Tail Threading Analysis for Airborne Pulp Dryer
Jääskeläinen, Ilari (2012)
2012
Automaatiotekniikan koulutusohjelma
Automaatio-, kone- ja materiaalitekniikan tiedekunta - Faculty of Automation, Mechanical and Materials Engineering
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Hyväksymispäivämäärä
2012-12-05
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201301161017
https://urn.fi/URN:NBN:fi:tty-201301161017
Tiivistelmä
In this thesis the tail threading procedure for a pulp drying plant in a Stora Enso's manufacturing plant in Finland is examined. Tools are developed to analyze the work shifts' operation during tail threading, examine the most problematic work stages and to determine causes for web breaks.
The process environment and the threading procedure were studied by interviewing the staff at the plant. The threading procedure is a very complex series of work tasks, which must be completed in order. The next work stage may be started only after certain criteria are fulfilled. Hidden Markov Modelling was applied to the procedure by dividing the tasks as hidden states into two models. The observations used to distinguish the work tasks were developed according to the interviews.
The developed Markov models consist of six states. The model parameters were taught using supervised learning method. The models were validated by comparing video recordings from the process environment with the state sequence estimate given by the model, and also by using Confusion Matrices between the taught and estimated system states.
According to the Confusion Matrix calculations and the video material comparison, the models are found to function reliably. The wet end model's true states are confused with the other states more often than those of the dry end model. This may partly be related to the low sampling rate of the data and the short duration of the wet end work stages. The effects of inaccuracies in state recognition are minimized by post-processing the state estimates. For the dry end model state recognition a very high level of confidence was achieved (over 99%).
Key figures were defined from the state sequence estimates. In this thesis, these characteristics are simplified for presentation. The characteristics include durations in various threading stages, the number of threading efforts and the distribution of web breaks between the different stages for each of the work shifts.
The analysis suggests that each work shift has strengths and weaknesses in different stages of the procedure. By further examining the shifts' operations, the most reliable methods for each work phase could be obtained.
During the research for this thesis some issues with one of the process measurements were discovered. According to the measurement, the pulp web is higher than normal, and the automation system attempts to lower the web by decreasing the speed settings for the process. According to the measurement, however, the web is not lowered. These issues may greatly affect the success of the threading. Cause for this measurement behaviour was not found during the research for this thesis.
The process environment and the threading procedure were studied by interviewing the staff at the plant. The threading procedure is a very complex series of work tasks, which must be completed in order. The next work stage may be started only after certain criteria are fulfilled. Hidden Markov Modelling was applied to the procedure by dividing the tasks as hidden states into two models. The observations used to distinguish the work tasks were developed according to the interviews.
The developed Markov models consist of six states. The model parameters were taught using supervised learning method. The models were validated by comparing video recordings from the process environment with the state sequence estimate given by the model, and also by using Confusion Matrices between the taught and estimated system states.
According to the Confusion Matrix calculations and the video material comparison, the models are found to function reliably. The wet end model's true states are confused with the other states more often than those of the dry end model. This may partly be related to the low sampling rate of the data and the short duration of the wet end work stages. The effects of inaccuracies in state recognition are minimized by post-processing the state estimates. For the dry end model state recognition a very high level of confidence was achieved (over 99%).
Key figures were defined from the state sequence estimates. In this thesis, these characteristics are simplified for presentation. The characteristics include durations in various threading stages, the number of threading efforts and the distribution of web breaks between the different stages for each of the work shifts.
The analysis suggests that each work shift has strengths and weaknesses in different stages of the procedure. By further examining the shifts' operations, the most reliable methods for each work phase could be obtained.
During the research for this thesis some issues with one of the process measurements were discovered. According to the measurement, the pulp web is higher than normal, and the automation system attempts to lower the web by decreasing the speed settings for the process. According to the measurement, however, the web is not lowered. These issues may greatly affect the success of the threading. Cause for this measurement behaviour was not found during the research for this thesis.