Shape optimization of a micromixer with staggered herringbone groove

Shape optimization of a staggered herringbone groove micromixer using three-dimensional Navier-Stokes analysis has been carried out. The analysis of the degree of mixing is performed by the calculation of spatial data statistics. The calculation of the variance of the mass fraction at various nodes on a plane in the channel is used to quantify mixing. A numerical optimization technique is applied to optimize the shape of the grooves on a single wall of the channel. Two design variables, namely, the ratio of the groove depth to channel height ratio and the angle of the groove, are selected for optimization. A mixing index is used as the objective function. The results of the optimization show that the mixing is very sensitive to the shape of the groove which can be used in controlling the mixing in microdevices. The mixing is affected by the depth of the groove much more than the angle of the groove.     

抚顺乙烯脱甲烷塔改造

通过乙烯装置脱甲烷塔系统改造后的运行状况的分析，对脱甲烷塔改造后的运行状态进行了总结，并为更进一步的优化工艺条件提出措施。     

高分子材料配方均匀设计系统

结合实验均匀设计和逐步回归优化方法，设计了更为科学的高分子材料配方设计系统，可更准确地预测产品的性能，简化实验程序，提高实验效率。该系统特别适用于多因素多水平多指标实验，在配方变量的变化范围内，利用回归方程建立单一组分与性能间的关系，并可用于预测某一配方的性能指数，利用计算机程序在设定优化条件后可得到相应的优化配方及其性能指标。     

企业信息系统多领域协同优化研究

企业信息化的前沿性问题是企业信息集成和优化。本文依据设计理论中的多学科协同优化的思想,将协同优化的概念引入到企业信息集成中。文中介绍了三种用于高层优化的算法,基于信息总线结构的EA I平台,提出了优化集成平台的构造方法。     

氯碱装置自动化提升总结

通过自动化提升项目的实施情况,对氯碱装置自动化提升方法和效果进行了分析总结,对控制回路优化、报警优化、顺序控制、先进控制等做了简要介绍,对于计划实施类似项目的氯碱企业有一定的指导和借鉴意义。     

质量交换网络集成的状态空间法

State space approach is an effective method to mass-exchange network (MEN) synthesis. By decomposing the network into two interactive parts, a distribution network and a process operator, the synthesis problem can be formulated into a mixed integer nonlinear programming (MINLP) model. In this article, a generalized state space model based on typical MEN is established and verified in two cases. A new asymmetrical operator and cost index are also adopted to speed up the solution process. The results demonstrate the efficiency of the proposed approach.     

Decomposition of timed automata for solving scheduling problems

A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.     

Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.     

An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.