Effect of forest-based biofuels production on carbon footprint, Case: Integrated LWC paper mill

International energy and climate strategies also set Finland’s commitments to increasing the use of renewable energy sources and reducing greenhouse gas emissions. The target can be achieved by, for example, increasing the use of energy wood. Finland’s forest biomass potential is significant compared with current use. Increased use will change forest management and wood harvesting methods however. The thesis examined the potential for integrated pulp and paper mills to increase bioenergy production. The effects of two bioenergy production technologies on the carbon footprint of an integrated LWC mill were studied at mill level and from the cradle-to-customer approach. The LignoBoost process and FT diesel production were chosen as bioenergy cases. The data for the LignoBoost process were obtained from Metso and for the FT diesel process from Neste Oil. The rest of the information is based on the literature and databases of the KCL-ECO life-cycle computer program and Ecoinvent. In both case studies, the carbon footprint was reduced. From the results, it can be concluded that it is possible to achieve a fossil-fuel-free pulp mill with the LignoBoost process. By using steam from the FT diesel process, the amount of auxiliary fuel can be reduced considerably and the bark boiler can be replaced. With a choice of auxiliary fuels for use in heat production in the paper mill and the production methods for purchased electricity, it is possible to affect the carbon footprints even more in both cases.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.