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Classification for raw oil inlet conditions of hydrocracking process based on ratio cluster

Hydrocracking process is a complex long-running industrial process. The change of inlet conditions will result in fluctuation of operating parameters and change of running state. It is necessary to classify the inlet conditions to detect the change of raw materials timely, so that the subsequent global operation conditions can be analyzed. However, sampling analysis of the quality indexes of raw oil is irregular and there is a certain hysteresis. It is difficult to realize the inlet condition classification directly by sampling test of the quality indexes of raw oil. Thus, in this paper, a classification method for raw oil inlet conditions is proposed for hydrocracking process based on ratio cluster. Through the component analysis of raw oil, an improved k-means algorithm based on ratio distance is proposed by using the measurable flow rate of feed stocks. Thereby, after the outliers are eliminated by the improved k-means algorithm, the inlet conditions are classified according to the flow rate of feed stocks instead of the quality indexes of raw oil. The similarity between classes and the coincidence rate are adopted to assess the classification results. Simulation results based on practical industrial data indicate that the proposed method could ensure the classification accuracy of raw oil inlet conditions even if there are only a few raw oil index data and their quality measurements are delayed. This is significant for on-line analysis and dynamic adjustment of the hydrocracking process.