Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a Constructive Algorithm and Definition of Initial Weights

IF 1.8 4区 工程技术 Q3 ENGINEERING, CHEMICAL
Adilton Lopes da Silva, Cristiano Hora Fontes, Marcelo Embiruçu
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Abstract

This work presents the development and validation of two virtual analyzers (density and Melt Index (MI)) for quality monitoring and control of the final product in an industrial unit of Linear Polyethylene (LPE). Both models are based on Feedforward Neural Networks which are improved through a strategy involving the initial estimation of weights and a constructive algorithm to define the number of hidden units. The initialization strategy is based on linearization of the neural model with only one hidden unit (nonlinear model) and subsequent optimization of this model by maximizing its similarity to the standard linear regression model whose solution is obtained analytically. The Initial Neural Model (INM) is then used as a starting point for a gradual increase in the number of hidden units. In a validation test involving MI and density values collected over 2 years of operation, the neural model is able to predict these properties with mean percentage errors equal to 0.81% (MI) and 0.04% (density) and determination coefficients equal to 0.970 (MI) and 0.983 (density). The population coefficient estimated in all tests involving grade transitions (0.96) shows a strong linear correlation between the proposed model and laboratory measurements.

Abstract Image

基于神经网络的MI和密度虚拟分析器,采用一种包含构造性算法和初始权值定义的集成策略进行改进
本工作介绍了两个虚拟分析仪(密度和熔体指数(MI))的开发和验证,用于线性聚乙烯(LPE)工业单元的最终产品的质量监测和控制。这两种模型都是基于前馈神经网络,通过一种涉及权重初始估计的策略和一种定义隐藏单元数量的构造算法来改进。初始化策略是对只有一个隐藏单元的神经模型(非线性模型)进行线性化,然后通过最大化其与标准线性回归模型的相似度来优化该模型,该模型的解是解析得到的。然后使用初始神经模型(INM)作为逐渐增加隐藏单元数量的起点。在涉及超过2年运行收集的MI和密度值的验证测试中,神经模型能够预测这些属性,平均百分比误差为0.81% (MI)和0.04%(密度),确定系数为0.970 (MI)和0.983(密度)。在所有涉及等级转换的测试中估计的总体系数(0.96)表明所提出的模型与实验室测量之间存在很强的线性相关性。
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来源期刊
Macromolecular Reaction Engineering
Macromolecular Reaction Engineering 工程技术-高分子科学
CiteScore
2.60
自引率
20.00%
发文量
55
审稿时长
3 months
期刊介绍: Macromolecular Reaction Engineering is the established high-quality journal dedicated exclusively to academic and industrial research in the field of polymer reaction engineering.
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