combustion control strategy directly with the fumace temperature as its goal to improve the control quality for the combustion processes. Studied in this paper is such a new strategy that the whole fumace is divided into several parts in the vertical direction, and the average temperature and its bias from the center in every cross section can be extracted from the visualization results of the3-D temperature distributions. In the simulation stage, a computational fluid dynamics(CFD) code served to calculate the 3-D temperature distributions in a furnace, then a linear model was set up to relate the features of the temperature distributions with the input of the combustion processes, such as the flow rates of fuel and air fed into the furnaces through all the bumers. The adaptive genetic algorithm was adopted to find the optimal combination of the whole input parameters which ensure to form an optimal 3-D temperature field in the fumace desired for the operation of boiler. Simulation results showed that the strategy could soon find the factors making the temperature distribution apart from the optimal state and give correct adjusting suggestions.     

大气二氧化硫污染预测模型初探

考虑了影响大气扩散的主要气象因素及其它因素,提出了一个大气SO_2污染预测模型,并运用加权实时最小二乘算法对模型进行实时辨识。这一模型尤其适用于城市及城市中SO_2日均浓度的分区预测,给出了该模型在上海地区应用的结果。     

长江常熟段排污口布置方案数值模拟论证研究

为合理确定常熟氟化工项目排污口布置方案，针对长江常熟段水环境特征，尤其是复杂的水地下形和水流流场，建立为较为准确和实用的平面二维水流水质耦合数学模型，应用剖开算子法模拟计算了3套排污方案对重点保护水域影响程序度和范围，并从水流场，水质影响评价，技术经济多角度对方案进行了比较论证，最终推荐污水江中排放的方案，为项目的工程设计和环境管理提供了科学依据。     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

疏散路径受阻情况下的人员疏散模型及算法

建筑火灾中,随着火势的不断增大和烟气的不断增多,随时会有某条疏散路径因被火势或烟气封堵而不能正常通行的情况发生。针对此情况下的应急疏散问题,以待疏散人员全部完成疏散所需时间最短为目标,以结合疏散路径的通行能力合理分配待疏散人员为原则,运用Dijkstra算法对网络中的最短路径进行求解,并结合网络流控制的方法实现疏散人员的合理分配,建立了疏散路径受阻情况下的人员疏散模型,并提出了该模型的算法思想及算法步骤,最后结合算例进行了验证。验证结果表明,该模型及算法可行、有效,既可较好地避免因大量人员选择相同疏散路线而造成的拥堵,还提高了疏散网络的整体使用率,又有效缩短了人员的疏散所需时间。     

基于网络流理论的矿井通风网络可靠性研究

矿井通风网络是通风系统的重要组成部分,对其进行可靠性研究具有非常重要的意义。针对矿井通风网络系统的自身属性,结合网络流理论建立了通风网络可靠性评价模型。对各风路的风量进行动态实时监测,并运用统计学方法分析了风网中风流的变化趋势及风量分布规律,得到了通风系统中任一风路风量的分布密度函数,进而推导出各风路的可靠性指标值。运用简化的邻接终点矩阵法求解通风网络最小路集并基于MATLAB编程实现此算法。提出了一种简化的不交化最小路集算法:对于任一长度为m-1的最小路,只需在其中添加n-m+1条该最小路中不出现的分支的逆,就可直接获得不交化结果,其余最小路采用BDD算法进行不交化运算。通过以上方法可量化分析通风网络的可靠性。最后通过实例解算检验其可行性。     

基于定量遥感技术的重庆市热岛效应

近年来，城市热岛效应不断增强，已经成为一个突出的城市环境问题。重庆市作为长江中上游特大型城市，工业发达，经济快速发展，市区热岛效应明显，但是很少对其研究。以2007年Landsat5 TM影像为数据源，利用单窗算法对重庆市城市地表温度进行反演，并进行城市地面亮温等级划分，在从城市土地利用类型、NDVI等方面对重庆市城市热岛效应强度和空间格局分布及其特征进行了探讨。结果表明：城区热岛现象显著，且分布不均匀；不同土地利用类型对热岛效应影响不同；城市地面亮温与NDVI呈明显的线性负相关关系。最后结合重庆市的特殊地形，提出了以下解决城市热岛效应的对策：加强城市规划，调整城市结构；构建最佳绿化结构，提高城市绿化率；改变能源结构，提高能源利用率，控制工厂废气排放量。     

遗传神经网络在煤层自燃预测中的应用

煤层开采自燃危险性程度与许多影响因素之间存在着复杂的非线性关系，利用人工神经网络技术和人工生命学中的遗传算法，建立了预测模型，不但很好地解决了该问题，同时也大大提高了运算速度。     

A novel framework-based cuckoo search algorithm for sizing and optimization of grid-independent hybrid renewable energy systems

Hybrid renewable energy systems (HRES) turned into an appealing choice for supplying loads in remote areas. The application of smart grid principals in HRES provides a communication between the load and generation from the HRES. Using smart grid in the HRES will optimally utilize the generating resources to reschedule the loads depending on its importance. This paper presents a new proposed design and optimization simulation program for techno-economic sizing of grid-independent hybrid PV/wind/diesel/battery energy system using Cuckoo search (CS) optimization algorithm. Using of CS will help to get the global minimum cost condition and prevent the simulation to be stuck around local minimum. A new proposed simulation program (NPSP) is acquainted using CS to determine the optimum size of each component of the HRES for the lowest cost of generated energy and the lowest value of dummy energy, at highest reliability. A detailed economic methodology to obtain the price of the generated energy has been introduced. Results showed that using CS reduced the time required to obtain the optimal size with higher accuracy than other techniques used iterative techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Numerous significant outcomes can be extracted from the proposed program that could help scientists and decision makers.