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Optimization of paper pulp production using Artificial Neural Networks and Simulated Annealing

Sandkvist, Filip LU (2014) FYTK01 20131
Computational Biology and Biological Physics - Has been reorganised
Abstract
The purpose of this paper is to investigate the possibilities of optimizing the production of paper pulp in a Conical Disc (CD) refiner for different production rates, taking into account the variables that are thought to be the most important for the production. These include three separate dilution waters, conical disc gap and flat gap. Another task is to compare the values of these variables after optimization for different production rates in order to see if there is a pattern between these to be found. These two tasks are solved using Artificial Neural Networks (ANN:s) and Simulated Annealing. Data consists of measurements made on a CD70 refiner, and is provided by Jan Hill at QualTech Jan Hill AB. ANN:s seems to be a possible tool... (More)
The purpose of this paper is to investigate the possibilities of optimizing the production of paper pulp in a Conical Disc (CD) refiner for different production rates, taking into account the variables that are thought to be the most important for the production. These include three separate dilution waters, conical disc gap and flat gap. Another task is to compare the values of these variables after optimization for different production rates in order to see if there is a pattern between these to be found. These two tasks are solved using Artificial Neural Networks (ANN:s) and Simulated Annealing. Data consists of measurements made on a CD70 refiner, and is provided by Jan Hill at QualTech Jan Hill AB. ANN:s seems to be a possible tool for calculating refiner-specific parameters needed to predict the long fiber content and dewatering capability of the pulp. Simulated Annealing was used in an attempt to show that there exists a relationship between variable values that gives maximum freeness for a fixed long fiber content of 45 %. (Less)
Please use this url to cite or link to this publication:
author
Sandkvist, Filip LU
supervisor
organization
course
FYTK01 20131
year
type
M2 - Bachelor Degree
subject
keywords
Canadian Standard Freeness., paper pulp, Simulated Annealing, hard/soft limit function, Artificial Neural Network, CD refiner
language
English
id
4422906
date added to LUP
2014-04-29 15:55:07
date last changed
2017-10-06 16:31:45
@misc{4422906,
  abstract     = {{The purpose of this paper is to investigate the possibilities of optimizing the production of paper pulp in a Conical Disc (CD) refiner for different production rates, taking into account the variables that are thought to be the most important for the production. These include three separate dilution waters, conical disc gap and flat gap. Another task is to compare the values of these variables after optimization for different production rates in order to see if there is a pattern between these to be found. These two tasks are solved using Artificial Neural Networks (ANN:s) and Simulated Annealing. Data consists of measurements made on a CD70 refiner, and is provided by Jan Hill at QualTech Jan Hill AB. ANN:s seems to be a possible tool for calculating refiner-specific parameters needed to predict the long fiber content and dewatering capability of the pulp. Simulated Annealing was used in an attempt to show that there exists a relationship between variable values that gives maximum freeness for a fixed long fiber content of 45 %.}},
  author       = {{Sandkvist, Filip}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Optimization of paper pulp production using Artificial Neural Networks and Simulated Annealing}},
  year         = {{2014}},
}