增压器式超高压水射流切割机产品化体会及展望

作为我国高压水射流切割机的第一个产品即“大地水刀”,已进入市场两年多了。笔者以此为背景,介绍增压器式水切割机的超高压发生器及切割部分;讨论其主要部件、零件结构设计及关键技术。尤其在产业化中,对高压缸体疲劳寿命和高压动密封可靠性等问题解决方案作深入探讨;给出在相关技术方面近期的发展。最后,结合我国水切割机技术的提高和推广,提出初步的看法和建议。     

矿井提升机的安全可靠性分析与设计

矿井提升机是井下作业提升物料和人员的关键设备 ,素有矿井咽喉之称。安全、可靠与高效是提升机设计与分析的基本准则与要求。笔者概要分析了矿井提升机的安全可靠性 ;提出了相应的设计要点 ,并对各子系统中相对薄弱环节的安全可靠性进行了探讨。以上科研成果 ,为提升机械的进一步研究与开发提供了一定的理论参考依据     

超高压宽极距管式电收尘器在水泥厂的应用

阐述了水泥厂磨机生产时的粉尘污染状况及采用和布袋除尘器或普通单管电收尘器收尘所存在的问题，针对磨机生产情况和存在问题进行设计的超高压宽极距防结露磨机静电收尘器的结构，工作原理等，并列出了该电收尘器的伏安特性和除尘效率测试数据。     

加强起重机械皮带运输系统安全管理

起重机械事故在冶金企业工伤事故中占很大的比例。从人和物的原因着手分析了若干起起重机械事故，并提出了对策措施。     

INTERPRETATION OF “RESTRICTED VALIDITY” IRRIGATION PERMITS IN THE SVARTÅ RIVER BASIN,SWEDEN1

ABSTRACT: Irrigation permits in the Svartå River basin, Sweden, are issued with restricted validities by which irrigation is permitted only in those periods when the release from the primary regulation facility in the basin is greater than 2.0 m³/s. A “risk” measure describing the expected total duration of periods in which irrigation will not be permitted in the basin under a range of conditions has been formulated and applied to the basin. The measure recognizes only the period for which irrigation is not permitted and does not consider water availability as it is not a factor in this case. Comparison of the measure with a previously proposed approach shows that it provides a better measure of system behavior. The new measure is particularly useful for transferring to the nontechnical water user or manager an understanding of how well the overall irrigation system can be expected to perform.     

数控机床的人性化设计研究

人性化设计是工业设计研究中一个非常重要的课题,对人性的关怀是每个工业设计师不变的追求.在数控机床的设计中,设计师应以人机工程理论为指导,充分考虑如何让使用者掌握数控机床的操作,解决诸如程序设计、人机对话、心理负荷、故障诊断和处理、系统安全以及社会心理等问题.这是数控机床人性化设计的关键,也就是如何设计出和谐的人机系统,创造一种良好的人机互动.     

浅谈机电管理在现代化矿井中的应用

机电管理是现代化高产高效矿井建设的重要组成部分，建立起一整套与高产高效相配套的管理体系，是加强设备现场管理，实现矿井高产、稳产的有力保证。文章介绍了补连塔煤矿为实现设备的可靠运转，加强机电管理的具体应用。     

基于扇区动态复杂性因素的航空管制员工作负荷计算

为了更加准确地计算和预测航空管制员的工作负荷,利用雷达管制模拟试验获取的数据,分别采用线性回归、神经网络的非线性回归和基于神经网络的支持向量机方法,建立了基于扇区复杂性因素的管制员工作负荷实时计算模型。结果表明,这3种模型的绝对误差平均值分别为0.969、1.049、0.240;相对误差平均值分别为16.667%、17.979%、6.229%;均方根误差分别为0.186、0.206、0.114。另外,若采用5%作为基准精度,基于神经网络的支持向量机模型可以将相对误差控制在-0.5%~0.5%,表现出较强的误差控制能力。研究表明,可以采用扇区动态复杂性因素来计算管制员的工作负荷,相比线性回归、神经网络的非线性回归方法,基于神经网络的支持向量机方法对管制员工作负荷的计算有更高的精度。     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.