天体运动对山区磷矿开采自然坡稳定的影响

针对宜昌磷矿高陡山区地下开采对上部山体稳定性影响,从天体力学的角度探讨天体运动对地球的摄动作用,并分析月球运动的引力对山区磷矿自然坡失稳的影响.     

栗西磷矿开采山体稳定性分析

根据山区陡崖地段采矿岩体移动变形的特点,针对栗西磷矿具体情况,将采空区上部每一点所产生的移动变形视为随机事件,由此可将概事积分法理论模型用于高陡山区地下磷矿开采岩体移动变形分析及山体穗定性预测.使用本文所建模型对磷矿开采后岩体移动变形及其对上部地表山体稳定性进行了具体计算和预测分析,提出了安全开采技术方案.     

丁西磷矿地下开采自然坡稳定性分析

根据模糊数学理论，针对丁西磷矿的开采问题，提出了自然坡稳定性分析的基本理论模型和方法。     

山区开挖对自然坡稳定性及环境影响分析

借助于概率积分法研究地下工程开挖对自然坡稳定性及环境影响,预计结果符合工程实际.表明概率积分法适用于预测分析地下开挖引起的地表移动及其对矿山环境的影响问题.     

柳山沟磷矿地下开采自然坡稳定性分析

针对柳山沟磷矿床地下开采自然坡失稳问题,应用模糊测度理论,推导了相应的地表下沉、倾斜、地表曲率、水平移动、水平变形的理论计算公式,对处于山区的柳山沟磷矿床地下开采所引起的地表移动与变形及山体失稳问题进行了具体分析.工程实例计算分析表明,理论计算结果与工程实测资料吻合.     

浅析降低磷矿开采损失率的途径

从矿床地质、工艺设计、管理等方面对磷矿开采过程中造成矿石损失率偏高的原因进行了分析,在此基础上提出了降低矿石损失的途径、措施.     

宜昌磷矿资源开采政策收紧

2008年湖北宜昌市通过对磷矿开采实行总量控制。探索矿肥、矿化一体化产业发展路子，使磷矿开发由单一卖矿、简单加工发展为精细化系列大产业。年产值超过100亿。     

公路下磷矿开采位移沉降规律分析

贵州省开磷集团用沙坝矿的部分矿体位于金阳公路下,为保护公路而保留的保安矿柱矿石量约2262万t,为矿区储量的50%。采用留竖向矿柱的房柱式分层充填法开采使金阳公路的位移沉降量仅为48.5mm,为公路下矿体开采提供了一种矿石损失率小,位移沉降量小的开采方法。     

磷矿开采企业发展战略选择研究

我国磷矿资源丰而不富,地区分布不平衡,据分析现有储量仅可供开采36年左右,因此,我国现有大中型磷矿开采企业正面临着事关生死的战略选择。本文从我国磷矿资源分布和供需形势出发,以战略管理理论为依据,深入分析磷矿开采企业的经营特点,提出磷矿开采企业的战略选择路径,从战略管理角度为磷矿开采企业的可持续发展提供支持。     

强化矿山开采技术提高磷矿资源回收率

介绍了贵州省磷矿资源概况,瓮安磷矿采矿生产现状和发展特点,并就如何提高矿山开采技术水平,发挥矿产资源优势,着力打造强有力的原料基地,促进贵州经济的可持续发展进行了论述.     

露天磷矿边坡岩体变形分析的Fuzzy模型

根据磷矿露天开采工程实际,采用Fuzzy测度理论,将开挖引起岩体边坡移动变形这一客观现象视为一模糊事件,依此建立了边坡岩体移动变形预测分析的Fuzzy测度模型.利用所建模型对已有的矿山边坡岩体移动参数进行了反分析,并对露天边坡移动变形及其稳定性进行了具体的分析,所获结果符合工程实际.     

