文章基本信息

标题：Sensor Fusion and Computer Vision Integrated System for Primary Separation Vessel Interface Level Estimation
本地全文：下载
作者：Jingyi Wang ; Yousef Alipouri ; Biao Huang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：3
页码：170-175
DOI：10.1016/j.ifacol.2021.08.237
语种：English
出版社：Elsevier
摘要：AbstractIn 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.
关键词：KeywordsLinear parametrically varying (LPV) methodologiesSoftware for system identificationAutotuningIterative modellingcontrol designAdaptationlearning in physical agents