Abstract
Ethylene cracking furnace is the key unit of ethylene production process. Process parameters are adjusted by operator quite often, which leads to the frequent switch of operation status. Meanwhile, the operation condition of ethylene cracking furnace is also affected by the deviation of material conditions, utility supplies and environmental factors. Although the operating conditions of the actual ethylene cracking process changes very frequently, the variation is usually within a certain range, which provides a possibility to the application of statistical model.
In this work, an industrial ethylene cracker is considered. A wavelet filter and moving average method are introduced to differentiate active process adjustment and passive fluctuation. Once a passive deviation is identified, fast alarm will be triggered, together with its possible root cause. In the case of process adjustment, a new monitoring model will be activated. There is a dilemma for on-line process monitoring, as only a limited number of data points are available for a given operating state, while the total amount of historical data is significantly rich. In this situation, historical data under different operational conditions are analysed, and corresponding PCA models are established. 99.6 % similarity was found in obtained covariance matrices. It indicates that the correlation among variables is highly similar among the different operational states. Via appropriate data normalization, the same PCA loading matrix can be used in the different operational conditions to map original data into each principal component space, by which new operation condition can be monitored by statistical model based on currently historical data under most circumstances. PCA model from each operation condition is tested by other operation state. The correct identification rate and the false alarm rate of this method are over 92 % and below 5 %.
