Sequential Image Restoration and Segmentation for Interface Detection in Primary Separation Cells

The primary separation cell (PSC) plays a key role in bitumen recovery during oil sands extraction. Controlling the froth-middling interface level in PSC is vital for optimal bitumen recovery. Existing sensors for the interface level measurement can be either costly or less reliable. This paper introduces an image restoration algorithm to enhance degraded images of PSC sight glasses followed by an image segmentation technique, which can serve as an alternative for interface level estimation. The restoration algorithm uses a regular state-space model with a skew-t distribution for measurement noise to account for image contamination. States and parameters are estimated using an expectation-maximization (EM) algorithm along with a robust Kalman filter (KF). The restored images are segmented using a Gaussian mixture model (GMM) with Markov random fields (MRF) for interface detection. Experimental results on a lab-scale PSC demonstrate the method’s effectiveness in improving interface level estimation compared to the existing models. Note to Practitioners—Accurate detection and control of the froth-middling interface is crucial for maintaining product quality and maximizing oil recovery in oil sand extraction. This paper presents an innovative approach that combines adaptive image restoration techniques to enhance image quality and a segmentation algorithm for more accurate interface level detection in PSCs. Through rigorous experimentation, our method improves accuracy and certainty in interface detection, offering a valuable solution for enhanced oil recovery processes.