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    Modeling and analysis of liquid real-time continuous leakage in horizontal liquid ammonia tank
    Juanxia HE Dongmei ZHOU Lei LIU Qiyong ZHOU Liwen HUANG Jianting YAO
    The Chinese Journal of Process Engineering    2021, 21 (6): 731-740.   DOI: 10.12034/j.issn.1009-606X.220171
    Abstract （343）      PDF （492KB）（103）       Save
    Based on Van der Waals equation and theory of fluid mechanics, the liquid real-time continuous leakage model of horizontal liquid ammonia tank was established considering the changes of tank pressure and liquid surface area. Mathematical modeling of a horizontal liquid ammonia tank in a refrigeration company was performed by this model, and the calculation results were compared with PHAST simulation results. The results showed that the decreasing range of liquid level height h grew slowly and then increased quickly, the decreasing range of liquid leakage mass flow rate Qm and liquid leakage rate v and the increasing range of liquid leakage mass m decrease slowly. At the beginning of leakage, Qm and v were the maximum values, at the end of leakage, m was the maximum values. When the diameters of leakage hole were 5, 30 and 100 mm, leakage time t were 29884.027, 837.289, 77.550 s, Qm(max) were 0.552, 19.913 and 221.160 kg/s, v (max) was 46.733 m/s, m(max) were 10255.649, 10339.923 and 10572.760 kg, respectively. The deviation between the calculation results of the model and the PHAST simulation results was less than 24%. From the analysis of parameter variation and risk emergency, the model had the certain applicability for the theoretical calculation of liquid leakage in horizontal liquid ammonia tank.
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    A robust method for chemical process monitoring based on Johnson transformation
    Ji WANG Nan LIU Minggang HU Wende TIAN
    The Chinese Journal of Process Engineering    2021, 21 (12): 1491-1502.   DOI: 10.12034/j.issn.1009-606X.220340
    Abstract （203）      PDF （2151KB）（29）       Save
    A tiny fault in chemical plants is likely to cause an enormous accident possibly with heavy losses of personnel, property, and environment. Therefore, process monitoring is demanded to timely detect faults and identify fault variables, so as to avoid deterioration of tiny faults into accidents. Nowadays principal component analysis (PCA) is the most widely used method in chemical process monitoring practice with its simplicity and effectiveness. However, it has some drawbacks. First, it roughly assumes process data as Gaussian distribution. But sometimes it is not satisfied. Furthermore, PCA uses variance and covariance (also called Pearson correlation coefficients) as criterion to choose principal components, however they are not robust in capturing nonlinear data variation. To alleviate these problems, an improved PCA-a Johnson transformation based robust method for process monitoring (JSPCA) was proposed in this work. First, Johnson transformation was introduced to make process data obey Gaussian distribution. Second, the Spearman correlation coefficient matrix instead of covariance matrix was established to extract principal components and span feature space. Finally, process data were projected into feature space where T2 and SPE statistics were obtained for process monitoring. The proposed method had its fault detection ability tested in the benchmark TE process with comparison of PCA and KPCA. The results showed that the proposed method had higher fault detection rates than PCA and KPCA when using T2 as detection indicator. However, the proposed method with SPE as detection indicator had higher false alarm rates than PCA and KPCA. As for fault diagnosis ability, the proposed method was tested against fault 5 and fault 10 of TE process and diagnoses fault variables more precisely than PCA and KPCA. The proposed method was better than PCA and KPCA and it was worth promoting.
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