基于多目标决策的室内空气质量评价模型

在室内空气质量各单项评价指标的基础上，应用运筹学的决策理论，建立室内空气质量评价模型。并通过实例应用此模型对室内空气品质进行了评价研究，取得了满意的结果。与其他评价方法相比，该评价模型结构严谨、计算简便、结果合理、分辨率高。为室内空气质量评价提供了一种简单而适用的评价方法。     

安全投入与事故直接经济损失的问题研究

通过对安全投入及事故直接经济损失的分析研究,利用最小二乘法对安全投入比例(安全投入占GDP的比例)与事故直接经济损失之间的关系进行了回归分析,确定了其对应的函数关系.结果表明,安全投入比例与事故直接经济损失之间呈较明显的负指数关系.探讨了安全投入存在的主要问题,提出了几点关于加强安全投入的建议及措施.     

不同塔型交流输电线路的电磁环境分析

选择500 kV同杆双回紧凑型线路、500 kV同杆双回常规线路、500 kV单回紧凑型线路、500 kV单回常规线路、220 kV同杆双回线路和220 kV/110 kV同杆4回线路进行不同塔型交流线路的电磁环境测量.通过测试和计算分析,给出了被测试线路的电磁环境水平.     

基于优势隐含周期分析的气象灾害预报

建立了优势因子和优势隐含周期的概念,并提出了多因子逐步回归周期分析的新方法。该方法既能增长预报期,又能充分利用物理因子的重要信息,同时在计算过程中还能挑选出起显著作用的优势因子和优势隐含周期,对预报气象要素的变化趋势效果较好,有一定的实用价值,是气象灾害预报的一条比较有效的途径。     

Water Use by Thermoelectric Power Plants in the United States1

Abstract: Thermoelectric power generation is responsible for the largest annual volume of water withdrawals in the United States although it is only a distant third after irrigation and industrial sectors in consumptive use. The substantial water withdrawals by thermoelectric power plants can have significant impacts on local surface and ground water sources, especially in arid regions. However, there are few studies of the determinants of water use in thermoelectric generation. Analysis of thermoelectric water use data in existing steam thermoelectric power plants shows that there is wide variability in unitary thermoelectric water use (in cubic decimeters per 1 kWh) within and among different types of cooling systems. Multiple‐regression models of unit thermoelectric water use were developed to identify significant determinants of unit thermoelectric water use. The high variability of unit usage rates indicates that there is a significant potential for water conservation in existing thermoelectric power plants.     

Integrated model for renewable energy planning in Turkey

All economic sectors are associated with energy use; therefore, government organizations aim to supply sustainable energy for human needs and economic growth. In particular, increased environmental concerns of the public in Turkey have impacted policymaking for renewable energy (RE) management in Turkey. The primary objective for RE sources of the Turkish Ministry of Energy is to ensure that 30% of the share of electricity production is from RE resources in 2023. In this paper, the integrated multi-objective, multi-period linear programming model is presented to determine effective allocation of RE supply for seven different geographical regions in Turkey for the period of 2017 to 2024. The integrated model consists of two different stages. The first stage involves qualitative evaluations of RE sources for seven geographical regions. Analytical Hierarchy Process (AHP) is applied to determine criteria priorities and overall ratings of geographical regions across determined criteria for RE sources are computed. The second stage of the integrated model consists of a multi-objective, multi-period linear programming model. The proposed multi-objective linear programming model is coded in MPL (Mathematical Programming Language) and solved using the GUROBI 5.1.0 solver. The output of the integrated model presents the total supply amount of RE sources for geographical regions in planning period. The ε-constraints method is applied to compute the total supply amount of RE from geographical regions for the period of 2017 to 2024. In this study, a systematic decision-making model is generated to allocate renewable energy sources to the geographical regions. The presented model integrates qualitative evaluations and quantitative parameters of different geographical regions to determine the optimal supply amount of RE. The obtained results are consistent with the potential quantities of RE alternatives in geographical regions, regional specifications, and social requirements.     

A DYNAMIC MODEL FOR WATER AND RELATED LAND RESOURCE PLANNING1

ABSTRACT. Planning an optimal system of activities for generating economic goods and services within an existing natural resource capacity is a difficult problem to solve. A mathematical programming model with the capacity to check multiple resource demand and supply compatibility over many time periods was developed for the solution to this type of problem. The characteristics of natural resource supply and the demand of activities were utilized to reduce the number of time periods and to minimize the loss of the dynamic reality of the problem. Reduction in the number of time periods extended the capability of the model to the solution of complex resource planning problems without oversimplification.     

THE IMPACT OF HIGH INTEREST RATES ON OPTIMUM MULTIPLE OBJECTIVE DESIGN OF SURFACE RUNOFF URBAN DRAINAGE SYSTEMS1

ABSTRACT. This research examines the impact of high rates of interest upon the least cost system design for urban drainage systems when water quality is a critical parameter. Results of examination of twelve alternative system designs in a case study watershed indicate the least cost study design is highly sensitive to the rate of interest, but not sensitive to the water quality parameters. When the high rates of interest currently prevalent are introduced into the model those systems which contain open channel collection components are selected as the least cost system. At low rates of interest pipeline collection components are selected as the least cost system. Holding pond components of system design are cost effective at several levels of water quality. They are neutral to the rate of interest so they are incorporated in least cost systems at all the levels of interest rates. The results of the study indicate that at the current high rates of interest open channel collection systems and holding ponds are cost effective system components to achieve selected levels of water quality in urban drainage system design.     

A MULTIOBJECTIVE MODEL FOR REGIONAL WATER QUALITY MANAGEMENT1

ABSTRACT: Linear programming is the simplest of all the optimization techniques used in regional water quality management studies; but the technique can optimize only one goal. When there are multiple goals with the same or different priorities, goal programming is a useful decisionmaking tool. This paper illustrates the application of goal programming to a regional water quality management problem where the following two goals are considered: (1) minimize the total cost of waste treatment, and (2) maintain the water quality goals (dissolved oxygen) close to the minimum level stated in the stream standards.     

THE EFFECT OF DATA INDEPENDENCE IN MODEL CALIBRATION AND MODEL TESTING1

ABSTRACT: With the increased use of models in hydrologic design, there is an immediate need for a comprehensive comparison of hydrologic models, especially those intended for use at ungaged locations (i.e., where measured data are either not available or inadequate for model calibration). But some past comparisons of hydrologic models have used the same data base for both calibration and testing of the different models or implied that the results of model calibration are indicative of the accuracy at ungaged locations. This practice was examined using both the regression equation approach to peak discharge estimation and a unit hydrograph model that was intended for use in urban areas. The results suggested that the lack of data independence in the calibration and testing of regression equations may lead to both biased results and misleading statements about prediction accuracy. Additionally, although split-sample testing is recognized as desirable, the split-samples should be selected using a systematic-random sampling scheme, rather than random sampling, because random sampling with small samples may lead to a testing sample that is not representative of the population. A systematic-random sampling technique should lead to more valid conclusions about model reliability. For models like a unit hydrograph model, which are more complex and for which calibration is a more involved process, data independence is not as critical because the data fitting error variation is not as dominant as the error variation due to the calibration process and the inability of the model structure to conform with data variability.