Enhancing Oil Processing Efficiency with Kalman Filter-based Middlings Interface Detection
Posted on October 18, 2023 Big Data Machine Learning & AI
The separation process in the oil sands industry involves extracting bitumen from ore that is primarily made up of clay and sand. One of the key vessels in this process is the primary separation cell (PSC) or primary separation vessel (PSV). The separation process involves the mixing of ore with hot water and chemicals, upstream of the PSC/PSV, to create a slurry of “aerated bitumen”, solids, and water. This slurry is fed into the PSC/PSV where, ideally, the aerated bitumen rises to the top and all the solids fall to the bottom.
The efficient operation of the PSC/PSV is critical for optimal production. A significant challenge lies in accurately detecting the middlings interface, which lies below the bitumen (froth) layer. Traditional methods often rely on single sensors, making them susceptible to noise and uncertainties that can impact the accuracy of the interface detection.
The Kalman filter offers a potential solution to address this challenge. By fusing data from multiple sensors, including pressure transmitters, level sensors, density meters, and cameras, the Kalman filter can obtain a more accurate and robust estimation of the middlings interface position. This sensor fusion approach leverages the strengths of each sensor, compensating for their individual limitations and ensuring accurate and reliable estimates.
The Kalman filter’s ability to handle noise and uncertainties is particularly valuable in real-world applications. The filter takes into account the known characteristics of sensor noise and the system dynamics. As new sensor measurements become available, the Kalman filter updates its internal state estimate and error covariance matrix in real-time, allowing the system to adapt promptly to changes in the process.
Using the Kalman filter for sensor fusion provides a cutting-edge solution that can increase bitumen froth quality; its ability to accurately estimate the interface level in real-time empowers the oil sand industry to make data-driven decisions, enhancing productivity and profitability.