A. Jahedsaravani

TL;DR: In this article, the relationship between the process conditions and the froth features as well as the process performance in the batch flotation of a copper sulfide ore is discussed and modeled.

TL;DR: In this paper, a machine vision system is successfully developed and implemented in a coal column flotation circuit, which can be used for diagnosing the process conditions as well as predicting the process performance at different operating conditions.

TL;DR: The promising results of this study demonstrate the significant potential of deep learning neural networks in froth image analysis, which is of great importance for development of machine vision systems.

TL;DR: In this paper, a watershed algorithm based on whole and sub-image classification techniques is introduced and successfully validated by several laboratory and industrial scale froth images taken under different process conditions.

TL;DR: In this article, a fuzzy controller was designed and implemented to control process performance through the extracted froth features at the desired level by manipulating the selected process variables, and the results indicate that the developed control system is able to handle process disturbances and track reference signals.