A multiple regression model was evaluated to predict pulp and handsheet properties including z-directional tensile strength (z-strength) and Scott bond values. One hypothesis that was central for the model evaluation was that the crill content, as measured with ultraviolet and infrared lights, would improve the statistical models. A chemi-thermomechanical pulp (CTMP) mill designed with two parallel primary refining lines and a reject refiner was the basis for this study, and all process data and pulp samples were gathered from the specific process. Pulp was extracted from the process for an extended period from a position after the latency chest (primary refined pulp) and from the pulp-stream exiting the mill to the board machine (accept pulp). The crill content was positively correlated to the z-strength of the accept pulp, explaining 55% of the variance with a linear regression model with the drill content as the sole predictor. The estimation model of the z-strength of accept pulp was based on a combination of the crill content, freeness, fibril perimeter for longer fibers, and mean kink angle, and resulted in an R-2 of 0.79. When applying cross-validation to determine the predictive model performance, the highest R-2 obtained was 0.67. This latter model included the crill content, fibril perimeter, and mean kink angle as predictors.

The manufacture of pulp and paper is an energy intensive process configured of several unit processes that shape a network of flows of wood chips, chemical pulp, mechanical pulp, board and other important components. Improved energy efficiency supports sustainability of the process and the products. With the purpose of monitoring and controlling, information from multiple process and quality variables is continuously collected in the process data system. The data may contain information about underlying patterns and variability, and using statistical and multivariate data analysis can create valuable insights into how reduced variations and predictions of certain properties can be accomplished.

This thesis investigates the application of mathematical models for processes and products. These models can be used to increase the knowledge of the process characteristics and for quality predictions, to support process optimization and improved product quality.

Based on process data from a board machine including the stock preparation process, an evaporation system and a CTMP plant, process models have been developed with the aims of quality predictions, improved energy efficiency and reduced process variability. 

The manufacture of pulp and paper is an energy intensive process configured of several unit processes that shape a network of flows of wood chips, chemical pulp, mechanical pulp, board and other important components. Improved energy efficiency supports sustainability of the process and the products. With the purpose of monitoring and controlling, information from multiple process and quality variables is continuously collected in the process data system. The data may contain information about underlying patterns and variability, and using statistical and multivariate data analysis can create valuable insights into how reduced variations and predictions of certain properties can be accomplished.

This thesis investigates the application of mathematical models for processes and products. These models can be used to increase the knowledge of the process characteristics and for quality predictions, to support process optimization and improved product quality.

Based on process data from a board machine including the stock preparation process, an evaporation system and a CTMP plant, process models have been developed with the aims of quality predictions, improved energy efficiency and reduced process variability. 

Through application of modelling and simulation techniques a range of models were developed in several case studies. These techniques included both mechanistic and statistical models and were demonstrated using Pinch to study energy recovery in the evaporation plant, time series and multiple linear regression modelling for predictions in the CTMP process, flowsheet modelling of stock preparation dynamics and neural networks for board quality predictions. The process models that were developed in the case studies demonstrated how these methods can be applied to predict important properties, study systematic variations and improve the energy efficiency by describing the opportunities and limitations associated with these techniques.

Frequently sampled process data from a conical disc refiner and infrequently sampled pulp data from a full scale chemi-thermomechanical pulp (CTMP) mill were evaluated to study autocovariance with aspects of potential dynamic modelling applicability. Two trial measurements with an online pulp analyzer at decreased sampling intervals were performed. For variability analysis, time-series containing up to one day of operational data were used. At the chip refiner, the clearest significant autocovariance was identified for the specific electricity consumption, based on the longer sequences. Most of the estimated pulp properties indicated low or non-significant autocovariance, limiting applicability of a specific dynamic model. A mill trial was conducted to investigate the impact from an increase in the conical disc gap on the specific electricity consumption and the resulting freeness. The response time from the gap change in the refiner to measured change in freeness was estimated at 19 min, which was approximately the hydraulic residence time in the latency chest. The relevance of this study lies in applicability of mill-data-driven modelling to capture the dynamics of a specific refining process. Through mill trials the sampling speed of pulp properties was more than doubled to gain insights into short term systematic variations by applying time-series-analysis.

The manufacture of pulp and paper is an energy intensive process configured of several unit processes that shape a network of flows of wood chips, chemical pulp, mechanical pulp, paperboard, steam and other important components. Improved energy efficiency supports sustainability of the process and the products. With the purpose of monitoring and controlling, information from multiple process and quality variables is continuously collected in the process data system. This data may be of time-varying nature and the variability might potentially span from seasonal to time-wise shorter variations and there are in some cases a need for predicting certain properties.

By applying models based on process data there is a potential to increase the knowledge of the process characteristics, investigate the applicability of predictive models and identify optimization opportunities. Based on data from an evaporation and a CTMP plant, process models have been developed with the aim of improving the energy efficiency and studying process variability.  

The primary objective of this study was to quantify the amount of excess energy that is present in the evaporation system of an integrated pulp and paper-board mill and to analyze a number of energy recovery cases. These focus on improving the energy efficiency in the evaporation plant and are mainly based on the process data of performance tests from the full-scale production site. A computer script was developed in order to analyze the process streams and can be used to construct the Grand Composite Curve (GCC) of the evaporation system. In addition, the study identified seasonal variations in the potential excess of energy (higher in warmer weather and lower, or even non-existent, in colder) and suggestions are made as to how this energy may be used in a thermodynamically optimal way. In the case studies, the thermodynamically optimal method of recovering heat involved a combination of sensible heat and flash evaporation, indicating the maximum reduction in steam consumption. For the case of only utilizing sensible heat outside the evaporator system to pre-heat one of the liquor flows, the results indicated a lower reduction in steam but also a lower capital cost.