人工神经网络在材料科学与加工中的应用

综述了人工神经网络在材料科学基础理论研究、材料检测以及其在材料工业中的应用研究。认为随着人工神经网络理论本身及其相关理论、相关技术的不断发展 ,其在材料科学与加工中的应用定将更加深入和广泛 ,特别是在材料加工中的应用具有良好的发展前景     

人工神经网络在深基坑变形预测中的应用

以 BP人工神经网络为基础 ,利用其强大的非线性映射能力 ,建立深基坑支护结构最大侧向位移的预测模型。在利用实测数据进行网络训练的基础上 ,对悬臂支护结构最大侧向位移进行预测 ,结果表明预测值与实测值吻合较好 ,显示了该方法进行深基坑变形预测的有效性。     

Application of artificial neural network for prediction of 10 crude oil properties

This study aims to develop an industrially reliable and accurate method to estimate crude oil properties from their Fourier transform infrared spectroscopy (FTIR) spectra. We used the complete FTIR spectral data of selected crude oil samples from seven different Canadian oil fields to predict 10 important crude oil properties using artificial neural networks (ANNs). The predicted properties include specific gravity, kinematic viscosity, total acid number, micro carbon content, and production of light and heavy naphtha, Kero, and distillate in oil refineries. The 107 different (65 light oil and 42 heavy/medium oil samples) crude oil samples used in this study came from seven oil fields and reservoirs across Canada. In line with standard practice, we used 80% of the dataset for training the ANN models and used the remaining 20% of the crude oil samples to test the models. In the ANN analysis, the mean squared error (MSE) was used as the loss function in models, and the mean absolute prediction error (MAPE) was used as a reference to compare the performance of different neural networks constructed with different numbers of layers. This work demonstrates that FTIR spectroscopy is a promising technique that provides rapid and accurate estimates for the oil properties of interest to the industry. A comparison of the values predicted by the validated ANN models and their corresponding measured (actual) values showed excellent prediction with the acceptable range of error (below 15%) aimed for by our industry partner for all properties except viscosity, for which building models based on the natural logarithmic values of measured viscosities significantly improved the results.     

山区露天矿高陡边坡爆破振动安全判据

以甘肃南部山区厂坝露天矿高陡岩石边坡工程为例,讨论了边坡稳定性评价中的爆破效应以及当前边坡工程中所采用的经验方法,在此基础上,提出了该露天矿山高陡岩石边坡爆破振动安全判据.     

英坪矿Ⅰ号坑东翼边坡的稳定性分析及变形防治措施

根据瓮福磷矿英坪矿Ⅰ号坑东翼边坡概况、区域她质条件、影响边坡稳定的因素等,分析了边坡的变形特征,提出了边坡变形的防治措施及治理方案;以便为Ⅱ号坑将来形成的东翼边坡提供经验与借鉴.     

Application of artificial neural network optimization for resilient ceramic parts fabricated by direct ink writing

Geometries of ceramic parts for high-temperature sealing have great influence on their compression-resilience behaviors. In this work, an accurate and large-scale artificial neural network (ANN) was established to match the relationship between structural parameters and mechanical properties of ZrO₂ parts fabricated by 3D printing. Four geometry parameters of the designed ZrO₂ parts were imported as input and apparent Young's modulus and maximum deformation simulated by finite element method (FEM) were imported as output. FEM calculation provided 400 groups of data for the training of ANN, which greatly improved the predicted accuracy of the network. The predicted results show the mechanical performance of the parts with a range of modulus from 9.24 × 10⁻³ GPa to 100.35 × 10⁻³ GPa and a range of maximum deformation from 2.32% to 5.80% can be forecasted with error less than 8%. Based on the optimized structural parameters, the designed ZrO₂ parts were fabricated by Direct Ink Writing (DIW) technique. The experimental compression-rebound property is comparable to that of ANN prediction. It demonstrates that the combined method of ANN and FEM is a preferable way to optimize the structure and guide the fabrication of complex ceramic parts by 3D printing method.     

有限差分法在露天采场边坡稳定性分析中的应用

以棒锤山露天铁矿为例,应用有限差分程序FLAC软件的分析计算方法,分别就应力应变,X和Y方向的位移,以及安全系数对采场边坡稳定性的影响进行分析,得出露天采场边坡稳定性定量化分析结果,为今后的边坡灾害防护治理提供依据.