番茄红素发酵培养基及发酵过程控制的研究

为提高番茄红素的发酵水平,筛选了最优的发酵培养基配方,在发酵过程中补加其所需的营养物质,得到一个稳定的配方,并对发酵过程补料进行了优化,番茄红素发酵水平达到2.4 g/L,比较稳定。     

聚烯烃装置大型反应器的设备监造

简述石化设备制造产品质量监督的重要性及种类,着重介绍目前国内单套生产能力最大的LL—HDPE装置心脏设备——气相反应器国产化制造中的全过程监造技术,总结委托第三方进行设备监造的重要作用。     

世界聚丙烯工业的发展趋势

聚丙烯工业产能不断扩大，兼并、联合、重组事件不断。2001年全球PP生产能力达到36Mt/a，预计2005年将达到48Mt／a。2001年我国PP的产量达到3．225Mt，预计2005年生产能力将达到4．1Mt／a。近年来，聚丙烯的催化剂技术也不断改进，茂金属和单活性中心催化剂技术使PP产品性能显著改善。     

沈阳6M50压缩机六七级PEEK活塞环改造应用小结

江苏华昌化工股份有限公司前身为张家港市化肥厂,2004年2月经批准,更名为江苏华昌化工股份有限公司,并整体搬迁至江苏省扬子江国际化学工业园.公司是以合成氨为主导产品的基础化工企业,在新搬迁的合成氨生产装置中,对直接决定合成氨生产能力的压缩机,分别选用了沈阳压缩机有限公司和上海压缩机有限公司生产的6M50型大型往复式压缩机,该机单台合成氨生产能力为:40ktNH3/a,故压缩机运行的稳定性直接影响到合成氨的产量.     

谈设备固定资产的两个考核指标

作为生产手段的设备固定资产,在生产过程中,不断将自身价值转移到产品中去,直至这种转移终结,设备报废,重新获得物质形态和价值形态的双重补偿。该变化过程在价值形态上可表示为下式: 原值=净值+折旧(1)设备固定资产本身的价值与其代表的生产能力创造的价值往往是不同的,在社会生产中,如果后者超过前者,则表明垫支的物化劳动的经济效益好,超出越多,     

煤矿主提升系统的技术改造及应用效果

河南煤业化工集团永煤公司车集煤矿．原设计生产能力为180万吨／年．经技术改造，核定生产能力为305万吨／年。主提升井筒深度为652．22米．采用4m×4绳（绳直径为φ39．50mm）低速直联落地式摩擦轮式（电机为双绕组交流同步电机．功率2500kw）双箕斗提升机．单箕斗满载为16．5吨．最大提升速度为12．15m／s。     

对本钢兴办经济实体的剖析

<正> 本钢作为一个大型钢铁联合企业,拥有十几万职工。然而,本钢职工人数与生产规模及生产能力相比,已暴露出严重的超员和劳动生产率低下的矛盾。为优化产业结构,提高国有资产运营效益,精简和分离富余人员,公司各单     

厦大研发连续催化碳纳米管反应器

厦门大学化学化工学院研发出与催化剂特性相匹配的化学气相沉积法连续＇制备的碳纳米管反应器,目前已有较好的小试结果,今年将在新疆建设一个模试装置,进一步研究考察反应器放大规律,最终形成单装置吨级生产能力。     

塔里木大化肥项目一次试车成功

2010年5月19日,我国陆上单套装置生产能力最大的现代化化肥装置—塔里木油田年产45万t合成氨/80万t尿素项目打通全部工艺流程,生产出合格大颗粒尿素。目前,工艺设备运行平稳,已转入试生产阶段。     

我国PTA技术发展与节能

近年来,国内精对苯二甲酸(PTA)生产能力和消费量快速增长,单套装置规模越来越大,新工艺、新技术、新设备和节能技术的开发应用也日新月异。着重论述了PTA装置主要节能技术及其应用效果,供新建PTA装置和现有装置技术改造参考,以提高PTA装置的技术水平,实现PTA产业的绿色、节能、环保和可持续发展。     

