并流喷雾干燥器优化设计数学模型与软件开发

以喷雾干燥系统的年总费用为目标函数建立优化设计数学模型 ,用菲波那契法求解。喷雾干燥器优化设计软件采用可视化编程语言VisualBasic 5 .0开发 ,软件界面友好、操作方便、运行可靠稳定。将其应用于牛奶并流喷雾干燥器的优化设计 ,算例表明 :优化设计数学模型和软件为喷雾干燥器的设计提供了较合理、有效的手段。     

炼油企业全厂调度优化系统的设计与开发

生产调度优化对于炼油企业提高经济效益、增强市场响应速度有着重要的作用。本文设计并开发出一款图形化炼厂生产调度优化软件,采用MVC架构技术,实现了调度优化建模的图形化,可以方便组态炼油厂调度模型;将开源优化代码Coin-OR移植为调度优化软件的求解器,实现了求解器与图形化建模接口的连接;丰富的报表输出,让现场操作工可以各种形式获得调度优化结果;完善的内部模型数据管理和丰富的外部数据接口,包括原油性质指标库和原油评价数据库接口、成品油性质指标库等。仿真结果表明,该软件能够优化出符合炼厂生产实际的调度优化排产方案。     

基于信赖域算法的油田开发规划模型研究

根据油田开发的各种成本投资形式,可以建立多种油田开发的非线性模型,结合求解非线性规划的信赖域方法,以最低开采成本规划模型为例,提出一种油田开发规划模型的求解算法,为合理开发油田提供了定量依据,并运用MATLAB进行了算例分析,得出规划模型的最优参数。     

基于Solid Edge的塑料模标准件库系统研究

针对模具设计需重复使用大量标准件的特点,以VB为开发工具,对三维CAD软件Solid Edge进行二次开发,开发出了塑料模标准件库系统,使标准件的调用实现了自动化,减少了设计中大量的重复性工作,简化了设计,提高了效率.研究证明:使用VB编程技术操纵Solid Edge变量表建立标准件库的方法是可行的.开发的塑料模标准件库系统不再仅仅局限于某个零件,还可扩展到装配体,为复杂模具标准件库系统的开发提供了思路.研究成果对建立相关行业标准件库有一定指导意义.     

大规模非线性优化软件包UniOptima的设计和开发

根据将SQP算法应用于大规模问题求解的两种改造思路,并结合目前这方面的研究成果,在M atlab环境下开发了大规模非线性优化工具箱Un iOptim a。Un iOptim a包括两个非线性求解器RSQP和THSQP,RSQP专门应用于自由度相对较小的等式约束优化问题,THSQP则应用于一般的非线性优化问题。本优化工具箱采用M atlab语言开发,并与M atlab优化工具箱保持了良好的兼容性,大大方便了用户使用。求解器中设有很多优化选项,用户可根据不同情况或不同需要选择不同的优化选项,增强了用户和程序的交互性。     

基于Solidworks的塑料模具标准件库开发

Solidworks软件在塑料模具设计中得到了广泛应用，通过对Solidworks软件二次开发建立了符合我国标准的塑料模具标准件库，并对采用VB6．0（Visual Basic 6．0）开发标准件库需要解决的对象链接与嵌入、数据库、标准件建模、菜单定制等技术进行了探讨。     

可控低强度材料(CLSM)性能评价方法分析

可控低强度材料(CLSM)是一种高流动性、可控低强的新型充填材料,被广泛应用于工程回填、道路基层、管道垫层等工程。目前,我国尚无CLSM性能测试标准,主要借鉴国外标准与混凝土、砂浆等材料相关评价方法,亟待制定合理、统一、系统的性能测试方法与标准,规范和促进CLSM大规模推广应用。本文综述了现有CLSM相关工作性能、力学性能、环境影响、耐久性等方面的测试方法,从测试原理、实验参数、优缺点等方面进行了对比分析,为CLSM性能测试提供依据,也为我国制定和完善CLSM标准体系提供参考。     

生产需求不确定的间歇生产调度决策

针对不确定条件下多周期间歇过程调度问题数学模型复杂,计算时间长等特点,本研究提出混合近似求解策略,通过惩罚和时间收缩等方式,将原问题的大规模MILP分解为若干小规模MILP,以实现其快速求解.通过算例的考察证实了论文提出的求解策略的有效性.     

