Integrated Real-time Fault Diagnosis System For The Process Of PTA Solvent Dehydration

This paper research and solve the problem that the diagnosis is difficult for slow variation fault of the process industry fault diagnosis system, and the analysis for the reason of fault is insufficient. The system combines strengths of lots of artificial intelligence method, based on SDG method for tracing to the fault source. This fault diagnosis and dispose framework contains forecast, diagnosis, expert opinion etc. function.First, this paper present the traditional SDG method model against the process of PTA solvent dehydration, and introduce the trend element ideology on some node of the SDG model, and the chemical industry process data is used to instantly analyzing automatically in the time series order. The fault point with obvious trend and according with the SDG method will be searched the source of fault, spike phenomenon because of production adjustment and error will be removed from fault detection. Meanwhile, the system will forecast fault based on real time data utilizing neural network, furthermore absolute difference cumulation is used for prevention and reminding of the slow change fault and major fluctuations variable, so the slow fault and the unstable variable can be forecasted and alerted before the accident occurred. This system can provide expert guidance based on the source of trouble to get rid of the fault.Based on the practice for the pure terephthalic acid solvent dehydration in some petrochemical factory, this method with a variety of intelligence method improve the completeness of the framework, makes the process of SDG tracing to the fault source more availability and accuracy, decrease the ambiguity, avoid misinformation which brought by system disturbance. One hand this method utilize the cause-and-effect relationship of the model, on the other hand, it can make use of the history data information effectively.