采用蚁群算法模拟机器人寻路的仿真实验

蚁群算法是一种基于蚁群寻找食物这一现象,实现寻路优化的算法。通过在MATLAB中进行程序设计,实现了利用蚁群算法模拟自动寻路的计算,并进一步将程序应用于简易机器人的寻路模块,初步实现机器人的寻路优化功能。     

A modified artificial bee colony algorithm for order acceptance in two-machine flow shops

We consider a two-stage make-to-order production system characterized by limited production capacity and tight order due dates. We want to make joint decisions on order acceptance and scheduling to maximize the total net revenue. The problem is computationally intractable. In view of the fact that artificial bee colony algorithm has been shown to be an effective evolutionary algorithm to handle combinatorial optimization problems, we first conduct a pilot study of applying the basic artificial bee colony algorithm to treat our problem. Based on the results of the pilot study and the problem characteristics, we develop a modified artificial bee colony algorithm. The experimental results show that the modified artificial bee colony algorithm is able to generate good solutions for large-scale problem instances.     

基于改进的蚁群算法的移动机器人路径规划

针对移动机器人路径规划中的传统蚁群算法收敛精度低、易陷入局部最优等问题,提出一种改进蚁群算法。首先,对算法的转移概率进行改进,加入转向代价,减少不必要的转折,并针对启发函数启发性能不够强,对路径启发信息进行改进;然后,提出一种自适应的参数调整伪随机状态转移策略,动态改变参数值,避免过早陷入搜索停滞,增强搜索的全面性,同时对信息素更新方式进行改进,调整信息素挥发系数,保持蚂蚁发现最优路径的能力;最后,通过Matlab与其他算法进行对比分析。仿真结果表明,改进的蚁群算法收敛速度快,且路径长度和算法迭代次数有明显减少,能得到全局最优路径。改进蚁群算法具有可行性、有效性,在移动机器人路径规划中有一定的应用价值。     

Do more bees imply higher fees? Honey bee colony strength as a determinant of almond pollination fees

Markets for pollination services have been growing in importance all over the world as pollinator populations and habitats have decreased over the last few decades (Potts et al., 2010; González-Varo et al., 2013). An early econometric analysis of pollination markets noted that honey bee colony strength in the California almond pollination market is highly variable and as such has an influence on pollination fees (Cheung, 1973). More recent economic analyses of pollination services markets have ignored the influence of colony strength on the supply and demand for pollination services. This paper provides the first econometric investigation of delivered colony strength as a determinant of almond pollination fees. I analyze the California State Beekeeper’s Association pollination fee survey responses for years 2008–2016, a total of 263 beekeeper-reported observations. I find that providing weak colonies for almond pollination results in lower fees collected by the beekeeper. By implication, beekeepers who experience high winter mortality rates likely also receive lower per-colony almond pollination fees due to low delivered colony strength. I estimate that a 10 percentage point increase in a beekeeper’s winter mortality rate leads to an average decrease of 16% in total revenues from almond pollination, a substantial overall loss given that almond pollination is a primary source of U.S. commercial beekeepers’ revenues. Additionally, I estimate that when colony strength is accounted for, payments from the Emergency Assistance for Livestock, Honey bees and Farm-Raised Fish Program in 2016 would have compensated many beekeepers for less than half of their total revenue losses in almond pollination from the corresponding 10 percentage point increase in winter mortality rates. This compensation rate is likely much lower than previously thought. This research highlights the need for additional research on the total economic impact of threats to the health of honey bee colonies, especially given the rise in recent policies enacted globally with the goal of improving honey bee health and assisting beekeeping operations.     

