Fuzzy cognitive approach of a molecular distillation process

Lima, Nádson Murilo Nascimento; Liñan, Lamia Zuñiga; Manenti, Flavio; Filho, Rubens Maciel; Maciel, Maria Regina Wolf; Embiruçu, Marcelo; Medina, L.C.

doi:10.1016/j.cherd.2010.08.010

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…Moreover, the simulation of the molecular distillation process of heavy residues have reported good results when Eq. (5) was used on mathematical modeling to predict the molecular weight of the distillate stream of the process (Zuñiga, 2009;Lima et al, 2010 …”

Section: Discussionmentioning

confidence: 99%

Correlation for predicting the molecular weight of Brazilian petroleum residues and cuts: An application for the simulation of a molecular distillation process

Liñan

Lima

Maciel

et al. 2011

Journal of Petroleum Science and Engineering

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Section: Discussionmentioning

confidence: 99%

Correlation for predicting the molecular weight of Brazilian petroleum residues and cuts: An application for the simulation of a molecular distillation process

Liñan

Lima

Maciel

et al. 2011

Journal of Petroleum Science and Engineering

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“…On the other hand, an ANN model does not give any kind of information about the particular phenomenon, but, with an appropriate training of the network, it can predict the effects of variables, such as the speed of the shake mechanism (Shao et al, 2007;Li et al, 2011). In addition, the artificial neural network constitutes a useful tool for modelling the process of molecular distillation with great precision but it needs a great amount the experimental data and does not give information about a determined effect (Shao et al, 2007;Haykin, 2009;Lima et al, 2011).…”

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confidence: 99%

The use of artificial neural network modeling to represent the process of concentration by molecular distillation of omega-3 from squid oil

Rossi¹,

Gayol²,

Renaudo³

et al. 2014

Grasas y Aceites

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SUMMARY:The concentration of omega-3 compounds obtained for the esterification of squid oil by molecular distillation was carried out in two stages. This operation can process these thermolabile and high molecular weight components at very low temperatures. Given the mathematical complexity of the theoretical model, artificial neural networks (ANN) have provided an alternative to a classical computing analysis. The objective of this study was to create a predictive model using artificial neural network techniques to represent the concentration process of omega-3 compounds obtained from squid oil using molecular distillation. Another objective of this study was to analyze the performance of two different alternatives of ANN modeling; one of them is a model that represents all variables in the process and the other is a global model that simulates only the input and output variables of the process. The alternative of the ANN global model showed the best fit to the experimental data. KEYWORDS:Artificial Neural Networks; DHA; EPA; Molecular Distillation; Omega-3 RESUMEN: Uso de modelos de redes neuronales artificiales para representar el proceso de concentración por destilación molecular de omega-3 proveniente de aceite de calamar. La concentración de compuestos omega-3, obtenidos de la esterificación de aceite de calamar, por destilación molecular fue llevada a cabo en dos etapas. Esta operación permite procesar componentes termolábiles y de alto peso molecular a muy bajas temperaturas. Dada la alta complejidad de los modelos teóricos, las redes neuronales artificiales (RNA) conforman una alternativa al análisis computacional clásico. El objetivo de este estudio fue crear un modelo predictivo usando modelos de redes neuronales artificiales para representar el proceso de concentración de compuestos omega-3 obtenidos del aceite de calamar por destilación molecular. Otro objetivo de este estudio fue analizar el desenvolvimiento de dos alternativas de modelos RNA; uno de ellos es un modelo que representa todas las variables en el proceso y otro es un modelo global que simula solo las variables de entrada y de salida del proceso. La alternativa de un modelo RNA global mostró el mejor ajuste de los datos experimentales.

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“…流程工业智能制造中的知识表示定义了流程工业中知识的表现形式, 知识表示方法依据后期知识处理中所采用的方法不同而不同. 当前知识表示常用的方法主要有关联规则表示法 [24,25] 、模糊认知图表示法 [26,27] 、动态不确定因果图 [28,29] 、Petri 网表示法 [30] 、语义网络表示 [31] 、面向对象表示法 [32] 、框架表示法 [33] 等. 知识推理作为流程工业知识处理的重要环节, 一直备受关注.…”

Section: 知识处理unclassified