Viskositas is a commercial software with a deep neural network and has better efficiency when compared to classic equations, neural networks in literature and other commercial software and uses the values of chemical composition and temperature to predict the viscosity value of the chemical system at the given temperature.
The database consists viscosity data with chemical composition and temperature of each sample and experimental test carried out, through tens international scientific articles composing a database with 19 different chemical species. The database has a range up to log₁₀ η=8 (or log η≈18.42) (log = logₑ) in fully liquid chemical systems.
There is a wide range of temperature in industrial processes, such as in the production, refining and solidification of steel, refining, homogenization and molding of glass and geophysical systems, for example.
All references used in mathematical and computational modeling, database, data analysis and computational model construction can be seen in the work of Duchesne et al. (2013), Chen et al. (2021) and Anjos (2021).
Through a database with different chemical composition data, temperature and viscosity measured experimentally, a preprocessing was carried out relating chain depolymerization (NBO/T) and liquidus temperature (Tliq) parameters (Vargas et al., 2001). A database for training, validation and testing was separated. The test dataset was NOT used for training the neural network.
The test database can be seen at the link: https://docs.google.com/spreadsheets/d/1D2P586GdPxGDtiExOhSxcPoetDIa8basNCdQz-0xWNI/edit?usp=sharing.
With the test database (tab test_data) predictions were performed in Viskositas 1.x, 12 literature models and FactSage® 7.2 software (tab predictions). Viskositas 1.x showed lower mean absolute error (MAE), lower standard deviation of error and higher coefficient of determination (R²) in relation to models S2, Watt-Fereday, Bomkamp, Riboud (Vargas et al., 2001), Duchesne, ANNliq (Duchesne et al., 2013) and FactSage® 7.2 (tab metrics).
| Metrics/ Models | Shaw | Watt-Fereday | Bomkamp | Riboud | Duchesne | ANNliq | Viskositas 1.0 | FactSage® 7.2 |
|---|---|---|---|---|---|---|---|---|
| Mean Absolute Error (log η) | 1.4217 | 1.5558 | 3.9684 | 2.8301 | 2.8027 | 11.6147 | 0.2309 | 0.8112 |
| Standard Deviation (log η) | 1.6857 | 1.82183 | 2.2953 | 1.9337 | 2.3038 | 4.8249 | 0.5446 | 1.3348 |
| Coefficient of Determination (R²) | 0.6452 | 0.6068 | 0.5395 | 0.5297 | 0.1611 | 0.1419 | 0.9864 | 0.8212 |
*η - Pa.s
The Viskositas 1.x model is a deep neural network with arbitrary depth built using the minimum width equation developed by Kidger and Lyons (2020) to the continuous function classes endowed with an uniform norm.
A pipeline was built with a preprocessing using standard scaler and the deep neural network developed through Keras (TensorFlow backend). The EarlyStopping technique was used to avoid overfitting. Training and validation graphs, computational model graph, histograms and parameters distributions can be seen at the link: https://tensorboard.dev/experiment/iqwQDtyJReWnzB2CkvUJIg/#scalars
Viskositas is a GUI application available for download (versions > 1.x in the full version, see Contact to make a specialized budget). The full version has customization, specialized technical assistance and Multi-Viskositas, an option capable of predicting in seconds the viscosity of thousands/millions chemical compositions at specified temperatures. The Viskositas 0.x and/or Viskositas 1.x Lite can be downloaded in Versions folder.
- Anjos PQ. Viskositas: Viscosity Prediction of Multicomponent Chemical Systems. arXiv. 2022. https://arxiv.org/abs/2208.01440
- Site: https://www.patrickdosanjos.com/
- E-mail: patrick.dosanjos@outlook.com
- Linkedin: https://www.linkedin.com/in/patrick-queiroz-dos-anjos/
© 2021, Patrick Queiroz dos Anjos. Licensed under the MIT License.
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Duchesne M, Bronsch A, Hughes R, Masset P. Fuel. 2013;114;38-43. http://dx.doi.org/10.1016/j.fuel.2012.03.010
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Chen Z, Wang M, Meng Z, Wang H, Liu L, Wang X. Ceramics International. 2021;47;30691-30701. https://doi.org/10.1016/j.ceramint.2021.07.248.
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Anjos PQ. Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo. 2021;51-52. Portuguese. https://repositorio.ifes.edu.br/handle/123456789/1195
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Vargas S, Frandsen FJ, Dam-Johansen K. Progress in Energy and Combustion Science. 2001;27;237±429. https://doi.org/10.1016/S0360-1285(00)00023-X
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Kidger P, Lyons T. Proceedings of Machine Learning Research. 2020;125;1–22. https://arxiv.org/abs/1905.08539
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Anjos PQ. Viskositas: Viscosity Prediction of Multicomponent Chemical Systems. arXiv. 2022. https://arxiv.org/abs/2208.01440