关键词: 系统工程, 主元分析法, 偏最小二乘法, 缓变故障, 过程监测

Abstract:

There are some small shifts in the actual chemical production process, which may be resulted by crucial process failure or cause serious problems later on. In the initial stage of this small shift, the process deviation from the normal working conditions is negligible with the appearance of data noise, and it is hard to capture by traditional process monitoring methods. In this work, a process monitoring method based on partial least squares-principal component analysis(PLS-PCA) was proposed. First, the correlation among variables was extracted by PLS method regression. By obtaining the error between the measured value and the predicted value of measurements, the process deviation between the operating state and the pre-set state was amplified. Based on this deviation, the PCA-based process monitoring model was established to realize the early identification of the small shift. A pre-reforming reactor of hydrogen production unit was investigated. The results showed that the small shift, the sulfur content in the diluted steam exceeded the normal level which resulted in catalyst poisoning, could be found at least 13h earlier than human operator and 8h earlier than the traditional PCA-based process monitoring model which could significantly reduce the loss of industrial production.

Key words: systems engineering, principal component analysis, partial least square (PLS), small shift, process monitoring