小型特种精细化工过程自动化和信息化研究发展趋势探讨

针对采取小批量间歇性批次生产方式、工艺介质腐蚀性强、危险性大的小型特种精细化工生产工艺研究试验过程自动化、信息化程度不高，导致工艺研究试验中获取数据较少、过程机理研究不够透彻、人工操作多、安全风险高、研究试验消耗大及效率不高等问题，综述了期待通过智能控制、在线分析、模拟仿真和虚拟制造、工况监测及预测性维护以及信息管理和生产调度等关键技术的研究和应用，提高小型特种精细化工生产工艺过程的自动化和信息化程度，实现小型特种精细化工生产工艺过程的数字化、虚拟化和智能化，降低生产安全风险和试验消耗，提高小型特种精细化工生产工艺研究试验的成功率和效率，达到小型特种精细化工生产工艺过程的数字化设计和精准生产的目标。     

车轮多边形磨耗统计规律及关键影响因素分析

针对列车车轮多边形磨耗问题广泛存在于轨道交通运输领域,会导致车辆/轨道系统产生高频的振动冲击,严重影响车辆和轨道系统零部件的使用寿命,危及行车安全这一问题,调查了大量车轮的多边形磨耗情况并进行统计分析,掌握了高速列车车轮多边形磨耗问题的现状和特点。以18～20阶多边形磨耗车辆为例,通过理论研究和试验分析(试验分析包括车辆系统振动特性测试和转向架模态特性测试),对车轮多边形磨耗的根本原因及诱导因素进行研究。研究发现,轮轨系统在580 Hz频率附近存在固有模态是导致车辆发生18～20阶多边形磨耗的根本原因,轮轨表面的各种不平顺能激发或者加剧轮轨系统在580 Hz频率附近的模态共振,从而诱发车轮多边形磨耗的产生。该结果可为高速列车车轮多边形磨耗问题的防止和进一步研究提供参考。     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

装填设备载荷影响因素分析及动力学仿真

大型货物的装填过程中影响载荷的因素较多,其中对中精度对装填载荷影响较为明显。通过动力学仿真软件ADAMS对装填过程中载荷进行模拟,并对各影响因素进行定量分析,得到对中偏差与装填载荷的关系。将仿真结果与试验数据进行对比,验证该仿真方法的正确性,可作为后续研究装填载荷的一种方法,为结构设计提供指导。     

机翼极限环颤振的CFD/CSD全隐式紧耦合方法

针对轴承振动实时状态监控的需要，从符号动力学角度提出了符号聚合近似与Lempel-ziv复杂度（SAX-LZC）融合的振动监控参数。首先，以Logistic映射和Duffing方程为对象，从理论角度验证了SAX-LZC对动力学结构表征的准确性，并验证了该指标的抗噪能力和计算效率；其次，将SAX-LZC指标与信息熵、样本熵、多分段Lempel-ziv复杂度等动力学参数性能进行了综合对比；最后，从实验角度对轴承早期微弱异常进行了监测，并对轴承典型故障进行了特征提取。理论研究结果表明，SAX-LZC具有动力学结构表征准确、抗噪能力好、计算高效简洁等优点，克服了常规动力学参数工程应用能力弱的问题。实验研究结果表明，SAX-LZC对早期微弱异常有准确的监测，对不同种类故障具有较好的区分度，弥补了时域和频域对轴承微弱故障表征能力不足的缺陷，是一种轴承振动实时状态监控与故障特征提取的有效参数。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACT

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.     

矿山深部开采岩体稳定性监测技术研究

针对矿山深部开采过程中易发生地压动力灾害的问题，基于微震监测技术，结合矿山实际特点建立岩体稳定性监测系统。优化台网设计，通过定点爆破的方法进行波速校正，最终建立了定位误差在10m,灵敏度在-2.2的监测台网。对监测数据进行波形拾取，分析微震事件时空演化特征，圈定潜在危险区域，并对潜在危险区域围岩变形损伤进行分析。最终总结出微震事件大幅度上升且处于较高水平、空间分布高度集聚、能量指数突然下降而累计视体积迅速上升的动力灾害预警前兆。研究结果可为深部开采岩体稳定性监测提供参考。     

New batch and continuous systems for converting hydrogen from water hyacinth

Water hyacinth was the chosen alternative biomass for H₂ production in this study. A batch and a continuous pilot plant systems are proposed. A batch system of 500?ml capacity was developed first for finding an appropriate biomass ratio which was found to be 2.5%wt. Results from the batch system was used to construct a kinetic model of water hyacinth hydrolysis. The same biomass and water ratio was used in the continuous system. Besides water hyacinth, glucose and cellulose?+?lignin biomasses were tested. During processing, temperature, pressure, and electric consumption of the system were recorded. The quality and quantity of gas products were analyzed by gas chromatography (GC). The water hyacinth biomass attained the highest rate of H₂ production (providing energy of 11.43?kWh/mole of glucose). Comparing the batch and continuous pilot systems, the continuous system achieved 2.7 times more H₂ mole% than the batch system did.     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.