镇沅金精矿选择性固砷焙烧—氰化浸出新工艺研究

对云南镇沅金矿的难当金精矿进行了选择性固砷焙烧－氰化浸出的试验研究，取得了满意结果。砷固定率大于９２％，硫挥发率为７８％－８４％，金浸出率可达９１％－９３％。选择性固砷焙烧－氰化浸出新工艺可有效消除砷害，综合利用有价成分硫，特别适宜于含砷高硫，同时含碳等杂质的细粒浸染型难浸金矿石的处理，在我国有较大的发展前景。     

Response time variability

Response time variability is a new optimization problem with a broad range of applications and a distinctive number of theoretic flavour. The problem occurs whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of time for which they wait for the next turn in obtaining the resources necessary for their advance. The problem has numerous real-life applications. We study its computational complexity, present efficiency, polynomial time algorithms for some cases, and the NP-hardness proof for a general problem. We propose a position exchange heuristic and apply it to improve the total response time variability of an initial sequence. The latter is the optimum bottleneck sequence, Webster or Jefferson sequence of the apportionment, or a random sequence. We report on computational experiments with the heuristic.     

裂解碳五选择性加氢研究

将裂解C5馏分资源经选择性加氢、醚化,合成了高辛烷值汽油调和组分。为满足醚化催化剂的要求,进行了蒸汽裂解C5双烯烃选择性加氢工艺研究,对裂解C5双烯烃选择性加氢催化剂进行了评价,考察了反应压力、空速、反应温度、氢烯摩尔比对双烯烃选择性加氢的影响,得出适宜工艺条件为:反应压力0.5～1.5 MPa,温度45～80℃,空速10～2．0 h-1 ,氢烯摩尔比大于1.1。     

A sequencing problem for a mixed-model assembly line in a JIT production system

In an assembly line of a just-in-time (JIT) production system, workers have the power and the responsibility to stop the line when they fail to complete their operations within their work zones. This paper deals with a sequencing problem for the mixed-model assembly conveyor line in the JIT production system. In some environment, the most important criterion is the line stoppage rather than the variation of production rates. The problem is to find an optimal sequence of units that minimizes the total line stoppage time. Lower and upper bounds of the total line stoppage time are derived and the branch-and-bound method is applied to the problem. A numerical example is given.     

基于Adams的摆杆输送机动力学仿真分析

采用Adams脚本语言建模,自动完成摆杆输送机整体系统的轨道、链条、摆杆和工件之间的装配约束和载荷边界条件的设定等.对某卡车白车身涂装过程进行动力学仿真分析,求得系统整体运行时所需的驱动力大小、链节间的相互作用力和进行静力学分析所需的载荷条件等,分析结果可指导摆杆输送机的整体设计.     

单片机混合编程的实现

介绍了单片机的发展,当前应用较多的主流单片机,并对单片机的混合编程实现进行分析与讲解。     

《计算机组装及维护》课程项目教学法研究

项目教学法对于中高职院校培养学生的实际操作能力十分有效,针对《计算机组装与维护》这门课程,通过项目设置、任务分解、组织完成的形式,激发学生学习的兴趣,提高了学生的实际动手能力,取得较好的教学效果。     

A novel vision-based method for 3D profile extraction of wire harness in robotized assembly process

Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments.     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.