电气柜疲劳强度校核及结构优化

针对多孔闭孔型发泡硅橡胶D型空心密封条的超弹性特性,通过试验找出了密封条回弹力与压缩量之间的关系,提出了密封条满足IP X5的判别方程式,同时对方程式进行了试验验证,最后对密封条进行了阿累尼乌斯图寿命推算,为变流器柜门IP防护设计提供了重要参考。     

B-P网络应用于地下水水质监测点优选

以地下水水质评价分级标准作为训练样本,构造B-P网络模型对其进行训练,用训练好的B-P网络对某地的地下水水质监测点进行评判、优选。并与其它方法的结果进行比较,结果表明,B-P网络用于环境测点优选不仅原理直观,而且具有较好的客观性和实用性。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

浅析总量监测中"生产周期"的确定及频次优化

根据排污周期确定加密监测采样频次,用实例说明有规律排污过程的生产特点,提出固定频次法进行监测频次优化的应用建议。     

湍球塔内气流分布优化的数值计算及试验

通过对湍球塔 (TCA)内三维流场的数值计算 ,找到了较佳的结构参数 ,在此基础上进行的试验表明 ,计算结果与试验结果相吻合。计算程序可为 TCA的设计及保证其长期安全经济运行提供理论依据。     

低碳城市理念下的郑州市绿地系统优化研究

低碳城市是一种实现低碳经济、进行低碳生产与消费、形成低碳生活、建设良性与可持续发展的能源生态体系的城市模式。通过调控城市绿地系统、优化城市开放空间,有利于低碳城市理念下的生态城市建设,实现城市的可持续发展。首先对低碳城市建设与城市绿地系统优化的关系进行了分析;其次对郑州城市绿地系统建设存在的主要问题进行了探讨,借助AreGIS9．3软件分析了郑州市绿地服务区的影响范围;最后基于城市绿地系统优化原则提出四方面的优化建议——注重城市的生态设计;优化绿地系统的空间布局;实现“点”、“线”、“面”的有机结合;实施适度的绿地容量限制;发展立体绿化与推广绿色建筑材料的使用。     

基于排队论的城市垃圾箱最佳间距研究

针对城市垃圾箱最佳间距的研究不够精细的现状,本文通过建立排队论模型得到了可供不同城市参考的城市垃圾箱最佳间距参数表。通过对部分城市街道的垃圾箱摆放间距情况进行实地调查,运用层次分析法将影响垃圾箱间距分布的多重因素转化为单因素;建立了基于指数分布的数学模型,利用排队论分析单因素状态下的垃圾箱间距分布,得到了求解城市垃圾箱最佳间距的一般方法。     

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.     

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.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.