Computer Vision Based Interface Sensors for Oil Sands Primary Separation Vessel

Abstract: In the oil sands extraction process, bitumen (crude oil) is separated from the sands in the Primary Separation Vessel (PSV) through a water-based gravity separation process. The interface between froth (crude oil) and middlings (water and sand) is the most important control variable in the PSV operation. Bitumen recovery and downstream operations are critically dependent on interface level measurement and control. Most of the traditional PSV level instruments have de_cient service factors, limiting the implementation of automatic control. Therefore, we proposed novel and robust computer vision based methods to estimate the froth-middlings interface level on video frames captured from a PSV's sight glasses camera.The first chapter of the thesis discusses the computer vision as a knowledge basis for the proposed work. Typical image processing and analysis methods are described, and they provide the foundation for the subsequent chapters.The subsequent chapters propose several approaches for the interface level detection. As the first approach, we present the froth-middlings interface level detection on single frames (static image processing). The level is detected based on edge detection performed on the frames, in which traditional filters are proposed to smooth the images through model-based image restoration.Next, we develop and implement a robust computer vision based method to estimate the froth-middlings interface level in PSV, in which we additionally consider the dynamics of a set of consecutive frames to improve the level estimation. The algorithm processes the online video frames of a camera mounted on PSV sight glasses, and the level is detected based on a combined operation of edges and motion detection in a set of consecutive frames (static and dynamic image processing). In addition, the algorithm uses reliability analysis to detect the environmental conditions that may limit the level estimation, and a time-based sliding window analysis of the level measurements is proposed. Industrial application results show that the proposed computer vision algorithm is more accurate and reliable when compared to other instruments, as well as more robust against the process and environmental abnormalities.Finally, advanced filters with finite impulse response (FIR) structure are proposed and developed to improve the image restoration and object tracking process. We address the problem of smoother design for state estimation based on a finite number of measurements collected over a finite horizon. Three different FIR smoothing algorithms are proposed using the maximum likelihood FIR estimation, which is robust against uncertain noise statistics and modeling parameters. The FIR algorithms are applied to the image restoration and level tracking problem in PSV, and they show better robustness against modeling uncertainties than traditional IRR filtering approaches.