基于改进TOPSIS模型的河道整治优选方案评价

针对河道整治方案优选的多目标决策问题,利用TOPSIS法构建评价模型,采用目标差值率对原始数据进行无量纲化处理,并利用变异系数赋权法对各项指标进行客观赋权,同时采用改进的相对贴近度计算方法来弥补传统方法使用欧氏距离的不足,建立了改进的TOPSIS模型。实例应用表明,将改进的TOPSIS模型应用于河道整治方案优选中,有效可行、计算简便。     

基于多目标GLUE算法的新安江模型参数不确定性研究

针对新安江水文模型参数的不确定性问题,以湘江湘潭断面控制流域为例,采用新安江日模型和两个纳什效率系数作为水文模型参数率定的目标函数,结合SCE-UA优化算法,分析了不同目标函数对实测径流的模拟能力,在此基础上进一步分析了单目标GLUE方法、多目标GLUE方法和NSGA-Ⅱ优化算法获得不确定性区间特征值。结果表明,不同目标函数对径流过程显示了不同的模拟性能;NSGA-Ⅱ算法可优化多目标拥有较小的不确定性区间,但区间对实测值的覆盖率较小;与单目标算法相比,在相同的区间宽度下,多目标GLUE方法具有更高的区间覆盖率。     

基于Morris法的HBV模型参数敏感性分析

随着社会的发展和科技的进步,基于计算机运算的水文模型越来越多地应用于各个流域。为了提高水文模型的预测精度,选择误差平均值、均方根误差、相关系数、转换后的均方根误差和流量历时曲线斜率为目标函数,以绥芬河流域2009~2012年的降水径流资料为基础,通过Morris方法运行HBV模型,分析不同目标函数下参数敏感度指数的值及置信区间收敛情况,发现不同目标函数下参数的敏感性有差异,参数越敏感,达到收敛需要的样本数越多。     

误差平方和目标函数在参数率定过程中遇到的问题

目标函数是参数估计最重要的信息来源,针对目前大多以误差平方和目标函数为参数率定方法,通过对误差平方和目标函数的结构及单参数、多参数模型进行实例计算分析,分别发现该方法遇到不同样本所对应的参数局部优值不同、不同样本组合的参数信息不合理、甚至会提供错误参数估计信息等问题,提出了选取合适目标函数的建议,为提高参数率定的精度和效果提供依据。     

基于数值模拟的矩形微槽热沉流道的优化设计

为了提高矩形微槽热沉的散热性能,首先利用热阻网络模型对数值方法进行验证;然后分别对斜槽开度u为0.2 mm时不同形式的流道进行数值模拟,找到最优的开槽方式;再以双排叉槽的斜槽开度u为设计变量,以热阻与泵功率为优化目标分别利用多项式回归与径向基函数进行代理模型的构建,通过精度分析,利用多目标遗传算法对径向基函数模型进行寻优,最后利用熵权TOPSIS法进行优化方案的选取。结果表明:泵功率随着斜槽开度的增大而逐渐降低,而热阻随着斜槽开度的增大先减小后增大。优化后槽道的斜槽开度u为0.3213 mm,与不开斜槽的矩形直槽道相比热阻与泵功率分别降低了26.62%与19.62%。     

盱江流域径流模拟及其对气候变化的响应

为研究盱江流域径流对气候变化的响应,构建了基于SWAT的分布式水文模型。采用SUFI-2法对SWAT模型的参数进行率定和验证。率定期和验证期的决定性系数R2和纳什系数Ens均大于0.9,模型在该流域的适用性较好。在SWAT-CUP中引入参数标识符建立49种气候情景(温度升高或降低1～3℃,降水增加或减少10%～30%)并代入模型中模拟径流。结果表明,温度与径流呈弱负相关;降水与径流呈强正相关;温度对径流的影响远小于降水对径流的影响。为直观表达降水-温度-径流量的关系,模拟三者空间平面关系函数,其决定性系数R2约等于1、和方差为0.000 647 4、均方根误差为0.003 752,降水-温度-径流的关系可用平面函数表达。     

Hymod模型参数敏感性分析和多目标优化

以汉江上游向家坪水文站以上流域为研究对象,通过对集总式模型Hymod的参数敏感性分析和多目标参数优化,分析了Hymod模型5个主要参数的敏感性特征,体现了该模型描述降雨径流过程特点的全面性,采用多目标遗传优化算法(NSGA-Ⅱ)对模型参数作多目标自动参数优化.结果表明,Hymod模型能较好地描述流域降雨径流过程特征,并能通过参数自动优化得到最优参数并取得理想的验证效果.     

