Fast increasing of surface ozone concentrations in Pearl River Delta characterized by a regional air quality monitoring network during 2006-2011

Based on the observation by a Regional Air Quality Monitoring Network including 16 monitoring stations, temporal and spatial variations of ozone(O3), NO2and total oxidant(Ox) were analyzed by both linear regression and cluster analysis. A fast increase of regional O3concentrations of 0.86 ppbV/yr was found for the annual averaged values from 2006 to 2011 in Guangdong, China. Such fast O3increase is accompanied by a correspondingly fast NOx reduction as indicated by a fast NO2 reduction rate of 0.61 ppbV/yr. Based on a cluster analysis, the monitoring stations were classified into two major categories – rural stations(non-urban) and suburban/urban stations. The O3concentrations at rural stations were relatively conserved while those at suburban/urban stations showed a fast increase rate of 2.0 ppbV/yr accompanied by a NO2 reduction rate of 1.2 ppbV/yr. Moreover, a rapid increase of the averaged O3 concentrations in springtime(13%/yr referred to 2006 level) was observed, which may result from the increase of solar duration, reduction of precipitation in Guangdong and transport from Eastern Central China. Application of smog production algorithm showed that the photochemical O3production is mainly volatile organic compounds(VOC)-controlled. However, the photochemical O3production is sensitive to both NOx and VOC for O3pollution episode. Accordingly, it is expected that a combined NOx and VOC reduction will be helpful for the reduction of the O3 pollution episodes in Pearl River Delta while stringent VOC emission control is in general required for the regional O3 pollution control.     

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.     

基于FA-BAS-ELM的海洋油气管道外腐蚀速率预测

为提高海洋油气管道外腐蚀速率预测的精度和效率,建立基于因子分析(FA)和天牛须搜索算法(BAS)的极限学习机(ELM)腐蚀速率预测模型。利用FA对影响因素数据集进行降维处理,确定预测模型的输入变量;建立ELM预测模型,并采用BAS对ELM模型的参数进行优化,避免参数取值随机性对模型预测性能的影响;以实海挂片试验为例,通过建模仿真评价模型的预测性能,并与其他模型进行对比分析。结果表明：FA-BAS-ELM预测模型的平均绝对误差(MAPE)仅为1.92%,决定系数R²高达0.994 9,相比于其他模型,该模型具有更优的预测性能。     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

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.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.