洱海地面水环境监测优化布点研究

根据１９８７年以后五年洱海水质监测数据的数理统计分析，选择污染指数大的ｐＨ、ＣＯＤ＿（Ｍｎ）、ＢＯＤ＿５、Ｔ－Ｎ、Ｔ－Ｐ进行单项方差分析，进而再进行灰色关联聚类分析和年际相关分析，确知洱海三个水质监测断面可归于一类。考虑水下地形、湖流、环湖工农业布局，水质监测点优化为两个即可保证其准确性、可控性和代表性。     

三峡库区小城镇水环境与工业产业结构优化模型——以万州区分水镇为例

以长江上游三峡库区腹部的重庆市万州区分水镇为研究对象,根据2005年分水镇工业各行业的结构特点及各行业对水环境的污染特点,建立水环境--经济工业结构多目标优化模型,选取工业生产总值最大和COD排放量最小作为优化目标,量化分水镇2010年工业结构,选择出符合当地经济发展"十一五"规划目标和水环境保护目标的工业结构优化方案,提出具体的工业产业发展建议,达到减少工业污染物对境内长江次级河流瀼渡河的污染,有效地改善瀼渡河水质状况,同时保证分水镇社会、经济、水环境的可持续发展的目的.文章同时也对三峡库区小城镇的工业产业结构优化调整,获得既符合经济发展目标又满足水环境保护要求的合理的工业产业结构,提供可借鉴的思路和方法.     

炼油厂"三泥"离心脱水处理研究

通过对目前炼油厂“三泥”性状的调查，利用已有污泥处理设施进行了炼油厂“三泥”离心分离的现场试验，并分析了影响“三泥”离心分离处理工艺的主要因素，发现了离心机转速和絮凝剂投加量对“三泥”离心分离的有关规律和最佳操作条件，在此基础上，提出了适合炼油厂“三泥”处理系统的优化方案。     

COMPARATIVE STUDY OF OPTIMIZATION TECHNIQUES FOR IRRIGATION PROJECT PLANNING1

ABSTRACT: This study presents three optimization techniques for on‐farm irrigation scheduling in irrigation project planning: namely the genetic algorithm, simulated annealing and iterative improvement methods. The three techniques are applied to planning a 394.6 ha irrigation project in the town of Delta, Utah, for optimizing economic profits, simulating water demand, and estimating the crop area percentages with specific water supply and planted area constraints. The comparative optimization results for the 394.6 ha irrigated project from the genetic algorithm, simulated annealing, and iterative improvement methods are as follows: (1) the seasonal maximum net benefits are $113,826, $111,494, and $105,444 per season, respectively; and (2) the seasonal water demands are 3.03*10³ m³, 3.0*10³ m³, and 2.92*10³ m³ per season, respectively. This study also determined the most suitable four parameters of the genetic algorithm method for the Delta irrigated project to be: (1) the number of generations equals 800, (2) population size equals 50, (3) probability of crossover equals 0.6, and (4) probability of mutation equals 0.02. Meanwhile, the most suitable three parameters of simulated annealing method for the Delta irrigated project are: (1) initial temperature equals 1,000, (2) number of moves equal 90, and (3) cooling rate equals 0.95.     

OPTIMIZATION MODELING OF A NEW FACILITY FOR THE CALIFORNIA STATE WATER PROJECT1

ABSTRACT: A non-linear optimization model is applied to the California State Water Project (SWP) and portions of the Central Valley Project (CVP). The model accounts for the major hydrologic, regulatory, and operational features of both projects. The model maximizes long-term SWP yields over a 70-year period, using a quarterly time step. The potential for increased yield associated with a proposed facility improvement is evaluated with the model. The proposed facility is an extension of the Folsom-South Canal, which would allow water to be conveyed from the American River below Folsom Reservoir into New Melones Reservoir on the Stanislaus River or into the California Aqueduct. Model results indicate that extension of the Folsom-South Canal has the potential to increase SWP yields by 13 percent.     

