Studies On RegFlow Modeling And Fault Diagnosis Method Of Complex Chemical Processes

The chemical processes involve a wide variety of variables, process equipments and chemical reactions. Fault diagnosis of chemical process has become a big chanllenge because of complex relationships between variables and complex process mechanism. Recent trends of the study of chemical process fault diagnosis are towards complex system modeling and the integration of qualitative and quantitative models.A novel modeling method called Regulating Flow Graph (RegFlow Graph, RFG) was proposed in this dissertation in attempt to solve the problem of fault detection and diagnosis in complex chemical processes. Formal definitions of RFG, quanlitative RFG and qualitative simulation system of RFG were presented, which were based on the framework of entity grammar system.Three RFG modeling methods were developed, namely, process flow diagram-based approach (FDBA), mathematical model-based approach (MMBA) and continuous monitoring data-based approach (MDBA). FDBA can be used to build a qualitative RFG of chemical process. MMBA can be used to build a quantitative RFG. The combination of MDBA and partical swarm algorithm can be used to build a phenomenalogical model of chemical process.For the transformation between qualitative RFG and quantitative RFG, the general dynamical equation for RFG was deduced. Together with the S-system dynamics equation, two transformation methods were proposed. The applications of three RFG modeling methods in CSTR system were explored.The techniques on RFG qualitative simulation, RFG-based fault detection and RFG-based fault diagnosis were developed in the later charpters. To verify and illustrate these techniques, CSTR process, TE process and crude oil distillation process were tested as case studies.Based on the qualitative simulation systems of RFG, the automatic reasoning system of RFG was implemented using Datalog language. The problems of rings, multi-channel regulation and multiple simulation have been effectively solved.For RFG-based fault detection, faults can be identified by the difference between RFG simulated data and monitoring data. The complexity and large computional requirement of traditional mathematical models have limited their application in fault detection. The phenomenological model obtained using MDBA is simple and the caculation is fast, which can overcome the drawbacks of traditional fault detection methods. The fault disgnosis based on RFG can be accomplished using RFG qualitative simulation, which can shows the propagation path of each fault and the effect of faults on each observerd variable. The simulation results can be used to establish fault diagnosis model with data mining method. Because of the complexity of chemical processes, back-forward reasoning strategy in falut disgnosis often leads to uncertainty results and combinatorial explosion problem. The combination of RFG qualitative simulation and data ming can partly solve these problems.This dissertation has explored the relationships between RFG and the related theories and approaches, such as system dynamics and SDG modeling method. The relathionships between fault diagnosis in chemical processes and emergence in complex systems was also analysised. As a novel modeling method, RFG has the formal features of entity grammar system and contains the advantages of related modeling methods. It has overcome the limitions of traditional research methods. The integration of qualitative simulation technology, data mining technique and parameter estimation method in RFG has provided a feasible scheme for fault detection and fault diagnosis in complex chemical processes.