Generative adversarial network-based image-level optimal setpoint calculation for flotation reagents control

Froth flotation is a vital mineral concentration process. Because of fluctuations in feed conditions of flotation processes, adaptively adjusting the setpoint in computer vision-based flotation reagent control is important to maintain the economic optimum in production. However, due to the high-dimension property of deep learning image features, it may be difficult to calculate a deep learning image feature-based setpoint that can satisfy the kinetic of flotation processes. Different from existing feature-level setpoint calculation methods, this study investigates image-level optimal setpoint calculation, and a generative adversarial network-based setpoint calculation model (SetpointGAN) is developed. Besides the widely used generative adversarial loss, we propose a feature consistency loss to encourage the visual consistency between synthesized setpoints and the ground-truth setpoint, and a feed consistency loss to guarantee the control attainable of calculated setpoints. Compared to feature-level setpoint calculation methods, the results of SetpointGAN can be intuitively evaluated by comparing it to collected real froth images of optimal flotation statuses. Experiments evaluated using real zinc flotation data demonstrate the effectiveness of the proposed SetpointGAN and its advantages over existing works.

Automated summary

This study proposes a SetpointGAN model based on generative adversarial network for calculating optimal setpoints in computer vision-based flotation reagent control. By introducing feature consistency loss and feed consistency loss, the model can better maintain the visual and control consistency between synthesized setpoints and ground-truth setpoints. Experimental results demonstrate the effectiveness of SetpointGAN and its advantages over existing methods.