动态规划法在输水管网优化中的应用

以某输水管网为例,采用动态规划法对其进行优化。以管网年折算费用最小为目标函数,以节点水压等为约束条件,用枚举法对备选管径进行优选,使目标函数取得最小值的那一组为最优管径。     

树状灌溉管网优化的整数编码改进遗传算法

建立了以投资最小为目标函数的树状灌溉管网优化数学模型,并采用整数编码改进遗传算法对模型进行求解。针对完整树状管网优化的特点,通过对整数编码的编码方式进行改进,实现了同时对管网中管线连接关系和管径的优化。改进了目标函数映射到适应函数的变换方式,设计了随机多父辈交叉方法,提出了2种与优化问题相适应的变异方法,从而提高了算法的优化性能。     

不同形状地块滴灌田间管网的优化研究

在前人研究规则地形的基础上,对不同形状地块滴灌田间管网的优化建立了数学模型,布局方面采用正交原则加经验方法,管径方面采用遗传算法。在不规则地形优化时先对其进行简化,然后提出了"补全法"和"分割法"对其进行优化,有效地解决了不同地块形状滴灌田间管网的优化。     

中粉质壤土泥浆泵法修筑淤地坝人工排水管网布设优选研究

以延安市志丹县前拐沟淤地坝筑坝现场修筑的人工试验小区为研究对象,使用土水比为1.0和1.5的泥浆,进行3种管道布设方式（横向管道、竖向管道、横竖向结合管道）、3种灌浆高度（30、60、90 cm）下的排水管网优选试验。结果表明,横竖结合管道的排水效果最好,竖向管道次之,且二者差距不大,横向管道最差,实际筑坝中宜优先选用竖向排水管道;在布设竖向管道和布设横竖结合管道2种情况下,管道排水均集中在试验最初的12 h内,且同种布设方式管道排水效果受灌浆高度的影响不大,可适当缩短1次灌浆后的静置时间和适当增大灌浆高度以缩短筑坝工期。     

基于进化神经网络的火灾后混凝土强度评估

高温后混凝土强度的评估是判断受火建筑的结构损伤程度、剩余承载力的重要依据，设计了一个进化神经网络模型。用遗传算法优化RBF网络的连接权和网络结构，并将其应用于火灾后混凝土强度的评估，介绍了混凝土立方试块经受不同火温后进行回弹测试混凝土强度的方法．经实验测试和仿真分析证明．所提出的进化算法用于RBF神经网络优化比基于聚类法的RBF网络具有更高的识别精度．     

基于高光谱数据的土壤含盐量BP神经网络模型研究

运用Hyperion数据,以黑龙江省大庆市某一实验区为例,进行了对土壤含盐量的定量研究,将BP神经网络模型(Back Propagation Network)应用到高光谱数据对研究地区土壤含盐量的反演中。通过对隐含层的传递函数、输出层的传递函数、训练算法的优化组合以及最适合隐层节点数量,得到最优的BP神经网络模型,实现了土壤含盐量的反演。对高光谱数据反演土壤含盐量采用BP神经网络具有一定的指导意义。     

土壤环境质量评价的径向基函数神经网络的模型设计与应用

针对土壤环境质量评价缺乏精确客观的评价数值作为目标集的难点,研究了构建起对应升序排列的训练、检验样本集。在此基础上,定义以检验集输出评价结果的排序误排率作为判定各参数组合下RBF网络性能优劣的指标,以此性能指标为依据,在MATLAB6.5环境下,用编程语句实现了应用于该质评工作的径向基函数网络的大量参数组合的全面快速寻优,克服了目前用试凑法进行参数组合优选的随机性,确保所选参数组合为使整体性能最优的组合。利用确立的网络对象,以某地土壤重金属监测值为实例,对监测点进行质评,评价结果表明:该设计好的网络的评价结果科学、稳定、表达精度高。     

管流剪切对改性原油流动性的影响

依据流体剪切形变的原理，导这中流体的剪切速率分布函数和当量剪切速率计算公式，提出了管道湍流的脉动剪切速率要领及其计算方法。按照当量剪切强度的相等的原则，给出了管道层流与湍汉及粘度计之间的等效剪切模拟基础，并指出通常把工业管道的管壁剪速作为当量剪切速率来模拟管道湍流剪切是不准确的，特别是高雷诺数时，把管壁剪速看作当是剪切速率的模拟试验会严重扩大工业管道中管流剪切的影响。文中给出的计算方法和模拟方法，     

基于混合粒子群算法的供热管网优化设计

供热管网优化设计一直是多年来城市地下管网工程中的研究热点。通过分析供热管网的优化模型,建立关于供热管网的目标函数即供热管网投资费用,根据供热管网的目标函数及约束条件建立适应度函数。利用粒子群优化算法对该非线性模型进行求解,借鉴遗传算法中变异操作的思想,设计基于遗传算法的混合粒子群算法,寻求在水力约束条件下目标函数的最小值。实例结果表明,将粒子群优化算法应用于供热管网优化设计可以取得较好的优化结果,并且充分的体现出粒子群算法的寻优能力。     

基于双频蝙蝠算法的树状灌溉管网规划

以管网投资费用最低为目标,建立了改进的自压树状灌溉管网规划模型,通过定义管网用水节点的上层水节点来保证管道连通性;同时,提出了改进的双频蝙蝠算法,通过使用双脉冲频率策略均衡算法多样性与收敛性。基于12个100维度测试函数的寻优结果表明,与遗传算法、粒子群算法及蝙蝠算法相比,双频蝙蝠算法能够有效提高全局搜索能力;应用双频蝙蝠算法分别对10节点和34节点的管网进行规划设计,管网投资费用均值较蝙蝠算法可分别减少23.60%和31.03%。     

