Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

A kinetic model involving oxidation and volatilization in SiC-B4C-xAl2O3 ceramics system

In this paper, a new kinetic model considering both oxidation and volatilization kinetics is established and applied to analyze the oxidation of SiC-B₄C-xAl₂O₃ ceramics and other systems in various oxidation conditions. The effects of diffusion area and volume changes during the oxidation process are considered in this model. The physical meaning of each parameter in this model is explicit and simple. According to this model, the diffusion coefficient of species and the corresponding diffusion activation energy are easily available. The practicability of this model is well verified by the experimental data of SiC-B₄C-xAl₂O₃ and other systems oxidized under different conditions. In addition, the practice shows that the model is applicable not only to the systems where oxidation and volatilization coexist, but also to the system where only oxidation plays a major role. We hope the model proposed in this work can be used in other materials with more complex environments.     

煤矿冲击地压与冒顶复合灾害研究

随着煤矿向深部发展,矿井动力灾害既表现出冲击地压的部分特征,又表现出冒顶的部分特征。2种典型的灾害打破以往冒顶与冲击地压的发生具有一种互为逆向性的认知规律,在深部高应力煤巷,特别是留顶煤巷道中出现了相互诱导、复合发生的新灾害类型。在总结山东、山西和新疆矿区典型巷道冲击致顶板(顶煤)动力灾害特征的基础上,提出了深部巷道冲击地压与冒顶复合灾害的概念、机理与分类,指出复合灾害机理关键点在于揭示巷道整体系统和破碎区子系统的稳定原理及其2者间的相互影响。建立了巷道发生复合灾害的力学模型,根据扰动响应失稳判据,提出并得到了巷道发生复合灾害的临界应力Pcr、临界软化区半径ρ_cr和最大容许采扰应力增量σ_max,厘清了灾害发生的主控因素,分析了煤岩冲击倾向指数K、支护强度ps、巷道半径ρ₀、煤岩强度σ_c等对灾害发生的影响规律,同时阐明了围岩塑性软化、破碎深度随地应力增加的发育规律。研究结果表明,破碎发育巷道的动力失稳主体为弹性区、软化区与破碎区构成的不稳定系统,垮落主体为破碎区;稳定的破碎区提升了巷道冲击启动临界值,使其启动难度增大,但破碎区的发育又易引起顶煤垮落;巷道稳定支护是解决复合灾害的关键,科学合理支护既能有效调控围岩破碎防冒,又能提升冲击启动临界值。通过理论研究,揭示了巷道冲击地压与冒顶复合灾害的发生机理,阐明了巷道软化与破碎区及其稳控支护对深部破碎发育巷道动力灾害防治的重要性。     

Experimental Study of Oilfield Water Plugging with Organic Chromium Gel

Chemistry and Technology of Fuels and Oils - Application of water flooding technologies in the oilfield causes intensive heterogeneity of the oil reservoir and improper circulation of the injected...     

花状与颗粒状NiMn2O4纳米材料的制备及其超级电容性能

分别以尿素和氨水为沉淀剂，采用热溶剂法制备了多孔的花状NiMn₂O₄和颗粒状NiMn₂O₄纳米电极材料，采用 X射线衍射仪、扫描电镜、透射电镜和N₂ 吸附-脱附等手段对NiMn₂O₄材料的物相、形貌结构和孔径分布进行了表征，并通过循环伏安、恒电流充放电、交流阻抗等方法测试了所制备材料的电化学性能。研究了沉淀剂对NiMn₂O₄材料形貌、微观结构及电化学性能的影响。结果表明：以尿素为沉淀剂的NiMn₂O₄是由纳米片组成的花状结构，纳米片厚度为50～60nm，比表面积为104m²/g。在 1A/g 电流密度下比电容为1614F/g，在5A/g电流密度下，尿素为沉淀剂的花状NiMn₂O₄材料经1000次恒电流充放电后其比电容可达初始值的89%。以氨水为沉淀剂的多孔NiMn₂O₄为直径约30nm的纳米颗粒结构，颗粒间团聚严重，比表面积为91m²/g。在1A/g电流密度下比电容为1147F/g，在5A/g电流密度下，氨水为沉淀剂的颗粒状NiMn₂O₄材料经1000次恒电流充放电后其比电容可达初始值的80%。尿素为沉淀剂的花状NiMn₂O₄具有优越的超级电容性能。     

因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。     

考虑煤层倾角和钻孔倾角的钻屑量理论研究

为准确预测与预防深部开采煤与瓦斯突出,建立了考虑煤层倾角与钻孔倾角的新钻屑量理论计算公式,并分析了煤层倾角和钻孔倾角对钻屑量的影响。以平煤股份有限公司八矿为研究对象,将钻屑量理论计算值与该矿己15-21030风巷现场钻屑量实测值进行对比验证,得出在煤层倾角一定时,钻屑量随钻孔倾角增大呈幂指数递减关系,减小的比率呈“V”形变化趋势,原钻屑量理论值与实测值平均误差为5.5%,在考虑煤层倾角与钻孔倾角情况下的新钻屑量理论值与实测值平均误差为3.9%。     

Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic Cuckoo Search Algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods.     

Separators for alkaline water electrolysis prepared by plasma-initiated grafting of acrylic acid on microporous polypropylene membranes

Highly hydrophilic separators for alkaline water electrolysis were prepared by plasma-initiated grafting of acrylic acid on porous polypropylene (PP) membranes. The membranes were activated in a low-pressure radio-frequency discharge in oxygen and subsequently graft polymerization of acrylic acid was performed in aqueous solution. The membranes were characterized by gravimetric grafting degree (GD), SEM, FTIR, critical wetting surface tension (CWST) test, mechanical strength, and electrolytic conductivity. Moreover, the membranes were applied as separators in alkaline electrolysis cell, and content of hydrogen in the produced oxygen was measured to determine membrane permeability to hydrogen dissolved in the electrolyte. It was observed that increasing GD improves performance of membranes as separators in alkaline electrolysis, although the particular effects on the electrolytic conductivity and hydrogen permeability strongly depend on structure the of initial PP substrate. Ageing test conducted in 30 wt% KOH at 60 °C revealed that although considerable degrafting took place at beginning of the test, the remaining polyacrylic acid provided highly hydrophilic character to membrane for 7000 h of the test.