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.

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 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.