月径流的分期组合预报模型及应用

针对年内汛期与非汛期径流变化规律的差异,建立了月径流分期组合预报模型.针对汛期径流预报不确定性较强的特点,该模型可量化预报不确定性并发布概率预报进而为制定决策提供参考.针对原贝叶斯平均模型参数优化方法存在的缺陷,提出了基于模糊优选模型的多目标粒子群算法优化参数.实例表明,该模型预报精度高,发布的概率预报对制定水电站的月调度计划具有一定的参考价值.     

基于统计学与新安江模型相结合的土地利用变化引起的径流响应分析方法

鉴于水文模型评价土地利用变化的特点,实际上水文模型的参数也可反映流域水文特性和降雨径流过程,不同时期的最优参数可以反映流域系统的变化。将统计学方法与水文模型相结合,分析土地利用变化对径流的影响,即先利用统计学方法判断降雨径流的趋势,找出降雨径流系列的突变点并据此划分不同时期,然后对不同时期日模型参数进行优选,并对参数变化结果进行对比分析。在大坡岭流域的应用表明,该方法对土地利用变化引起的径流响应分析行之有效。     

生成日径流资料的散粒噪声模型

描述时间连续日径流过程散粒噪声过程是非正态随机模型。本文对G.weiss提出的单散粒噪声模型中的涨水退水曲线的形式改用为Γ-分布的密度函数表示,推导出改进后的散粒噪声模型的一、二阶矩的计算公式和以日平均流量资料为依据的离散序列的一、二阶矩的递推公式。并用物理-统计相结合的方法估计有关模型参数,改进拟合程度。本文还对weiss提出的单散粒噪声综合数据的生成方法上做了修正。最后通过潮白河密云水库等站的日平均径流资料说明这模型的应用。     

TOPSIS方法应用中若干问题的探讨

应用效用函数理论分析了常用TOPSIS函数,从无差异曲线和边际替换率角度获得无差异曲面为平面族和超球面族两种基本TOPSIS价值函数,基于"差异驱动"原理建立了两类四种评价模型,提出了基于加速遗传算法的优化求解客观权重方法,并将其应用于丰满水库防洪调度决策中,在验证方法有效性的同时比较了不同偏好TOPSIS函数和不同权重确定方法对TOPSIS法的影响。结果表明,该方法更能体现经典的理想点法思想,TOPSIS函数在评价时对评价结果影响明显,但权重对评价结果的影响较大。     

考虑线路可用裕度问题的配电网多目标馈线规划

针对配电网线路布局与通道资源利用之间存在的矛盾,研究了配电网馈线规划的多目标模型,在考虑线路投资成本和网络损耗的同时,建立了可用裕度模型。针对该多目标模型提出了一种适用于配电网馈线规划的删除路径算法,并结合不同的网络复杂程度对算法中的K进行了函数拟合,最后通过逼近理想解排序法优选出最优方案。结果表明,考虑可用裕度模型的馈线布局方案可为下一个阶段的馈线规划尽可能的留有建设余地。     

Energy,exergy, and exergoeconomics (3E) analysis and multi-objective optimization of a multi-generation energy system for day and night time power generation - Case study: Dezful city