乙烯分离流程联塔系统的大规模联立动态模拟

在乙烯生产流程中,以脱丙烷塔与脱丁烷塔的复杂联塔系统作为研究对象,根据机理建立双塔联立的动态数学模型.该联塔数学模型是一个大规模的微分代数混合系统(DAEs),包括了精馏塔、控制器、阀门、泵等多种操作单元和设备.动态模拟实质即为求解这个大规模的DAE系统.文章以动态模拟软件gPROMS为建模工具和模拟环境,通过调用大规模DAE求解算法,模拟联塔的动态操作过程,并与Aspen Plus稳态模拟结果进行了比较和分析.     

实用化的注塑模CAD/CAE/CAM微机系统HSC1.1

基于微机开发的注塑模CAD/CAE/CAM系统集成了注塑零件图形输入、结构设计、流通设计、流动及冷却分析软件和数控加工软件,并建立适合于各个工厂的标准模架库、标准结构图库、工艺参数数据库。具有很强的实用性。     

基于收敛深度控制的多元混合非线性规划算法

随着对象模型描述的系统性和完整性的提高,过程优化问题的复杂程度逐步增加,对优化算法的性能提出了更高的要求。现有的非线性规划算法在求解性能上各有优劣,本文提出了一种基于收敛深度控制的多元混合非线性规划算法,将各个非线性规划算法视为元算法,利用收敛深度来控制这些元算法之间的相互协作,更好地发挥元算法各自的优势,从而提高求解大规模复杂优化问题的能力。采用空分系统的数据校正问题以及脱丙烷塔和脱丁烷塔联塔系统的优化问题对多元混合算法进行了测试,数值结果表明相比各个单独的非线性规划算法而言,多元混合算法具有更好的求解性能。     

Genetic algorithm in mix proportioning of high-performance concrete

High-performance concrete is defined as concrete that meets special combinations of performance and uniformity requirements that cannot always be achieved routinely using conventional constituents and normal mixing, placing, and curing practices. Ever since the term high-performance concrete was introduced into the industry, it had widely used in large-scale concrete construction that demands high strength, high flowability, and high durability. To obtain such performances that cannot be obtained from conventional concrete and by the current method, a large number of trial mixes are required to select the desired combination of materials that meets special performance. Therefore, in this paper, using genetic algorithm that is a global optimization technique modeled on biological evolutionary process—natural selection and natural genetics—and can be used to find a near optimal solution to a problem that may have many solutions, the new design method for high-performance concrete mixtures is suggested to reduce the number of trial mixtures with desired properties in the field test. Experimental and analytic investigations were carried out to develop the design method for high-performance concrete mixtures and to verify the proposed mix design.     

并行计算在分子模拟中的应用

简要介绍了并行计算机的发展背景,结合网络中心搭建的Linux cluster并行机说明了此类并行机的基本架 构。对三款常用的并行分子模拟软件进行了评述,比较了不同架构并行机的运行效率。对其中一款软件在此并行 机上进行了实测,并对并行机和分子模拟软件的发展进行了合理的预测。     

Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor

This work presents a procedure to solve nonlinear dynamic data reconciliation (NDDR) problems with simultaneous parameter estimation based on particle swarm optimization (PSO). The performance of the proposed procedure is compared to the performance of a standard Gauss-Newton (GN) scheme in a real industrial problem, as presented previously by Prata et al. [2006. Simultaneous data reconciliation and parameter estimation in bulk polypropylene polymerizations in real time. Macromolecular Symposia 243, 91-103; 2008. In-line monitoring of bulk polypropylene reactors based on data reconciliation procedures. Macromolecular Symposia 271, 26-37]. Both methods are used to solve the NDDR problem in an industrial bulk propylene polymerization process, using real data in real time for the simultaneous estimation of model parameters and process states. A phenomenological model of the real process, based on the detailed mass and energy balances and constituted by a set of algebraic-differential equations, was implemented and used for interpretation of the actual plant behavior in real time. The resulting nonlinear dynamic optimization problem was solved iteratively on a moving time window, in order to capture the current process behavior and allow for dynamic adaptation of model parameters. Obtained results indicate that the proposed PSO procedure can be implemented in real time, allowing for estimation of more reliable process states and model parameters and leading to much more robust and reproducible numerical performance.     

