Editorial for Special Issue “Design, Modeling, Optimization and Control of Flotation Process”

Flotation is a significant and widely used processing technique, effectively separating valuable and gangue minerals. Mineral producers have long sought ways to consistently monitor and control the flotation process to ensure optimal conditions for effective mineral separation. However, establishing such a sophisticated control system entails significant financial investments in equipment, resulting in substantial costs. Additionally, ongoing maintenance needs to be performed to uphold the system’s high standards, which will incur additional expenses. To address these challenges, predictive models emerge as effective and economically viable solutions to handling the intricacies of the flotation process. In the flotation process, numerous linear and nonlinear relationships exist between the operating parameters, chemical reagents, and minerals. These connections can be evaluated using various experimental and numerical methods. Over recent decades, a variety of intelligent computing and statistical techniques, including machine learning, genetic algorithms, artificial neural networks, fuzzy systems, and image processing, have been employed to predict the flotation process outcome and facilitate the process control.