基于EMD-MFOA-ELM的瓦斯涌出量时变序列预测研究

为准确分析工作面绝对瓦斯涌出量的非平稳特征,实现瓦斯涌出量的准确预测,基于经验模态分解(EMD)、修正的果蝇优化算法(MFOA)和极限学习机(ELM)基本原理,构建瓦斯涌出量的EMD-MFOA-ELM多尺度时变预测模型。通过EMD将瓦斯涌出量时变序列进行深层次分解,获得多尺度本征模态函数(IMF);采用MFOA-ELM对各IMF时变序列建立动态预测模型,等权叠加各预测值,得到模型最终预测结果。以晋煤某矿瓦斯涌出量监测时序样本为例进行研究分析,结果表明:EMD能充分挖掘出监测数据隐含信息,有效降低数据复杂度;该模型预测相对误差为0.024 3%~0.651 0%,平均值仅为0.252 6%,预测精度和泛化能力高于未经EMD分解模型,能很好地适用于非平稳时变序列预测。     

基于数据挖掘技术的事故隐患预警方法研究

为改良企业既有隐患排查治理方法,提高辨识效率,提升整改效果,应用并优化数据流滑动窗口模型、等价类变化算法、变化挖掘算法等数据挖掘技术,构建1种关联隐患预警方法,该方法以企业历史隐患数据为基础,通过挖掘实时隐患数据的关联规则,发布关联隐患的类型、存在可能性和变化模式等预警信息,指导企业进行针对性排查和整改。最后,以72家安全管理性质相似企业的28 957项隐患数据为例,验证该方法可行性。研究结果表明:该方法不但能挖掘关联隐患类型及其变化模式,而且预警准确率随置信度水平提升而不断提高。     

基于改进遗传-单纯形混合算法的危险气体泄漏溯源分析

当发生危险气体泄漏时,确定其泄漏位置和泄漏源强,是制定应急方案的基础和依据之一。当无法直接确定泄漏位置、测量泄漏源强时,就需要通过有限的几个监测点,反演出可能的泄漏位置和泄漏源强,现有方法存在收敛过慢、初值敏感、参数过多等问题。描述一种结合改进遗传算法和单纯形法的IGA-NM混合算法,可用于快速反算气体泄漏的位置和源强。IGA-NM混合算法既避免了GA的收敛过慢,又避免了NM初值敏感,兼顾了全局优化。与GA、NM相比,IGA-NM混合算法的计算速度更快,计算误差更小。最后,应用IGA-NM混合算法,基于WebGIS设计了一套计算气体泄漏源强和位置的计算机程序,简化了输入参数,使用方便,可适用于气体泄漏应急监测、大气污染源溯源反查等场合     

区域联防联控挥发性有机物(VOCs)最优减排方案研究

VOCs区域性联防联控治理是当前彻底治理VOCs污染的有效手段,为了实现VOCs区域性联防联控治理,建立了以减排总量最大、减排成本最低、VOCs减排限产产生的经济影响最小为目标的多目标最优减排模型,提出了基于自适应变异率的分解多目标进化算法（Decomposition Multi-objective Evolutionary Algorithm based on Adaptive Mutation Rate,简称MOEA/DAMA）.选取关中某城市群为研究对象,将VOCs的排放数据代入到最优减排模型中,运用MOEA/DAMA算法对其求解.根据最终的优化结果与MOEA/D初始算法和NSGA-Ⅱ优化算法的优化结果进行对比,结果表明MOEA/DAMA相较于NAGA-Ⅱ和MOEA/D的Pareto解的分布性更广、解的收敛性更高.在对Pareto解的分析上融入决策者的根据实际情况的权衡,计算出更合理的VOCs最优减排方案,为长效科学治理VOCs污染提供借鉴.     

