Nordic Process Control Workshop, Porsgrunn, Norway Application of the Enhanced Dynamic Causal Digraph Method on a Three-layer Board Machine Cheng Hui, Vesa-Matti Tikkala, Sirkka-Liisa Jämsä-Jounela Helsinki University of Technology Faculty of Chemical Technology and Material Sciences Research group of Process Control and Automation

Nordic Process Control Workshop, Porsgrunn, Norway Table of Contents 1.Introduction 2.Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 3.Application of the EDCDG on an Industrial Process 4.Description of the Process and the Test Environment 5.Fault Diagnosis Results 6.Conclusions

Nordic Process Control Workshop, Porsgrunn, Norway 1. Introduction 1.1 Background Fault diagnosis methods are needed in the paper industry due to complex processes and problems caused by the process faults Fault diagnosis based on causal digraph methods has been researched since 1970’s

Nordic Process Control Workshop, Porsgrunn, Norway 1. Introduction 1.2 Aim of the Research The aim of this study was to test the enhanced dynamic causal digraph method on a board machine simulator by simulating real industrial fault scenarios –Four fault scenarios were selected, one is presented in here

Nordic Process Control Workshop, Porsgrunn, Norway Table of Contents 1.Introduction 2.Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 3.Application of the EDCDG on an Industrial Process 4.Description of the Process and the Test Environment 5.Fault Diagnosis Results 6.Conclusions

Nordic Process Control Workshop, Porsgrunn, Norway 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.1 Fault Detection and Isolation Fault diagnosis using EDCDG method is based on the process models and the causal structure of the process –Method performs the detection by observing residuals generated by comparing model outputs and process measurements –The isolation is carried out by an inference mechanism which evaluates the residuals and forms a propagation path of the fault effect according to the causal structure

Nordic Process Control Workshop, Porsgrunn, Norway 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.2 Dynamic causal digraph model Describes the cause-effect relationships between the process variables –The structure of the causal digraph is based on the prevailing causal dependencies in the process –The cause-effect models can be described by any mathematical model type Cause-effect model Pump rotation speed Valve opening Mass flow rate Tank level Cause-effect model

Nordic Process Control Workshop, Porsgrunn, Norway 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.3 Fault Detection Faults are detected by observing the global residuals generated by the comparison of measurements and simulation values Cumulative sum algorithm is used for detection

Nordic Process Control Workshop, Porsgrunn, Norway 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 2.4 Fault Isolation The origin of the fault can be located by a recursive evaluation of local residuals –Total local residual (TLR) –Individual local residuals (ILR) –Multiple local residuals (MLR) The nature of the fault can be also inferred from the residuals

Nordic Process Control Workshop, Porsgrunn, Norway 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method In case of a process fault a further step can be taken in order to isolate the actual process component causing the fault –There are 2 n -1, where n is number of input variables, possibilities for the cause A solution is to introduce process knowledge in a form of a matrix M –It describes the relationships between the arcs of the digraph

Nordic Process Control Workshop, Porsgrunn, Norway 2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method The consistency between the suspected sets of arcs and process knowledge is checked suspected arcs sets knowledge matrix inference decreased number of suspected arcs sets

Nordic Process Control Workshop, Porsgrunn, Norway Table of Contents 1.Introduction 2.Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 3.Application of the EDCDG on an Industrial Process 4.Description of the Process and the Test Environment 5.Fault Diagnosis Results 6.Conclusions

Nordic Process Control Workshop, Porsgrunn, Norway 3. Application of the EDCDG method on an Industrial Process A procedure for offline testing: –Process study –Causal digraph modeling –Fault simulations/data collection –Fault diagnosis testing

Nordic Process Control Workshop, Porsgrunn, Norway Table of Contents 1.Introduction 2.Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 3.Application of the EDCDG on an Industrial Process 4.Description of the Process and the Test Environment 5.Fault Diagnosis Results 6.Conclusions

Nordic Process Control Workshop, Porsgrunn, Norway 4. Description of the Process and the Test Environment The process comprises a three-layer board machine, which has two stock preparation parts and three short circulations The process was simulated in APROS simulation environment

Nordic Process Control Workshop, Porsgrunn, Norway Table of Contents 1.Introduction 2.Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 3.Application of the EDCDG on an Industrial Process 4.Description of the Process and the Test Environment 5.Fault Diagnosis Results 6.Conclusions

Nordic Process Control Workshop, Porsgrunn, Norway 5. Fault Diagnosis Results 5.1 The causal digraph model of the process A causal digraph model of the board machine was constructed –55 variables in total –To describe to cause-effect relationships various model types were used First principles models Static regression Neural networks –Also the knowledge matrix M was obtained

Nordic Process Control Workshop, Porsgrunn, Norway 5. Fault Diagnosis Results 5.2 Hydrocyclone plugging Hydrocyclones are used for cleaning of the stock –In some cases they are vulnerable to plugging A fault in the hydrocyclone cleaning plant causes –Decreased cleaning efficiency –Extra pressure loss

Nordic Process Control Workshop, Porsgrunn, Norway 5. Fault Diagnosis Results The fault was simulated with the board machine model in two operation points Global residuals were generated based on the simulation values and the measurements –5 detected GRs

Nordic Process Control Workshop, Porsgrunn, Norway 5. Fault Diagnosis Results Local residuals (TLR, ILR, MLR) were generated for the detected variables –The fault origin was located: total of three origins acceptcon2 headcon2 headflow22 Fault was identified as a process fault

Nordic Process Control Workshop, Porsgrunn, Norway 5. Fault Diagnosis Results Three nodes with 4, 3 and 2 input arcs, respectively, resulted as: sets of suspected arcs Process knowledge matrix M was used to decrease the number of them –Each set was tested by –If the above equation holds, the set can be accepted as a possible result The number of possible results decreased to 5

Nordic Process Control Workshop, Porsgrunn, Norway 5. Fault Diagnosis Results

Nordic Process Control Workshop, Porsgrunn, Norway Table of Contents 1.Introduction 2.Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method 3.Application of the EDCDG on an Industrial Process 4.Description of the Process and the Test Environment 5.Fault Diagnosis Results 6.Conclusions

Nordic Process Control Workshop, Porsgrunn, Norway 6. Conclusions The Enhanced Dynamic Causal Digraph method was applied on an advanced board machine simulator and tested with four fault scenarios The EDCDG method provides valuable additional information for fault isolation compared to the previously presented methods –The results from each four cases were very promising In future the methods is going to be tested online on this board machine

Nordic Process Control Workshop, Porsgrunn, Norway Thank You for your attention! Questions?