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.