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教学模型,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。     

装置级突发事故应急预案编制的思考与对策

结合石化企业装置生产特点,分析装置级突发事故应急预案编制中存在的不足和问题,并对应急预案编制的思路和处置对策进行了深入地探讨.     

河源市企业环境事故应急预案的评估与管理现状分析

结合应急预案编制相关要求及评估工作实际,分析了我市企业环境事故应急预案评估、备案及管理工作中存在的问题,并提出了建议和措施,以期为企业应急预案的评估和管理工作提供参考。     

重质油储罐固定式泡沫灭火系统设计优化研究

通过重质油储罐的实体火灭火试验,获得了氟蛋白泡沫液和水成膜泡沫液的供给强度与灭火时间对应关系数据,确定了每种泡沫液扑救重质油储罐火灾的最低泡沫供给强度;以10 000 m3固定顶重质油储罐为例进行了泡沫系统设计计算,将优化设计方案与原有设计方案进行了对比,主要变化是泡沫发生器数量从4只提高至6~8只,泡沫主管线管径从DN200提高至DN250,泡沫消防泵流量也相应提高。优化后的储罐泡沫灭火系统提高了泡沫灭火能力,提升了系统的可靠性,降低了储罐火灾风险。     

基于非点源溶解态氮负荷估算的率水流域土地利用结构优化研究

利用区域营养盐管理模型(ReNuMa)对率水流域2000~2010年的溶解态氮(DN)负荷进行了定量估算和来源解析.在率定期和验证期,径流和DN负荷模拟的Ens和R2都大于0.9,模型具备可靠的模拟能力.结果表明,率水流域的年均非点源DN负荷为1.11×103t·a-1,负荷强度为(0.75±0.22)t·km-2.在所有土地利用类型中,水田的DN负荷强度最大[28.60kg·(hm2·a)-1],林地的DN负荷强度最小[2.71 kg·(hm2·a)-1].农业生产用地(水田、谷物、经济作物、果园和茶园)对DN负荷的贡献最大,表明人类影响下的农业生产活动是流域非点源污染的最主要来源.基于污染负荷适量削减和农业经济产值最大化原则,开展了流域2015年土地利用结构优化分析,规划结果表明在土地利用结构最优情况下,经济收益的增长依然伴随着负荷的增加,但经济产值的增幅大于DN负荷的增幅.     

响应面法优化胡敏素对Cu2+的吸附及机理研究

采用Box-Behnken响应面优化实验设计对胡敏素吸附去除水中Cu~(2+)的过程进行了优化,设定吸附时间、吸附剂用量、pH、温度和Cu~(2+)初始浓度为5个影响因素,Cu~(2+)吸附率为响应值,建立了吸附率与上述因素之间的二次多项式模型,确定最佳吸附条件,对吸附过程的等温模型及吸附机理进行了研究.响应面分析表明,吸附剂用量、pH和Cu~(2+)初始浓度是显著因素.胡敏素对Cu~(2+)吸附的最佳条件为:吸附时间110 min、吸附剂用量2.4 g·L~(-1)、pH=5.4、温度25.0℃、Cu~(2+)初始浓度208 mg·L~(-1).在该条件下,测得胡敏素对Cu~(2+)的吸附率可达到80.78%,吸附符合Langmuir等温线方程.胡敏素表面疏松多孔,有利于其通过物理吸附方式吸附Cu~(2+),同时,胡敏素表面的羟基、羧基和羰基等活性基团可以与Cu~(2+)发生配位络合作用,Na+、Ca~(2+)、Mg~(2+)等与Cu~(2+)发生离子交换作用,从而发生化学吸附.研究结果表明,胡敏素作为一种绿色、高效、廉价的吸附剂,可应用于Cu~(2+)污染废水的治理.     

对环境评价中替代方案编写的几点看法

阐述了环境影响评价中有关替代方案应当包括的内容 ,并对替代方案编写的原则和方法做了说明 ,最后 ,列举厂址选择的替代方案的编写实例。     

“十五”期间解决我国工业用水问题的建议

本文针对我国工业用水存在的问题,从六个方面,提出了“十五”期间我国水资源可持续利用的对策。     

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.