AI-DeepFrothNet: Continuous monitoring and tracking of froth flotation working condition by root cause analysis and optimized predictive control

Abstract

Achieving optimal working conditions in froth flotation is critical for maximizing mineral recovery. Traditional manual observation methods are limited by subjectivity and the inability to adapt to changing production environments. Many existing approaches do not provide a clear picture of the flotation behavior's root cause, which directly impacts the grade recovery rate. In this study, we proposed an AI-DeepFrothNet solution to address the prevailing challenges. The proposed work utilized a Putrefaction Enrichment and Tuning Network (PETNET) to eliminate and adjust the noise in the Red, Green, and Blue (RGB) images. Using a Skipped Attention Gated Recurrent Unit (SkA-GRU) for RGB to Hyper Spectral Image (HSI) conversion ensured the preservation of the local and global features. The pre-processed frames were subjected to frame-by-frame analysis using the You Look Only Once-V7 (YOLO-V7). To identify a root cause, the proposed research utilized a Multi-Agent Deep Q Learning (MA-DQL) solution, in which three agents were involved in analyzing the different conditions and properties of the froth layer. To ensure the quality and stability of the mineral outcome, the optimized controller comprehended the root cause control variables and optimized their values using the Gazelle Optimization Algorithm (GOA) logic. The proposed work demonstrated superior performance compared to existing methods and achieved 93 % accuracy, 96 % precision, 95 % recall, and 87 % F1 score, outperforming other methods.