Artificial Intelligence Models to Predict the Influence of Linear and Cyclic Polyethers on the Electric Percolation of Microemulsions
This book chapter presents three predictive models, based on artificial neural networks, to determine the percolation temperature of different AOT microemulsions in the presence of different additives (crown ethers, glymes, and polyethylene glycols), which were developed in our laboratory by differe...
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2020-05-25.
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| LEADER | 02383 am a22002173u 4500 | ||
|---|---|---|---|
| 001 | intechopen_books_9401 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Alonso-Ferrer, Manuel |e author |
| 700 | 1 | 0 | |a Astray Dopazo, Gonzalo |e author |
| 700 | 1 | 0 | |a Mejuto, Juan Carlos |e author |
| 245 | 0 | 0 | |a Artificial Intelligence Models to Predict the Influence of Linear and Cyclic Polyethers on the Electric Percolation of Microemulsions |
| 260 | |b IntechOpen, |c 2020-05-25. | ||
| 500 | |a https://mts.intechopen.com/articles/show/title/artificial-intelligence-models-to-predict-the-influence-of-linear-and-cyclic-polyethers-on-the-elect | ||
| 520 | |a This book chapter presents three predictive models, based on artificial neural networks, to determine the percolation temperature of different AOT microemulsions in the presence of different additives (crown ethers, glymes, and polyethylene glycols), which were developed in our laboratory by different authors. An artificial neural network model has been developed for each additive. The models developed, multilayer perceptron, were implemented with different input variables (chosen among the variables that define the packing or its chemical properties) and different intermediate layers. The best model for crown ethers has a topology of 10-8-1, for glymes the selected topology is 5-5-1, and for polyethylene glycol, the best topology was 5-8-8-5-1. The selected models are capable of predicting the electrical percolation temperature with good adjustments in terms of the root mean square error (RMSE), presenting values below 1°C for glymes and polyethylene glycols. According to these results, it can be concluded that the models presented good predictive capacity for percolation temperature. Nevertheless, the adjustments obtained for the crown ethers model indicate that it would be convenient to study new input variables, increase the number of cases, and even use other training algorithms and methods. | ||
| 540 | |a https://creativecommons.org/licenses/by/3.0/ | ||
| 546 | |a en | ||
| 690 | |a Application of Expert Systems - Theoretical and Practical Aspects | ||
| 655 | 7 | |a Chapter, Part Of Book |2 local | |
| 786 | 0 | |n https://www.intechopen.com/books/9401 | |
| 787 | 0 | |n ISBN:978-1-83881-006-1 | |
| 856 | \ | \ | |u https://mts.intechopen.com/articles/show/title/artificial-intelligence-models-to-predict-the-influence-of-linear-and-cyclic-polyethers-on-the-elect |z Get Online |