基于相对增益和优先级的化工过程协调优化裕量设计

化工过程设计裕量一般是通过设计经验或经济优化给出的,设计经验无法保证经济性能的优化,而经济优化需要求解大规模非线性优化问题,计算复杂,容易陷入局部极值点,设计结果有时与设计经验违背。本文用非方相对增益阵和非方相对能量增益阵描述化工过程设计的自变量和因变量的灵敏度关系,将自变量划分为操作变量和设计变量,将因变量划分为经济指标和约束变量。相对增益具有无量纲化和归一化的优点,因此可根据经济指标和约束变量的相对增益对操作变量和设计变量划分优先级,针对过程不确定性的大小按照优先级依次调整各个操作变量和设计变量,找到对过程经济性能影响最小并有效移动操作点、远离约束边界的裕量设计方案。以串联反应釜为例对该设计方法进行了验证,结果表明,与求解经济最优化问题的裕量设计方法相比,本设计方法得到了经济性能与之接近的设计结果,计算简单,无须求解最优化问题。     

A multilevel coordinate search algorithm for well placement,control and joint optimization

Determining optimal well placements and controls are two important tasks in oil field development. These problems are computationally expensive, nonconvex, and contain multiple optima. The practical solution of these problems requires efficient and robust algorithms. In this paper, the multilevel coordinate search (MCS) algorithm is applied for well placement and control optimization problems. MCS is a derivative-free algorithm that combines global and local search. Both synthetic and real oil fields are considered. The performance of MCS is compared to generalized pattern search (GPS), particle swarm optimization (PSO), and covariance matrix adaptive evolution strategy (CMA-ES) algorithms. Results show that the MCS algorithm is strongly competitive, and outperforms for the joint optimization problem and with a limited computational budget. The effect of parameter settings for MCS is compared for the test examples. For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.     

The optimal design of experiments (ODOE) as an alternative method for catalysts libraries optimization

A comparative study was made between two optimization strategies for the development of heterogeneous catalytic materials. The evolutionary approach (EA), which is based on genetic algorithms (GA), is a well-proven stochastic strategy here used as a reference to evaluate our library optimization method (CLOM), which is based upon the optimal design of experiments (ODOE). This method was validated by means of two virtual models that correlate the performance and the formulation of a catalytic system. In order to build up the ODOE three criteria were considered: (1) D-Optimality's criterion, (2) a reduced set of 240 experiments and (3) a statistical model with at least two-parameter interactions. The sets of experiments proposed by the ODOEs were evaluated using the validation models and the results were used to fit the statistical models. The optimization of the fitted model was made by a traditional optimization technique (i.e. Levenberg–Marquard's) and the resulting optimal formulations were very close to the validation models with an error lesser than 3%. Also, a considerable reduction of the total number of experiments was achieved by means of ODOE and CLOM, i.e. a catalytic system with up to eight components needed 40% of the experiments required by the reference method.     

An efficient algorithm for community detection in complex weighted networks

Community detection decomposes large-scale, complex networks “optimally” into sets of smaller sub-networks. It finds sub-networks that have the least inter-connections and the most intra-connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi-objective optimization problem. The whale optimization algorithm is extended to enable it to handle multi-objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population's positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non-dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process and several benchmark community-detection problems.     

Multiperiod design of azeotropic separation systems. I. An agent based solution

In this paper, the first in a two-part series, we explore an agent based solution approach to the multiperiod design of azeotropic separation systems. Asynchronous Teams (A-Teams), a software organization that has been successfully applied to combinatorial optimization problems in other disciplines, is applied to this chemical process system design problem. A-Teams is a framework in which multiple design techniques can work harmoniously in the solution of a problem too demanding for any one method. We introduce a simple architecture for constructing A-Teams that incorporates the use of persistent data. Several approaches for generating improved multiperiod azeotropic design solutions are developed and are used simultaneously in a large-scale demonstration. The software organization is found to be capable of developing and improving a population of preliminary designs for the multiperiod azeotropic design problem.