Applications of Genetic Algorithm in Polymer Science and Engineering

In the last several years, genetic algorithm (GA) has gained wide acceptance as a robust optimization algorithm in almost all areas of science and engineering. Polymer science and engineering is no exception. Researchers in this field have devoted considerable attention to the optimization of polymer productionusing objective functions and constraints that lead to products having desired material properties. Multiple-objective functions have been optimized simultaneously. An example is the minimization of the reaction time in a reactor (lower costs) while simultaneously minimizing the concentration of side products (that affect the properties of the product adversely). Several end-point constraints (equality or inequality) may also be present, as, e.g., obtaining polymer of a desired molecular weight. Again, this requirement stems from producing polymer having desired strength. Solving such problems usually result in Pareto sets. A variety of adaptations of GA have been developed to obtain optimal solutions for such complex problems. These adaptations can be used to advantage in other fields too. The applications of GA in areas of polymer science and engineering other than polymerization systems are few and far between, but this field is now maturing, and it is hoped that the future will see several newer applications.     

Multiobjective Optimization of Simulated Moving Bed Reactor and its Modification — Varicol Process

A comprehensive optimization study on a Simulated Moving Bed Reactor (SMBR) system is reported in this article for the direct synthesis of Methyl Tertiary Butyl Ether (MTBE). In addition, the SMB technology, which is based on synchronous switching of ports, was modified to a new more flexible Varicol process based on non‐synchronous switching. Multi‐objective optimization has been performed for both SMBR and Varicol and their efficiencies were compared. A state‐of‐the‐art optimization technique, viz., Non‐dominated Sorting Genetic Algorithm (NSGA) that allows handling of these complex optimization problems is employed in this study.     

A comparison of methods of extreme wind speed estimation

The paper presents a comparative assessment of methods for extreme value analysis of the US wind speed data using four different methods, namely Standard Gumbel, Modified Gumbel, Peaks-Over-Threshold (POT) and Method of Independent Storms (MIS). The analysis highlights the influence of methodological assumptions on the estimates of design wind speed corresponding to 50-year and 500-year return period. The results demonstrate that the MIS method leads to more stable quantile estimates than the POT method.     

基于多目标粒子群算法的电力系统环境经济调度研究

提出了一种新的多目标粒子群优化(Multi-Objective Particle Swarm Optimization, MOPSO )算法,用于求解电力系统的环境/经济调度问题。通过设计特定的约束修正因子,将不可行解修正成可行解,并在此基础上用惩罚函数法构建了新的适用于多目标粒子群的适应度函数模型。根据帕累托占优条件形成历史帕累托最优解集和全局帕累托最优解集,引入稀疏度排序法选择全局最优解,基于帕累托最优前沿的斜率特性,提出用斜率法筛选非劣解,采用基于模糊数学的满意度评价模型选择POF的折衷最优解。最后,用IEEE-30节点标准测试系统对所提算法进行了仿真测试,并与其他算法进行了对比。仿真结果表明所提算法可行、有效。     

基于多目标决策方法的反应离子刻蚀机约束环优化设计研究

从反应离子刻蚀设备腔室内部结构细节角度出发,研究腔室约束环对以450 mm为代表大晶圆尺寸刻蚀机流场影响。与同类研究相比,本文旨在突出研讨仿真模型的精确性与边界条件比较设置准则的重要性。本文首先介绍刻蚀机腔室约束环的结构及其功能并提出高精度粘度修正的实现方法并计算腔室流体气压分布。提出了腔室气压分布的多目标问题,采用遗传算法求解,并通过多目标决策对优化结果进行评价和排序,得到最适合的优化结果。最后,给出约束环设计结果并得出约束环可以优化腔室内气流分布轮廓的结论。     

Kriging Surrogate Based Multi-objective Optimization of Bulk Vinyl Acetate Polymerization with Branching

Despite the established superiority in finding the global as well as well-spread Pareto optimal (PO) points, the need of more numbers of function evaluations for population based evolutionary optimization techniques leads to a computationally demanding proposal. The case becomes more miserable if the function evaluations are carried out using a first principle based computationally expensive model, making the proposal not fit for online usage of the application. In this work, a Kriging based surrogate model has been proposed to replace a computationally expensive model to save execution time while performing an optimization task. A multi-objective optimization study has been carried out for the bulk vinyl acetate polymerization with long-chain branching using these surrogate as well as expensive models and Kriging PO solutions similar to those found by the first principle models are obtained with a close to 85% savings in function evaluations.     

Network morphospace

The structure of complex networks has attracted much attention in recent years. It has been noted that many real-world examples of networked systems share a set of common architectural features. This raises important questions about their origin, for example whether such network attributes reflect common design principles or constraints imposed by selectional forces that have shaped the evolution of network topology. Is it possible to place the many patterns and forms of complex networks into a common space that reveals their relations, and what are the main rules and driving forces that determine which positions in such a space are occupied by systems that have actually evolved? We suggest that these questions can be addressed by combining concepts from two currently relatively unconnected fields. One is theoretical morphology, which has conceptualized the relations between morphological traits defined by mathematical models of biological form. The second is network science, which provides numerous quantitative tools to measure and classify different patterns of local and global network architecture across disparate types of systems. Here, we explore a new theoretical concept that lies at the intersection between both fields, the ‘network morphospace’. Defined by axes that represent specific network traits, each point within such a space represents a location occupied by networks that share a set of common ‘morphological’ characteristics related to aspects of their connectivity. Mapping a network morphospace reveals the extent to which the space is filled by existing networks, thus allowing a distinction between actual and impossible designs and highlighting the generative potential of rules and constraints that pervade the evolution of complex systems.     

帕累托图的锅炉事故分析方法

为查明导致锅炉事故发生的主要原因,以利于锅炉的安全管理,特应用管理学上的帕累托图,将可能导致锅炉事故的隐患分为低水位工况、操作失误或维护不良及燃烧器损坏等9类,并利用Minitab软件绘制帕累托图,分别对2007年美国蒸汽锅炉、动力锅炉及热水锅炉事故隐患类别进行研究分析.结果表明,80%的锅炉事故来自低水位工况与部分操作失误或维护不良等20%的事故隐患,是导致锅炉事故发生的主要原因,因此可以针对这20%的事故隐患进行有效的管理和控制.     

改进型GEO卫星BTDAMA MAC协议时延性能分析

比较了采用不同调度策略的宽带GEO卫星网络突发目标按需分配协议,提出了采用纯按需分配与预约按需分配相结合的BTDAMA-HD协议。采用具有自相似特性的Pareto ON/OFF信源进行计算机仿真,对纯按需分配(BTDAMA-PD)与BTDAMA-HD协议进行了比较。仿真结果表明,BTDAMA-HD具有更好的时延/吞吐量性能。     

Determination of design space and optimization of solar water heating systems

In this paper, a methodology is proposed to determine the design space for synthesis, analysis, and optimization of solar water heating systems. The proposed methodology incorporates different design constraints to identify all possible designs or a design space on a collector area vs. storage volume diagram. The design space is represented by tracing constant solar fraction lines on a collector area vs. storage volume diagram. It has been observed that there exists a minimum as well as a maximum storage volume for a given solar fraction and collector area. Similarly existence of a minimum and a maximum collector area is also observed for a fixed solar fraction and storage volume. For multi-objective optimization, a Pareto optimal region is also identified. Based on the identified design space, the solar water heating system is optimized by minimizing annual life cycle cost. Due to uncertainty in solar insolation, system parameters and cost data, global optimization may not be utilized to represent a meaningful design. To overcome this, a region of possible design configurations is also identified in this paper.