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Sensor Fusion and Computer Vision Integrated System for Primary Separation Vessel Interface Level Estimation

机译：用于初级分离血管界面级别估计的传感器融合和计算机视觉集成系统

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摘要

In oil sands industry, primary separation vessel (PSV) is a critical component to recover bitumen from oil sands slurry. Accurate interface level estimation between froth and middlings layers ensures economical and environmental benefits of bitumen recovery. Nuclear density profiler, differential pressure (DP) cell, and image processing based computer vision system are usually used to estimate the interface level. The computer vision system, which uses a camera to capture sight glass vision frames, is considered to be the most accurate. Although the accuracy of computer vision system is high in normal operational conditions, its qualities are influenced by abnormalities, such as sight glass vision blocking, stains, and level switching between sight glasses. A sensor fusion approach, which recursively updates fusion parameters according to accurate computer vision results whenever they are reliable, is proposed. The fused results can then be used to provide reliable interface level estimation under abnormal scenarios. The sensor fusion algorithm is further integrated with computer vision system to improve froth-middlings interface level estimation accuracy and robustness. Industrial environment simulations and factory accepted test (FAT) demonstrate the advantages and effectiveness of the sensor fusion and computer vision integrated system, which is applied in the industry.
机译：在油砂产业中，初级分离容器（PSV）是从油砂浆料中恢复沥青的关键组分。泡沫和中间层之间的准确界面级别估计可确保沥青恢复的经济和环境效益。核密度分析器，差压（DP）单元和基于图像处理的计算机视觉系统通常用于估计界面电平。使用相机捕获视线玻璃视觉框架的计算机视觉系统被认为是最准确的。尽管计算机视觉系统的准确性在正常运行条件下高，但其质量受到异常的影响，例如视力玻璃视觉阻挡，污渍和视镜之间的水平切换。一种传感器融合方法，可以根据精确的计算机视觉结果递归更新融合参数，只要它们可靠，都是可靠的。然后可以使用融合结果来在异常场景下提供可靠的接口电平估计。传感器融合算法进一步与计算机视觉系统集成，以提高泡沫中间界面级别估计精度和鲁棒性。工业环境模拟和工厂接受的测试（FAT）展示了传感器融合和计算机视觉集成系统的优缺点，该系统适用于该行业。

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