基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

为合理利用多智能体算法解决城市扩张动态模拟问题,基于地理学理论和社会学规律对粒子群算法进行有针对性的改进,提出分段式粒子群算法(SPSO),并结合元胞自动机模拟复杂时空过程的能力,构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据,挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSOCA及NULL模型结果得:SPSO-CA总精度86.3%,Kappa系数为0.792,Moran’s I为0.078,PSO-CA总精度83.6%,Kappa系数为0.755,Moran’s I为0.054,NULL模型总精度81.9%,Kappa系数为0.741,真实的Moran’s I为0.072。这表明无论是总精度还是空间一致性,SPSO-CA都优于PSO-CA和NULL模型,即用SPSO-CA模拟城市扩张是可行的。     

湖北省土地利用减碳增效系统仿真及结构优化研究

土地利用变化是引起碳排放的重要原因之一,土地经济效益是利用土地的目标之一,如何通过优化土地利用结构实现土地利用减碳增效是值得研究的重要问题。基于系统结构与功能相互作用的视角,梳理复杂系统内变量间的反馈关系,运用系统动力学(SD)进行建模,将约束条件纳入到多目标规划(MOP)中,实现MOP与SD模型整合,进行系统仿真并得出优化后2020年湖北省土地利用结构。结果显示,利用SD-MOP模型能够实现减碳增效目标下土地利用结构优化,与2008年真实值相比,耕地、林地、牧草地及建设用地分别增加了0.33×10~4、30.17×10~4、0.08×10~4和16.37×10~4 hm~2,其他农用地及未利用地分别减少7.23×10~4、33.15×10~4 hm~2;与无约束SD单模型仿真相比,土地利用碳排放量减少了58×10~4 t,经济效益年增长率维持在3.58%,优化方案具有可行性。SD-MOP模型优化的土地利用结构符合区域可持续发展要求,兼顾了土地利用碳减排和经济效益增长的双重目标,能为区域土地资源优化配置提供参考。     

Comparative evaluation of sizing of metal hydride (MH) hydrogen storage tank filled with different alloys

This study presents a comparative analysis of sizing of metal hydride tank filled with different alloys. Alloys include solid solutions and intermetallic compounds of the generic families AB₅, AB₂, AB, A₂B. The effects of the different alloys on the sizing of metal hydride hydrogen storage tanks are complicated and depend on many factors. In this paper, a thermoeconomic optimization analysis with a simple algebraic formula was presented for the estimation of optimum metal hydride tank surface area for heat transfer enhancement. The optimum area of the metal hydride tank filled with commercially available different alloys (LaN₅, Ti_0,98Zr_0,02V_0,43Fe_0,09Cr_0,05Mn_1,5, TiFe, Mg₂NiH₄) was evaluated and compared by the developed method. The optimum net savings and the value of payback were determined for four alloys. It is found that mathematical model can be employed for the determination of optimum metal hydride tank design and increasing net savings according to alloy types. The optimum areas of the tanks filled with four alloys (LaN₅, Ti_0,98Zr_0,02V_0,43Fe_0,09Cr_0,05Mn_1,5, TiFe, Mg₂NiH₄) were calculated as 0.136, 0.130, 0.133, and 0.173 m², respectively. The optimum net savings for tanks filled with four alloys (LaN₅, Ti_0,98Zr_0,02V_0,43Fe_0,09Cr_0,05Mn_1,5, TiFe, Mg₂NiH₄) are about 461.0, 409.3, 419.6, and 979.6 $ and the values of payback are about 1.98, 2.1, 2.17, and 1.37 years, respectively. Excessive area of the metal hydride tank would not be as economical as the optimum tank area. Thermal management of metal hydride tank must be designed for optimum points calculated at which maximum savings occur.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Optimization-Based Atmospheric Plume Source Identification

This work applies optimization and an Eulerian inversion approach presented by Bagtzoglou and Baun in 2005 in order to reconstruct contaminant plume time histories and to identify the likely source of atmospheric contamination using data from a real test site for the first time. Present-day distribution of an atmospheric contaminant plume as well as data points reflecting the plume history allow the reconstruction and provide the plume velocity, distribution, and probable source. The method was tested to a hypothetical case and with data from the Forest Atmosphere Transfer and Storage (FACTS) experiment in the Duke experimental forest site. In the scenarios presented herein, as well as in numerous cases tested for verification purposes, the model conserved mass, successfully located the peak of the plume, and managed to capture the motion of the plume well but underestimated the contaminant peak.