地质统计学反演在薄煤层预测中的应用

曙光煤矿煤层多为薄煤层,利用地震数据常规反演技术预测开采区域煤层厚度的分辨能力有限,远低于煤层开采的需要。引入基于MCMC算法的地质统计学反演技术对曙光煤矿采区内薄煤层进行厚度预测。预测结果与常规解释结果进行对比,并利用已有钻孔资料进行验证,发现该方法的分辨率可达到1 m,对煤层厚度、煤中夹矸厚度的预测误差分别控制在0.5、0.1 m以内。有效解决了曙光煤矿薄煤层预测问题。     

基于RBF神经网络的露天矿爆破效果预测研究

露天矿爆破是一个受诸多因素共同影响的系统工程,是露天开采的重要环节之一,其爆破效果的优劣直接影响后续工序的完成。提高爆破技术水平和爆破质量,对矿山安全和生产具有重要的意义。本文通过随机森林选择影响爆破效果的主要参数,结合模糊评价确定爆破综合效果,建立了RBF神经网络爆破效果预测模型。将该模型应用于矿山爆破效果预测中,并将爆破现场实测的11组数据作为模型训练样本,另外5组现场数据作为预测样本进行测试,通过与BP神经网络比较,发现RBF神经网络的预测性能更为优越,可广泛应用于现场实践中。     

遗传算法和模矢法结合的概率积分法参数反演方法

遗传算法在反演概率积分法预计参数时从参数取值范围内的串集开始搜索,并使用弹性策略来维持群体的多样性,使得算法可以跨过局部收敛的障碍,向全局最优解方向进化;但这种概率化的寻优算法存在局部探索能力差、结果不稳定的缺陷,只能获得问题的近似最优解。模矢法是一种降梯度算法,算法局部探索能力强、收敛快;但这种算法对初值选取敏感,初值选择不当易陷入局部极值。本文提出并实现了一种模矢法与遗传算法结合的组合算法:先使用遗传算法求得参数的全局近似最优解,然后将近似最优解作为探索初值,使用模矢法获得参数的稳定、精确最优解。研究结果表明:组合算法反演概率积分法预计参数的精确度高、收敛快、稳定性好,综合性能较遗传算法和模矢法有明显优势。     

我国建筑石料用灰岩采矿权成交价变化分析

砂石资源采矿权出让收益是地方财政收入的重要来源。近年来,砂石资源供应紧张价格大幅上涨的背景下,本文以典型的砂石资源建筑石料用灰岩为例,分析了全国及部分典型省份建筑石料用灰岩采矿权成交价的变化特征,主要结论是2011～2018年我国建筑石料用灰岩采矿权平均成交单价与砂石市场供应价格均呈上升趋势但前者涨幅远高于后者,并且采矿权成交单价的区间分布结构年度变化差异也较大,不同区域平均成交单价年度变化规律分异明显,贵州、甘肃两省采矿权成交单价相对较低且变化幅度相对较小,山西、吉林、湖北、广西4省采矿权成交单价相对较高且变化幅度相对较大。最后,从平抑砂石价格过快上涨、加强矿业权市场建设、促进砂石资源区域供需平衡、维护良好砂石市场价格秩序的角度提出了有关政策建议。     

基于未确知测度理论的采矿方法优选

为选择适合贵州某金矿的最佳采矿方法,以解决该矿开采难度大与开采成本高的问题,根据矿山实际地质条件,初选出了适用于该矿体的采矿方法。将数值模拟与未确知测度理论相结合,利用数值模拟分析三种备选采矿方法的安全性,同时作为优选模型的指标。结合矿山实践经验,优选出采场生产能力等11项影响采矿方法选择的指标,利用信息熵计算各指标的客观权重,引入置信度识别准则进行评价,同时按照优越度进行排序,构建基于未确知测度理论的采矿方法优选模型。通过综合比较分析,最终确定最佳的采矿方法即下向中深孔落矿嗣后充填法,该方法可以有效解决目前开采遇到的问题,扩大矿山生产能力,降低采矿成本,提高矿山经济效益。     

