Transactions on Machine Intelligence 2821-1693 1 4 2018 12 01 A Model for Extracting the Velocity Features of Fluid Movement in Flotation Cells of the Shahr-e Babak Copper Complex Using the PIV Method 182 190 206008 10.47176/TMI.2018.182 EN M. Eghbali Fard MSc Student, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran M. H. Gholizadeh Assistant Professor, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran 0000-0003-4337-5067 H. Ghayoumizadeh Assistant Professor of Biomedical Engineering, Vali-e-Asr University of Rafsanjan 0000-0002-5390-3938 H. Fatehi Marj Assistant Professor, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran 0000-0002-1634-8197 Journal Article 2018 04 06 In this study, the flotation process is examined as a conventional method for processing low-grade copper sulfide ores. The performance of this process is influenced by various factors, including the type and dosage of collector, frother, pH regulator, activator, and depressant. Identifying effective reagents and determining optimal conditions are crucial for enhancing efficiency, and modeling and simulation of this process can further aid in its improvement. Traditionally, experienced operators assess and control the process based on the visual characteristics of the froth. However, with advancements in technology, machine vision has emerged as a valuable tool for monitoring and controlling flotation circuits. In this context, a machine vision system was installed on a flotation cell in the rougher circuit of the Shahr-e Babak Copper Flotation Plant to monitor the process under various conditions. The primary visual feature extracted from the captured images is the bubble velocity on the froth surface, which directly impacts flotation performance. In this research, the Particle Image Velocimetry (PIV) technique was employed to determine bubble velocities without the need for additional particles or lasers. Simulation results demonstrate that the proposed method exhibits high accuracy in providing relevant velocity features, making it a potent tool for monitoring and optimizing the performance of flotation cells. https://www.tmachineintelligence.ir/article_206008_9cbd8c93230ba86ce6c3d13ba9604ed2.pdf