The present study aimed to investigate a multi-generation energy system for the production of hydrogen, freshwater, electricity, cooling, heating, and hot water. Steam Rankine cycle (SRC), organic Rankine cycle (ORC), absorption chiller, Parabolic trough collectors (PTCs), geothermal well, proton exchange membrane (PEM) electrolyzer, and reverse osmosis (RO) desalination are the main subsystems of the cycle. The amount of exergy destruction is calculated for each component after modeling and thermodynamic analysis. The PTCs, absorption chiller, and PEM electrolyzer had the highest exergy destruction, respectively. According to meteorological data, the system was annually and hourly tested for Dezful City. For instance, it had a production capacity of 13.25 kg/day of hydrogen and 147.42 m³/day of freshwater on 17th September. Five design parameters are considered for multi-objective optimization after investigating objective functions, including cost rate and exergy efficiency. Using a Group method of data handling (GMDH), a mathematical relation is obtained between the input and output of the system. Next, a multi-objective optimization algorithm, a non-dominated sorting genetic algorithm (NSGA-II), was used to optimize the relations. A Pareto frontier with a set of optimal points is obtained after the optimization. In the Pareto frontier, the best point is selected by the decision criterion of TOPSIS. At the TOPSIS point, the exergy efficiency is 31.66%, and the total unit cost rate is 21.9 $/GJ.     

计及(火用)分析的综合能源系统多目标优化调度

建立综合能源系统优化调度模型并进行高效求解有利于可再生能源的开发利用,发掘综合能源系统降本增效的潜力。针对含光伏发电的综合能源系统,以系统■效率倒数最小和总运行成本最小为目标,结合电-热-冷综合需求响应模型和运行约束,构建综合能源系统多目标运行模型。针对模型中存在的非凸非线性项进行等价线性转化处理,将问题由多目标混合整数线性分式规划等价转换为多目标混合整数线性规划,进一步采用ε约束法将其转换为一系列单目标混合整数线性规划问题进行高效求解获得帕累托Pareto前沿,并采用TOPSIS法进行决策。算例仿真表明,所建立的含光伏发电的综合能源系统能提升系统运行灵活性,相比于单目标运行,计及■分析的综合能源系统多目标优化调度能够实现系统运行成本和效率的折衷。     

我国从俄罗斯引进天然气工程的多目标模糊决策分析

介绍了一种新的模糊多目标决策解法－隶属度离异决策法。应用该方法 以投资总额、总成本、还贷年限、销售气量等作为考核指标,对我国从俄罗斯引进 天然气工程方案进行了决策分析。实践表明,本文提出的隶属度离异决策法对于 解决两级多目标模糊决策问题具有很好的适用性。     

Exergetic sustainability evaluation and optimization of an irreversible Brayton cycle performance

Owing to the energy demands and global warming issue, employing more effective power cycles has become a responsibility. This paper presents a thermodynamical study of an irreversible Brayton cycle with the aim of optimizing the performance of the Brayton cycle. Moreover, four different schemes in the process of multi-objective optimization were suggested, and the outcomes of each scheme are assessed separately. The power output, the concepts of entropy generation, the energy, the exergy output, and the exergy efficiencies for the irreversible Brayton cycle are considered in the analysis. In the first scheme, in order to maximize the exergy output, the ecological function and the ecological coefficient of performance, a multi-objective optimization algorithm (MOEA) is used. In the second scheme, three objective functions including the exergetic performance criteria, the ecological coefficient of performance, and the ecological function are maximized at the same time by employing MOEA. In the third scenario, in order to maximize the exergy output, the exergetic performance criteria and the ecological coefficient of performance, a MOEA is performed. In the last scheme, three objective functions containing the exergetic performance criteria, the ecological coefficient of performance, and the exergy-based ecological function are maximized at the same time by employing multi-objective optimization algorithms. All the strategies are implemented via multi-objective evolutionary algorithms based on the NSGAII method. Finally, to govern the final outcome in each scheme, three well-known decision makers were employed.     

Flow pattern analysis and multi-objective optimization of helically corrugated tubes used in the intermediate heat exchanger for nuclear hydrogen production

