关键词: 油水界面, 测量, 自适应阈值, 聚类中心, 算法, 优化

Abstract:

Accurately detecting the oil-water interface and the liquid level in a crude oil tank is very important for petrochemical process system engineering related to the accuracy of the net oil moisture control and that of combination station check system metering. In order to solve the problems that the conventional classification algorithm needs to select typical values manually and the traditional K-means clustering algorithm also needs to select initial clustering centers manually, and the results might not be accurate and stable, this paper proposed an adaptive threshold algorithm for oil-water interface detection, which is based on improved K-means clustering optimization algorithm. The proposed algorithm can calculate the best typical values automatically, and improves the conventional classification algorithm and the traditional K-means clustering algorithm. The proposed algorithm works as follows: first, find the typical values automatically by using an adaptive threshold searching algorithm, then search the optimal points by using the improved K-means clustering optimization algorithm, and finally calculate the oil-water interface and the liquid level according to the optimal partition. Experimental results showed that, compared with the conventional classification algorithm and the traditional K-means clustering calculation algorithm, the proposed adaptive threshold clustering algorithm does not need artificial selection, and can guarantee the results to be correct. Moreover, the number of iterations and running time needed by our proposed algorithm are smaller than those by the traditional and other improved K-means clustering calculation algorithms.

Key words: oil-water interface, measurement, adaptive threshold, cluster center, algorithm, optimization