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.     

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...     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

不同粒径改性粉煤灰对磷酸根吸附性能的影响

废水排放过量的磷导致水体污染日益严重，将粉煤灰通过化学改性制备成了水化硅酸钙吸附剂，研究了改性吸附剂对磷酸根的吸附效果。利用XRD, SEM及BET比表面积等手段对粒度分级前后的吸附剂进行表征，研究不同粒级吸附剂对磷酸根的吸附性能，并考察其吸附机理。结果表明，不同粒级的吸附剂其化学成分出现了明显的偏析现象，孔隙结构也差异显著。相比其他粒径下的吸附剂颗粒，颗粒粒径在50?75 μm时，吸附剂中钙和硅含量较多，铝、铁和镁含量较低，水化硅酸钙组分含量最高，且伴有含铝的托贝莫来石晶体出现，钙离子的增加使其可以与更多的磷酸根结合形成沉淀。同时此粒径下具有较高的比表面积及孔隙度，疏松多孔的结构为钙离子提供更多活性位点。当使用粒径在50?75 μm的吸附剂吸附磷酸根时，磷的饱和吸附量可达到17.1 mg/g，比未分级的吸附剂高19.58%。     

具有光照鲁棒的图像匹配方法

针对现有的基于局部特征的图像匹配算法对光照变化敏感、匹配正确率低等问题，提出一种具有光照鲁棒性的图像匹配算法。首先使用实时对比保留去色（RTCP）算法灰度化图像，然后利用对比拉伸函数模拟不同光照变换对图像的影响从而提取抗光照变换特征点，最后采用局部强度顺序模式建立特征点描述符，根据待匹配图像局部特征点描述符的欧氏距离判断是否为成对匹配点。在公开数据集上，所提算法与尺度不变特征变换（SIFT）算法、加速鲁棒特征（SURF）算法、"风"（KAZE）算法和ORB算法在匹配速度和匹配正确率上进行了对比实验。实验结果表明：随着图像亮度差异的增加，SIFT算法、SURF算法、"风"（KAZE）算法和ORB算法匹配正确率下降迅速，所提算法下降缓慢并且正确率均高于80%；所提算法特征点检测较慢和描述符维数较高，平均耗时为23.47 s，匹配速度不及另外四种算法，但匹配质量却远超过它们。对实时性要求不高的系统中，所提算法可以克服光照变化对图像匹配造成的影响。     

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。     

催化裂化装置生产异戊烯的模拟计算研究

应用HYSIM模拟软件,对催化裂化(FCC)装置获取碳五馏分的各种方案进行了研究,并对各方案的优缺点进行了评述。仅有一套FCC装置的炼油厂或者同时拥有几套FCC装置但各装置稳定汽油辛烷值接近的炼油厂,推荐采用塔底方案。炼油化工型企业可考虑塔顶方案或侧线方案。     

Disposal of Waste Plastics With Traditional Coking Process

A new technology for treating waste plastics （WP） by traditional coking process was introduced. With a thermo-balance and a 10 g atmospheric fixed bed reactor, the thermo-gravimetric behavior and product were studied during co-coking of WP with blended coal. And then, using a coke-oven with capacity of 200 kg, the characteristics of products were assessed. The results showed that there is an overlapping temperature range （200-550℃ ） of decomposition between WP and blended coal, and the pyrolysis synergism index η and synergism strength β proposed could evaluate the synergism between them. 1% of added WP results in the maximum synergism in all series experiments. The increase of added WP decreases the synergism. Tar yield in co-coking is increased with the decrease of water yield for synergism. Moreover, it was also found that the quality indexes of coke, such as M10, M40, CRI and CSR, are degraded with the increase of WP until 4%, though the quality of tar and gas is optimized for WP addition.     

基于一种混合的节点定位算法的研究

针对基本的DV-Hop节点定位算法会产生不良的节点和多级跳带来的累积误差以及未知节点的坐标没有进行修正等缺点,提出了一种改进的混合节点定位算法WHDV-Hop算法,改进后的算法定位误差率明显比改进前的定位误差率小,通过MATLAB软件对节点定位误差方面进行仿真验证,仿真实验结果证明该改进后的算法稳定、可靠,易于实现。与DV-Hop算法相比,定位精确度提高了4.65%,计算量减少,降低了能量消耗水平,实现预期的目标。     

和声搜索算法改进BP网络在水质评价中的应用

针对BP神经网络收敛速度慢,容易陷入局部极小值的缺点,提出了用和声搜索算法优化BP网络连接权和阈值的方法改进神经网络,并通过对水质评价指标的分析,建立了水质评价问题的数学模型,结合指标PH、DO、COD、NH3将和声搜索算法改进的BP网络应用于所建立的模型中进行求解;实验结果表明,和声算法改进BP网络在一定程度上可以使网络收敛速度加快和避免局部极小,准确评价出水质的级别。