Modeling the Temperature of the Evacuation Chamber with Artificial Neural Networks
Journal Title: Advances in Robotics & Mechanical Engineering - Year 2018, Vol 1, Issue 2
Abstract
This investigation approaches the artificial neural networks applied to the ore drying process in carbonate-ammonia leaching. To carry out this research, the main variables that characterize the process were identified. Besides, it was collected the data that comprise a whole month of facility´s operation. Furthermore, it was developed a regression analysis backwards, step by step, which allowed to determine that the linear correlation coefficient did not reach values higher than 0,62. In addition, it was pinpointed a two layered feed - forward back propagation neural network to model the temperature. Thins one reached the correlation coefficient values of 0,97 during its training and 0,95 in validation, as well as 0,87 in its generalization. In a global context, nowadays, modern control systems play a fundamental role when developing solutions to issues or problems presented in domestic and industrial applications. The main contributions of modern control systems at industrial level contribute to technological innovation, profitability and maintainability of the controlled processes. Within the advanced control strategies under investigation to automate complex processes are: adaptive control, predictive control based on models, robust control, and intelligent control, among others. Intelligent control relies on several techniques such as: fuzzy logic, evolutionary algorithms, and artificial neural networks. Artificial neural networks can be used effectively and accurately for modeling systems with complex dynamics, especially for nonlinear processes that vary over time. The growing interest in neural networks is due to its great versatility and the continuous advance in network training algorithms and hardware.
Authors and Affiliations
Deynier Montero Gongora, Ramon Alpajon Videaux, Keiler Cobas Cardoza
Keywords
Related Articles
- EP ID EP580541
- DOI 10.32474/ARME.2018.01.000107
- Views 79
- Downloads 0
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