风化煤腐植酸增效尿素红外光谱分析

采用碱性混合活化剂处理风化煤,研制腐植酸添加剂,按不同比例添加到尿素熔融液中,采用模拟喷浆造粒的方法,制备3类增效尿素。通过风化煤与添加剂、尿素与增效尿素红外光谱特征对比发现：风化煤经过活化后,碳单键数量降低,碳链缩短,活性腐植酸HA类提取物出现活性官能团,FR类除单键数量减少和碳链缩短外,生成部分胺、酰胺结构;增效剂与尿素在反应过程中,出现了三键和累积双键破坏、稳定性复合物形成、碳链缩短、双键结构增加等变化,但不同类型增效剂对产品红外光谱特征影响存在差异。     

基于MCOA布置矿井瓦斯传感器

为了预防矿井瓦斯事故,确定增设瓦斯传感器的数量和位置。通过建立监测有效 等级来确定增设瓦斯传感器的数量,然后通过分析多个猴群同步并行搜索、简易猴群算 法的初步搜索和基本和声算法的二次搜索等方法,设计出猴群优化算法的计算流程,再 根据瓦斯传感器布置的目标函数确定增设瓦斯传感器位置。以大隆矿为例进行了现场工 业试验,结果表明,增设瓦斯传感器的数量与监测有效等级之间成正比例关系,但并非 随着监测有效等级的提高而持续增加。     

改进的GA-BP神经网络在矿井突水水源判别中的应用

矿井突水水源的判别是制定防治水措施的重要环节。通过对某矿含水层水化学特 性的相关性分析,将PCA算法、K折交叉验证算法嵌入GA-BP神经网络,提出了一种新的 GA-BP神经网络,将其应用于实例分析中,并与传统的方法进行比较。结果表明:针对 水化学特性相近的含水层,PCA算法能够排除样本中的冗余信息,降低样本指标维度, 简化BP神经网络结构;K折交叉验证算法能够提高GA算法对BP神经网络权值的寻优质量 ,使GA算法的进化方向更具合理性;二者的引入大大优化了传统GA-BP神经网络性能, 其判别精度更高、适用性更强、结果更可靠,在矿井突水水源判别方面具有很好的应用 前景。     

Land-use planning via enhanced multi-objective evolutionary algorithms: optimizing the land value of major Greenfield initiatives

The successful implementation of major development initiatives relies on the sound allocation of land uses against critical design criteria and constraints. The discovery of optimum development plans introduces severe complexities in formulating and solving the underlying multi-objective optimization problem. Moreover, in the presence of conflicting planning criteria decision-makers should be provided with a set of alterative-yet-optimum solutions that uniformly cover the spectrum of feasible maps. The introduction of sophisticated optimization algorithms addresses this challenge by pursuing a complete approximation of the Pareto front containing all prominent spatial allocations. This study demonstrates the effectiveness of a new evolutionary algorithm (UDT-MOEA) against the results of an established multi-objective genetic algorithm (NSGA-II) when applied on a major greenfield initiative against the optimum location(s), size and shape of three new land uses. Each algorithm performs best in different areas of the feasible objective space, providing planning alternatives with distinct characteristics.     

Positive relationships between phenol oxidase activity and extractable phenolics in estuarine soils

Decomposition of recalcitrant materials such as phenolics is known to play a pivotal role in organic matter decomposition and nutrient cycling in estuaries. The specific goals of this study were to determine temporal and spatial variations of phenol oxidase and phenolics in estuarine soils, and to elucidate controlling factors for phenol oxidase activity. To achieve these goals, phenol oxidase activity and phenolic content were measured in soils developed along the side of an estuary in the Han River, Korea. Soil samples were collected in three locations with different vegetation: mud flats, Zizania-dominated soils, and Salix-dominated soils. Monthly measurements were also made in a Zizania-dominated site over a year period. Phenol oxidase activity varied between 0.00 and 0.28 diqc min^?1 g^?1, whilst phenolic content ranged from 0.0–10.5 μg g^?1. A correlation analysis revealed that phenol oxidase activity exhibited positive correlations with phenolic content in both seasonal and spatial data. The same relationship was found when the data were analysed separately for each site. Unlike peatlands or upland forest soils where negative correlations were often found between phenol oxidase activity and phenolics, substrate induction appears to account for the positive correlation in the present study.     

Carboxymethyl‐β‐cyclodextrin influence on the solubility and toxicity of substituted indole compounds

The solubilization of eight selected substituted indole compounds (SICs) by carboxymethyl‐β‐cyclodextrin (CMCD) in water is reported. The results show that their solubility was well improved in CMCD aqueous solution, and the stoichiometry of inclusion complex of the studied SICs with CMCD was 1: 1. The acute toxicity to Photobacterium phosphoreum of the investigated SICs in CMCD aqueous solutions was also investigated and was compared with that in water. The results show that the toxicity of five studied SICs decreased while the toxicity of the rest three SICs increased in CMCD aqueous solutions, but neither the increase nor the decrease was significant.     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.     

矿井通风网络的社团结构及灾害控制研究

为研究矿井通风网络的社团结构特征和有效控制灾害在通风网络中的传播范围,根据通风网络的连接特点对传统的GN算法进行改进,以淮南某矿井的通风网络结构为例进行说明,发现矿井通风网络具有较强的社团结构特征;引入强弱社团结构和外内连接边数比后,社团结构的划分更符合实际。根据矿井通风网络社团间的连接关系,就会找到灾害传播的关键分支与节点并加以控制,降低灾害对相邻社团危害程度,控制灾害的蔓延范围。     

基于蚁群神经网络的煤矿瓦斯预测模型

为了提高煤矿瓦斯涌出量的预测精度和预测速度,用蚁群算法和神经网络相结合的方法进行预测模型设计。选择瓦斯涌出的重要影响因素,建立其神经网络的预测模型。以网络的均方误差为目标函数,通过蚁群算法的迭代运算,实现BP网络的权值优化,并用优化好的BP网络进行瓦斯涌出预测。仿真结果表明,该方法具有较高的拟合预测精度。     

基于蚁群算法的石化项目应急路径规划研究

蚁群算法是一种智能仿生优化算法,实际运用仿真模拟时具有良好的寻优性和高效性。将蚁群算法与石化项目的路径规划结合,借鉴蚁群算法解决旅行商问题TSP的思路并将之运用于应急路径规划,利用MATLAB仿真软件,以某石化项目的应急路径规划为实例,较好地实现了有关应急路径模拟图,可为有关石化项目的安全规划提供借鉴和参考。