Evaluation of models for process time delay estimation in a pulp bleaching plant

Abstract: The chemical processes used to manufacture pulp are always in development to cope with increasing environmental demands and competition. With a deeper understanding of the processes, the pulping industry can become both more profitable and effective at keeping an even and good quality of pulp, while reducing emissions. One step in this direction is to more accurately determine the time delay for a process, defined as the time it takes for a change in input to affect the process’s output. This information can then be used to control the process more efficiently. The methods used today to estimate the time delay use simple models and assumptions of the processes, for example that that the pulp behaves like a ”plug” that never changes its shape throughout the process. The problem with these assumptions is that they are only valid under ideal circumstances where there are no disturbances. This Master’s thesis aims to investigate if it is possible to measure the process time delay using only the input and output data from the process, and see if this estimation is more accurate than the existing model based methods. Another aim is to investigate if the process time delay can be monitored in real time. We investigated three methods: cross-correlation applied to the raw input and output data, cross-correlation applied to the derivative of the input and output data, and a convolutional neural network trained to identify the process time delay from the input and output data. The results show that it is possible to find the time delay, but with significant deviations from the models used today. Due to a lack of data where the time delay was measured, the reason for this deviation requires more research. The results also show that the three methods are unsuitable for real-time estimation. However, the models can likely monitor how the process time delay develops over long periods.