Using the heat of the very-high-temperature gas-cooled reactor (VHTR) for large-scale and carbon-free hydrogen production provide an excellent option for a new sustainable energy economy. The intermediate heat exchanger (IHX) is a crucial component to realize this new hydrogen production mode. To supply sufficient heat for hydrogen production, the heat transfer of IHX needs to be enhanced. In this study, helically corrugated tubes (HCTs) were used to enhance the heat transfer of IHX. The flow pattern of HCTs was analyzed and a multi-objective optimization was then performed. First, the numerical experiment design with two objectives and three factors was conducted by Box-Behnken design in the response surface methodology (RSM). Second, the significant regression models for Nusselt number, friction factor, and performance evaluation criteria were obtained by the analysis of variance. Combining the analysis of turbulence kinetic energy and flow dead zone, a more comprehensive mechanism for the effect of corrugation parameters on flow and heat transfer was proposed. Finally, the fast-elitist non-dominated sorting genetic algorithm (NSGA Ⅱ) was adopted to conduct the multi-objective optimization of HCTs. The technique for order of preference by similarity to ideal solution (TOPSIS) was adopted for multi-objective decision-making. The CFD validation and comparison with RSM experimental design confirmed that the obtained Pareto optimal set and Pareto front can provide a comprehensive and effective design strategy for the IHX used for nuclear hydrogen production.     

Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm

There are various analyses for a solar system with the dish-Stirling technology. One of those analyses is the finite time thermodynamic analysis by which the total power of the system can be obtained by calculating the process time. In this study, the convection and radiation heat transfer losses from collector surface, the conduction heat transfer between hot and cold cylinders, and cold side heat exchanger have been considered. During this investigation, four objective functions have been optimized simultaneously, including power, efficiency, entropy, and economic factors. In addition to the four-objective optimization, three-objective, two-objective, and single-objective optimizations have been done on the dish-Stirling model. The algorithm of multi-objective particle swarm optimization (MOPSO) with post-expression of preferences is used for multi-objective optimizations while the branch and bound algorithm with pre-expression of preferences is used for single-objective and multi-objective optimizations. In the case of multi-objective optimizations with post-expression of preferences, Pareto optimal front are obtained, afterward by implementing the fuzzy, LINMAP, and TOPSIS decision making algorithms, the single optimum results can be achieved. The comparison of the results shows the benefits of MOPSO in optimizing dish Stirling finite time thermodynamic equations.     

车载清洁移动机械臂的时间-冲击最优轨迹规划

以对塔式热发电站定日镜表面清洁作业的车载清洁移动机械臂为研究对象,考虑到机械臂的清洁效率及运动过程中的震动和磨损问题,提出一种机械臂时间-冲击最优轨迹规划方法,优化其时间与冲击量,提高机械臂工作效率的同时延长使用寿命.利用关节空间的位置-时间序列,采用5次B样条函数在关节空间构造插值曲线,以机械臂关节的角速度、角加速度...     

A multi-objective model for optimizing hydrogen injected-high pressure natural gas pipeline networks

The paper develops a statistical model for optimizing the Hydrogen-injected Natural gas (H-NG) high-pressure pipeline network. Gas hydrodynamic principles are utilized to construct the pipeline and compressor station model. The model developed is implemented on a pipeline grid that is supposed to carry Hydrogen as an energy carrier in a natural gas-carrying pipeline. The paper aims to optimize different objectives using ant colony optimization (ACO). The first objective includes a single objective optimization problem that evaluates the maximum permissible hydrogen amounts blended with natural gas (NG) for a set of pipeline constraints. We also evaluated the variations in operational variables on injecting Hydrogen into the natural gas pipeline networks at varying fractions. The study further develops a multi-objective optimization model that includes bi-objective and tri-objective problems and is optimized using ACO. Traditional studies have focused on single-objective optimization with minimal bi-objective issues. In addition, none of the earlier research has shown the effect of introducing Hydrogen to the NG network using tri-objective function evaluations. The bi-objective and tri-objective functions help evaluate the effect of injecting Hydrogen on different operational parameters. The study further attempts to fill the gap by detailing the modelling equations implemented through a bi-objective and tri-objective function for the H-NG pipeline network and optimized through ACO. Pareto fronts that show the tradeoff between the different objectives for the multi-objective problem have been generated. The primary objective of the bi-objective and tri-objective optimization problems is maximizing hydrogen mole percent in natural gas. The other objective chosen is minimizing compressor fuel consumption and maximizing delivery pressure, throughput, and power delivered at the delivery station. The findings will serve as a roadmap for pipeline operators interested in repurposing natural gas pipeline networks to transport hydrogen and natural gas blend (H-NG) and seeking to reduce carbon intensity per unit of energy-delivered fuel.