基于遗传神经网络的陕西省土地利用结构模型研究

针对土地利用结构变化的复杂特征与BP人工神经网络在预测应用上的缺陷,利用遗传算法来优化BP人工神经网络模型,形成了遗传算法与BP网络结合的遗传神经网络模型,通过运用matlab软件编制神经网络和遗传神经网络程序,实现模型的建立。证明遗传神经网络用于土地利用结构的预测是可行和有效的,有着良好的前景。     

埋地热油输送管道的经济管径计算模型

考虑了输送温度对油品粘度的影响以及油品粘度和输送量对泵效、流态和经济管径的影响,以管道建设投资的年分摊费用、保温投资的年分摊费用、管道年维护费用、年动力消耗费用和年热力消耗费用之和为目标函数,给出了埋地热油管道经济管径的完整计算模型。该模型能自动满足管道的沿程温降约束条件,适用于不同粘温特性的原油、不同保温材料和不同埋地深度的管道。采用斐波那契搜索算法对所建模型进行了算例计算,结果表明,该模型能较好地反映各种因素对经济管径的影响,可为埋地热油管道的优化工艺设计提供理论依据。     

Optimization Design of Multi-hole and Varied Diameter Pipe Based on RAGA

Combining real accelerating genetic algorithm(RAGA) with the optimization design of multi-hole and varied diameter pipe, the authors solved the problem of optimizing multi-dimensional parameters at the same time. In which the advanced convergence and easily to run into partial optimization were avoid. Applied the RAGA to solving the problem in the optimization design of fixed piping sprinkler irrigation system. The optimized parameters, such as diameters and the length of pipe were calculated and the result was reasonable, which provides as a reference to readers who work at related research.     

小工业园区排水系统存在的缺陷及其对河涌污染的影响研究

针对小工业园区排水系统存在的问题,提出了整改建议,如加强排污企业的监管,尽快监督落实排水许可制度;改造各个污水直排口;完善排水体制,建立污水管网验收制度,对局部排水体制进行改造等措施,为乡镇工业园区污水管网的建设和改造提供参考。     

等温输油管道运行方案优化方法及其应用

根据能量平衡原理,以各站的升压值作为决策变量,以全线的总动力费用为目标函数,建立了等温输油管道优化运行的数学模型;采用动态规划方法,利用管道进出站压头的约束条件,确定了各站的供给能量,进而获得全线各站升压值的最佳组合,制定运行优化方案。以大庆-锦西原油管道为例,对运行方案最优工况的确定方法和过程进行说明,并获得了经济合理的运行参数,可为制定管道运行优化方案提供技术支持。     

管灌管网经济管径的确定

本文根据管网优化设计年费用最小的原则,结合低压管灌的运行特点,推导出确定管灌管网经济管径的计算公式。为了便于实际应用,同时给出了井灌区管灌典型情况的树状网和环状网管网系数的计算公式。     

城市天然气管网的选材问题

在城市燃气中，气源由天然气取代人工煤气已成为一种发展趋势，针对天然气置换人工煤气后城市配气管网出现的问题，从安全性、适应性及经济性方面对城市配气管网常用管材（如铸铁管、PE管和ERW管）的各项技术指标进行了对比分析，指出ERW管是城市燃气管网的首选管材。     

Multi–objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function (RBF) neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-II could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃ to 25℃,and the power consumption was 0.5 MJ.Compared with the three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1% and 28.5%,respectively.     

温室地热系统结构设计及节能环保效益

为了达到节能环保效益,采用传热效率高、结构简单且投资小的分离式热管换热器作为温室地热系统,对其进行了结构设计和计算,选择了适当的工质和材料,并对它的节能和环保效益进行了阐述,说明了分离式热管系统的可靠性和实际应用的价值,为其在实际应用中提供可靠依据。     

A novel algorithm for damage recognition on pest-infested oilseed rape leaves

Cabbage caterpillar infestation of oilseed rape will leave wormholes on leaves. The percentage of wormholes’ area on leaf is an effective index to evaluate infestation seriousness. Hyperspectral imaging technology can be used to extract leaf from non-vegetation objects efficiently. Wormhole reconstruction can then be carried out for counting the wormholes’ area. The reconstruction of wormholes that are entirely within the leaf contour can be easily processed by holes filling function. However, it is difficult to process wormholes at the edge of a leaf. A novel location factor and an improved genetic-wavelet neural network reconstruction algorithm (G-WNNRA) have been proposed in this paper to process wormholes at the edge of a leaf. For the edge of a damaged leaf, the infested part represented by a hole at the edge and non-infested part should be distinguished automatically. Thus the novel location factor which was based on the first derivative of inverse function was used to develop test function for locating the infested part. Then the proposed G-WNNRA was constructed to reconstruct the missing part of an edge following the step of learning the non-infested part of the edge. The topological structure and parameters of the G-WNNRA was optimized by genetic algorithm and morlet wavelet function was applied as a transfer function. The points on non-infested part of edge were adopted as the training data set and the missing part of the edge were predicted. During the prediction, the points making up the reconstructed edge were chosen based on the output of the G-WNNRA. For performance comparison, wavelet neural network (WNN), genetic neural network (GNN) and back propagation neural network (BPNN) were tested on infested oilseed rape leaves and the RMSE of G-WNNRA was smaller than those of WNN, GNN and BPNN. The proposed location algorithm and G-WNNRA can be combined to reconstruct infested oilseed rape leaves.