不同品种水稻对Cu积累分配的差异

以21个水稻品种（包括常规稻、二系杂交稻、三系杂交稻）为材料,通过室内盆栽试验探讨了Cu在不同品种水稻根-茎-叶-籽粒（糙米）中的积累分配的差异。结果表明,供试水稻品种根、茎、叶、籽粒中Cu含量的均值分别是67.35、16.24、8.29、11.62mg·kg-1,多数水稻品种Cu的含量顺序是根茎籽粒（糙米）叶,但也有少部分水稻品种Cu的含量顺序是根籽粒（糙米）茎叶。各器官的Cu含量在不同品种间存在显著差异,其中以根和籽粒Cu含量的差异最大,茎次之,叶最小。常规稻根部Cu含量显著低于杂交稻,而叶中Cu含量显著高于杂交稻,但两者的茎和籽粒中Cu含量没有明显的差异。三系杂交稻根、茎、籽粒Cu含量显著高于二系杂交稻,而叶的Cu含量两者差异不显著。不同遗传背景的水稻各器官对Cu积累也存在不同程度的差异。相关分析结果表明,不同品种水稻相邻器官（根与茎,茎与叶）的Cu含量呈极显著正相关,相间器官（根与叶、籽粒,茎与籽粒）的Cu含量呈显著正相关,地上部各器官Cu的转运系数相互之间呈极显著的正相关。     

基于蛙跳算法优化的生态环境质量评价的韦伯-费希纳指数公式

为了建立简便、实用的生态环境质量评价模型,在构建生态环境评价指标体系的基础上,设定各指标的参照值和规范变换式,使规范变换后的不同指标同级标准的规范值差异不大。进而提出了一个对多项指标的规范值都适用的生态环境质量评价的韦伯-费希纳(W-F)指数公式。采用混合蛙跳算法对公式的参数进行优化,得出优化后对多项指标皆适用的生态环境质量评价的W-F指数公式。运用该公式对巢湖流域的生态环境质量进行评价,其结果与该流域生态环境质量实际情况基本相符,表明模型有一定的实用性。     

An Optimization Method for Estimating Constituent Mean Concentrations in Base Flow‐Dominated Flow

Pollutant coefficients have been widely used to assess runoff nonpoint source pollution from individual land uses (e.g., agricultural, residential) of a watershed. Pollutant coefficients, known as event mean concentrations (EMCs), were developed by the U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) to serve as a national measure for characterizing pollutant loading in a receiving water body. The term “baseflow pollutant coefficient (BPC)” is used in this study as a surrogate for EMC to describe mean concentration of pollutants in base flow‐dominated flow. A method for characterizing base flow quantity and quality for different land uses was explored using inverse modeling with two optimization techniques (a least square method and a genetic algorithm [GA] optimization), land use information, and streamflow quantity and quality data. The inverse model was formulated as a constrained minimization problem and demonstrated with data for 15 watersheds in Indiana. Results showed that estimated pollutant coefficients are comparable to the published literature. This indicates that the proposed method has the potential to effectively estimate constituent mean concentrations for pollutant load determination in gauged and ungauged watersheds, albeit more analysis with larger and more robust datasets is desirable to further refine and validate the accuracy of the approach.     

微生物处理技术在有机污染物降解中的应用

微生物处理有机污染物是一种费用低,效果好,无二次污染且操作简单的处理方法。综述了国内外几种微生物处理有机污染物技术的研究进展。     

基于SVR的船舶溢油事故预测

船舶溢油事故的预测,是确定所需应急资源多少的重要依据;但船舶溢油事故具有小样本性,传统预测方法不再适用。针对船舶溢油事故的小样本性,该文选择SVR模型对其进行预测;SVR算法的关键是核函数的选择及自由参数的确定,针对上述问题该文分别采用交叉验证法、遗传算法;并在matlab软件上建立SVR模型,最后将该模型应用于上海港水域船舶溢油事故的预测。     

Developing a fuzzy linear programming model for locating recovery facility in a closed loop supply chain

In this research a closed loop supply chain is designed which incorporates reverse logistics and forward logistic system simultaneously. In the design of reverse logistic system, recovery options are embedded in traditional supply chain for treating returned products. The recovery system includes collection centres, remanufacturing plants and disposal centres. Since the product return is supply driven, there is an uncertainty about it. In the proposed configuration for closed loop supply chain, the optimised configuration for supply chain in terms of locating recovery plants is developed. Accordingly, a fuzzy mixed integer linear programming model develops to deal with the uncertainty of returning products by customers. A general-purpose solver (LINGO 8.0) and a Meta heuristic approach (genetics algorithm) are implemented to solve the proposed model. The answers are compared by defining indexes and then the optimal answer, configuration and variables are identified. This solution will suggest a new design of supply chain network in which waste of materials is minimised and the new raw materials are necessary only when the used products may not be recovered by recovery options.