Processing, June 2020
The graph in the middle of the lower row of Figure 3 shows the degree of abnormality and it can be seen that the degree is gradually increasing The chart on the lower right of Figure 3 shows the contribution of each process variable causing the abnormality When corrosion and clogging occur in piping associated with a distillation column separation performance decreases and production is hindered Although it is possible to detect the situation in which the abnormality occurs by observing an increase of differential pressure or changes in many other process variables the method described above can be used to rank the contribution of each variable to the problem producing more useful results Quality prediction modeling Product quality metrics are often not available in real time because samples must be pulled and analyzed in a lab For example product quality may vary due to differences in raw materials and environmental conditions but it takes time to obtain lab results Therefore a model to predict product quality in real time based on process data is valuable because results can be acted upon before large amounts of offspec product are produced These techniques can be used to predict product quality problems before they occur by examining multiple process variable inputs and applying a linear regression model or a nonlinear machine learning model However a problem arises because prediction models are typically created from past data The state of the process is different from the state when the model was taught causing prediction value shifts For example an offset can occur in the prediction model due to degradation of catalyst in a reaction process changes in the raw material composition and other disturbances To address this issue the prediction value can be corrected by referring to the recent prediction error based on the assumption that the prediction value shifts temporarily To further improve prediction based on the assumption that the error of the model 16 Processing JUNE 2020 Figure 3 Equipment abnormality detection and factor ranking Figure 4 Improvement of prediction accuracy by applying a JIT model
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