Digester modelling for diagnostics and control

This thesis will show the possibility for the development and use of an on-line model for application to continuous digesters for pulp production. The model is developed by using a program called Dymola (Dynamic Modeling Laboratory). What makes the Dymola software so well suited is that the program solves equations simultaneously. The model is a further development from the Purdue model [Bhartiya et al, 2003]. The main difference between this model and the Purdue model however, is the dynamics in the model. The dynamics are very important when you use the model for control purposes because the cooking process has long dead and retention times. The main purpose of this model is to use it for the advanced control of continuous digesters as well as giving the operators a better understanding of what happens in the cooking process when changes are made. The model will also be used for diagnostic purposes. Advanced control in this case is Model Predicted Control (MPC). The MPC will control the quality of the pulp “kappa” number and the chemical consumption during digestion. This thesis describes the model and results are shown for applications of on-line diagnosis in three pulp mills in South Africa. Real time process data from the pulp mills is fed into the model and a simulation is performed. Thereafter, the results from the simulation are compared to the actual measured data for a number of key variables. By comparing the simulation results to the real process data and following the trends of the deviations between the two, different types of faults and upsets can be detected in both the process and sensors.KKS project:Use of physical models combined with statistical models for improved digester contro