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- Title
Development of Mathematical Model Based on Artificial Neural Network to Predict Density in Polymerization Process of Styrene.
- Authors
Valim, Isabelle C.; Silva, Alessandra M. M.; Grillo, Alexandre V.; dos Santos, Brunno F.
- Abstract
In the chemical industry is important to control the process in order to guarantee the quality and repeatability of the final product. Using sensors in the industrial plant allows a large volume of data to be captured regarding the process. These data can be used for modelling to better understanding and predict the properties of the product in the process. In this work, two types of Artificial Neural Networks (ANN) and the hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS) were used to predict the density of polystyrene along the styrene polymerization process. The dataset used was extracted from the batch of polymerization reactions performed in open-loop, manual control and closed-loop and monitored in each 5 seconds. The Feedforward and Elman ANN has coefficient of correlation (R) equal 94.2%. However, the best topology obtained to Feedforward ANN presents 2 hidden layers and error index RMSE (Root Mean Squared Error) equal to 2.69x10-2. The Elman ANN presents only 1 hidden layer and RMSE of 3.39x10-2. The ANFIS model, in turn, presented R equal to 91% and RMSE of 0.2123. Therefore, ANFIS model did not prove to be the most adequate for the prediction of the polystyrene density in the studied process.
- Subjects
STYRENE; POLYMERIZATION; ARTIFICIAL neural networks; CHEMICAL industry; CHEMICAL detectors; CHEMICAL processes
- Publication
CET Journal - Chemical Engineering Transactions, 2019, Vol 74, p751
- ISSN
1974-9791
- Publication type
Article
- DOI
10.3303/CET1974126