基于响应面法的d -最优混合设计优化钢结构膨胀型防火涂料配方中的阻燃剂

Jian Hu
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引用次数: 0

摘要

响应面方法(RSM)是一种非常有效的工具,为开发和优化新工艺提供了很好的实践见解。该方法可以帮助工程师建立一个数学模型,将系统的行为表示为一个令人信服的过程参数函数。在涂料制备的研究和开发中,许多实验都涉及到混合成分。这些是用混合物进行的实验,其中实验因素是混合物的组成部分,响应变量取决于每种成分的相对比例,而不是混合物的绝对数量。因此,混合成分不能独立变化。优化用于制备膨胀型耐火涂料的配方需要确定几种阻燃剂组合的耐火有效性。我们讨论了这些类型的实验设计和分析,提出了一种d -最优设计方法,用于涉及混合组分的阻燃涂料配方的计算机辅助实验设计,并举例说明了使用实验设计(DOE)与统计软件包一起促进结构钢配方制定的好处。本文的目的是鼓励在涂料制备研究和开发中更多地利用信息技术。防火涂料的使用是保护材料防火的最有效方法之一[1]。膨胀防火系统是一种化合物,当加热时,它会熔化、起泡并形成泡沫炭,作为底层钢结构的绝缘材料[2]。膨胀型阻燃涂料组合物通常包含三种基本活性成分:碳源(如季戊四醇- per)、发泡剂(最常见的是三聚氰胺- mel)、酸源(通常是聚磷酸铵- app),它们通过聚合物材料等粘结剂连接在一起。为了优化膨胀型防火涂料(IFRC)的配方,需要确定几种阻燃剂的阻燃作用。本研究测试了三种阻燃剂:聚磷酸铵、季戊四醇和三聚氰胺。采用国际标准化组织(ISO)的耐火测试对其耐火效果进行了评价[3]。从有限数量的实验中,采用d -最优混合设计来提供最大的信息。本研究的主要目的是基于design Expert®软件对实验设计的数据进行多变量分析。我们在MODDE软件的帮助下分析了我们的调查结果,并制定了符合ISO 834- 1:20 99要求的涂料。多变量分析是一种统计工具,可用于确定多变量系统中自变量和因变量之间的贡献效应和关系。因变量是由一个或多个独立变量预测或解释的不受控制的变量。自变量是一个可以控制(改变)的量,可用作因变量的预测或解释变量。独立的
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Fire Retardants in a Formulations of Intumescent Fire Retardant Coatings for Steel Structure via a D-optimal Mixture Design Based on Response Surface Methodology
Response surface methodology (RSM) is a very efficient tool to provide a good practical insight into developing new process and optimizing them. This methodology could help engineers to raise a mathematical model to represent the behavior of system as a convincing function of process parameters. Many experiments in research and development in the paint preparation involve mixture components. These are experiments with mixtures in which the experimental factors are the components of a mixture and the response variable depends on the relative proportion of each components, but not on the absolute amount of the mixture. Thus the mixture components cannot be varied independently. Optimizing the formulations for a preparation of intumescent fire-resistant coating requires the fire resistant effectiveness of several fire retardants combinations to be determined. We discuss the design and analysis of these types of experiments, presents a D-optimal design methodology for computer aided experimental design for fire retardant coating formulations involve mixture components, exemplifies the benefits of using design of experiments(DOE) together with statistical software package to facilitate the formulating of recipe for structural steelworks. Goal of this paper is to encourage greater utilization of information technology in paint preparation research and development. Introduction The use of fire-resistant coatings is one of the most efficient ways to protect materials against fire[1]. Intumescent fire-resistant systems are chemical compounds which, when heated, melt, bubble and form a foamed char which acts as insulation for underlying steel structures[2]. Intumescent fire-retardant coatings composition usually contain three fundamental active ingredients: a carbon source (such as pentaerythritol-PER), a blowing agent (most often melamine-MEL), an acid source (generally ammonium polyphosphate-APP), and they are linked together by a binder such as polymer materials. Optimizing the formulations for a preparation of intumescent fire-resistant coating(IFRC) requires the fire resistant usefulness of several fire retardants collaboration to be confirmed. Three fire retardants were tested in this study: ammonium polyphosphate, pentaerythritol and melamine. Their fire resistant effects were evaluated using the fire-resistance tests of the International Organization for Standardization (ISO) [3]. From a limited number of experiments, a D-optimal mixture design was used to give a maximum of information. The main objective of the research presented here was to carry out multivariate analysis upon data from the experiment design based on Design Expert® software. We analyze our investigation results with the help of the MODDE software and to formulate coatings convincing requirements of the ISO 834-1:1999. Multivariate analysis is a statistical tool that can be used to determine the contributing effect(s) of and identify relationships between independent variables and dependant variables in a multivariable system. A dependant variable is an uncontrolled variable which is being predicted or explained by one or more independent variables. An independent variable is a quantity which can be controlled (altered) and used as a predicting or explanatory variable for a dependant variable. Independent
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