基于狼群算法的概率积分法模型参数反演方法研究

概率积分法模型属于典型的多元复杂非线性函数,且部分参数存在相关性,使得概率积分法模型参数反演一直是开采沉陷数据处理的热点问题和难点问题。WPA(狼群算法)作为一种新群体智能算法,已在多维背包求解问题、水电站水库优化调度以及旅行商等复杂非线性最优化问题中得到成功应用,但在开采沉陷数据处理领域尚未见文献报道。鉴于此,本文首次将WPA引入概率积分法模型参数反演中,构建了基于WPA的概率积分法模型参数反演方法。研究结果表明:WPA反演概率积分法模型参数具有较高的准确度、较好的精度、良好的稳健性,且满足工程应用标准。本文研究成果对概率积分法模型参数精准反演具有重要参考价值。     

State of charge estimation method based on the extended Kalman filter algorithm with consideration of time-varying battery parameters

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, time-invariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with time-varying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with time-varying battery parameters yields higher accuracy.     

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Power losses cause the underutilization of distributed generation (DG) units in addition to the cost increasing in microgrid. Minimizing these losses has been focused in many papers. Using energy storage system (ESS) is a crucial solution for loss reduction. ESS can balance the power exchange in on-peak times where its location and size optimization can improve the microgrid efficiency and reduce the loss cost significantly. Moreover, to ensure the power quality by improving the voltage profile, capacitor bank can be installed optimally on some buses. Optimization of size and location of the capacitor bank can enhance the reactive power that is leading to power loss reduction. In other words, the capacitor bank is applied to compensate the total reactive power and consequently, the current is reduced that results in power loss reduction. In this article, the problem is defined as the optimum location and size of ESS and capacitor bank in the microgrid. Due to the complexity of the problem in many options for selecting the buses to implement these elements (ESS and capacitor bank), robust approach using the particle swarm optimization algorithm and general algebraic modeling system are applied for optimization process. In addition, the uncertainty of renewable DGs such as photovoltaic and wind turbine is modeled by probability density functions and Monte-Carlo is used for selecting more probable cases in optimization processes. The results show the loss cost reduction and improvement in voltage and power profile with less fluctuations and more stability.     

基于遗传算法的汽油机朗肯循环余热回收系统的优化

为了更加高效利用汽油机排气余热,分析了某款汽油机排气余热回收潜力,建立了基于蒸发器和活塞式膨胀机的汽油机-朗肯循环联合余热回收系统模型。利用遗传算法,同时考虑膨胀机输出功、排气利用率、蒸发器效率和膨胀机绝热效率,以膨胀机输出功和系统总效率为优化目标,以蒸发压力和膨胀机转速为优化变量,对汽油机4个工况下朗肯循环系统的最佳运行参数进行了研究。结果表明,在整个发动机转速范围内,排气最大可利用效率均高于46%,转速越高则排气品质越高。在不同工况下存在最优的膨胀机转速和蒸发压力。经过优化,在选取的4个工况下,功率提高率均在6%以上,最高达到7.08%。     

From making things public to the design of creative democracy: Dewey’s democratic vision and participatory design

ABSTRACT

Participatory design’s (PD) shift from the workplace to civic settings has led to a reorientation of the field’s political bearings. Informed by science and technology studies, practice is now often framed in terms of design things, infrastructuring, and John Dewey’s concept of ‘publics’. Taking the publics concept as a starting point, the present article seeks to contribute by providing a broader outline of Dewey’s democratic vision. It is proposed that Dewey’s vision may be seen to offer a potentially useful perspective that directly relates the ‘publics’ concept to the areas of freedom, experientialism, and the institutions of government. Linking to contemporary developments in PD and beyond, the vision is seen to carry conceptual and practical implications, which, if borne out, would connect the discipline’s capacity to ‘spark’ publics into being to the processes of policy formation and institutional reform.