蚁群算法在城市交通系统中的应用

根据城市交通的实际情况,介绍了用蚁群算法求解城市交通行驶中车辆最优路径的方法,帮助车辆找到最优路径,从而选择车流量较少的路径行驶。     

Flexible job shop scheduling with due window—a two-pheromone ant colony approach

Recently, the companies reduce the manufacturing costs and increase capacity efficiency in the competitive environment. Therefore, to balance workstation loading, the hybrid production system is necessary, so that, the flexible job shop system is the most common production system, and there are parallel machines in each workstation. In this study, the due window and the sequential dependent setup time of jobs are considered. To satisfy the customers’ requirement, and reduce the cost of the storage costs at the same time, the sum of the earliness and tardiness costs is the objective. In this study, to improve the traditional ant colony system, we developed the two pheromone ant colony optimization (2PH-ACO) to approach the flexible job shop scheduling problem. Computational results indicate that 2PH-ACO performs better than ACO in terms of sum of earliness and tardiness time.     

A multiple ant colony optimization algorithm for the capacitated location routing problem

The success of a logistics system may depend on the decisions of the depot locations and vehicle routings. The location routing problem (LRP) simultaneously tackles both location and routing decisions to minimize the total system cost. In this paper a multiple ant colony optimization algorithm (MACO) is developed to solve the LRP with capacity constraints (CLRP) on depots and routes. We decompose the CLRP into facility location problem (FLP) and multiple depot vehicle routing problem (MDVRP), where the latter one is treated as a sub problem within the first problem. The MACO algorithm applies a hierarchical ant colony structure that is designed to optimize different subproblems: location selection, customer assignment, and vehicle routing problem, in which the last two are the decisions for the MDVRP. Cooperation between colonies is performed by exchanging information through pheromone updating between the location selection and customer assignment. The proposed algorithm is evaluated on four different sets of benchmark instances and compared with other algorithms from the literature. The computational results indicate that MACO is competitive with other well-known algorithms, being able to obtain numerous new best solutions.     

Metaheuristics for drilling operation scheduling in Taiwan PCB industries

Among all types of production environment, identical parallel machines are frequently used to increase the manufacturing capacity of the drilling operation in Taiwan printed circuit board (PCB) industries. Additionally, multiple but conflicting objectives are usually considered when a manager plans the production scheduling. Compared to the single objective problem, the multiple-objective version no longer looks for an individual optimal solution, but a Pareto front consisting of a set of non-dominated solutions will be needed and established. The manager then can select one of the alternatives from the set. This research aims at employing a variable neighborhood search (VNS) algorithm and a multiple ant colony optimization (MACO) algorithm to solve the identical parallel-machine scheduling problem with two conflicting objectives: makespan and total tardiness. In VNS, two neighborhoods are defined—insert a job to a different position or swap two jobs in the sequence. To save the computational expense, one of the neighborhoods is randomly selected for the target solution which is also arbitrarily chosen from the current Pareto front. In MACO, a two-phase construction procedure where three colonies are employed in each phase is proposed. These two algorithms are tested on a set of real data collected from a leading PCB factory in Taiwan and their performances are compared. The computational results show that VNS outperforms all competing algorithms—SPGA, MOGA, NSGA-II, SPEA-II, and MACO in terms of solution quality and computational time.     

Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations

This paper studies the simultaneous dock assignment and sequencing of inbound trucks for a multi-door cross docking operation with the objective to minimize total weighted tardiness, under a fixed outbound truck departure schedule. The problem is newly formulated and solved by six different metaheuristic algorithms, which include simulated annealing, tabu search, ant colony optimization, differential evolution, and two hybrid differential-evolution algorithms. To evaluate the total weighted tardiness associated with any given inbound-truck sequence and dock assignment, an operational policy is developed. This policy is employed by every metaheuristic algorithm in searching for the optimal dock assignment and sequence. Each metaheuristic algorithm is tested with 40 problems. The major conclusions are: (1) metaheuristic is generally an effective optimization method for the subject problem; (2) population based metaheuristic algorithms are generally more effective than projection based metaheuristic algorithms; (3) proper selection of algorithmic parameters is important and more critical for projection based metaheuristic algorithms than population based algorithms; (4) the two best algorithms are ant colony optimization and hybrid differential evolution 2; among them, ACO takes less time than hybrid 2 and thus can be declared the best among all the six metaheuristic algorithms tested.     

Ant colony optimization for the single model U-type assembly line balancing problem

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them.