Quantitative composition-property modelling of rubber mixtures by utilising artificial neural networks

被引:13
作者
Borosy, AP [1 ]
机构
[1] Tech Univ Budapest, Fac Chem Engn, Dept Chem Informat Technol, H-1119 Budapest, Hungary
关键词
artificial neural network; adaptive learning rare; quantitative composition-property modelling; rubber; multivariare optimisation;
D O I
10.1016/S0169-7439(98)00212-3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A significant opportunity exists to improve operations and resulting profitability by streamlining the formulation design task. Artificial Neural Network (ANN) approximation addresses this opportunity that is most useful in an environment where theoretical descriptions are difficult to obtain, but partial knowledge about the process is known and input-output data are available. Quantitative relationships between the composition (and process variables) of formulation and the physico-chemical properties of the product are modelled by an Adaptively Learning Artificial Neural Network (ALANN). The trained ALANN is then used as an interpolating function to estimate product performance when given specific formulations and processing requirements (direct modelling). The trained ALANN is also used as the object function of a Nelder-Mead simplex to optimise formulation and processing to accomplish desired product characteristics (inverse modelling). ALANN was compared to another application by using data from rubber industry. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:227 / 238
页数:12
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