• Energy Research

2023

2023

In Finland, in recent years, the combustion of dry reed canary grass (RCG, Phalaris arundinacea) grown intensively on cutover peatlands, has decreased markedly. We therefore made experiments in two areas to assess the alternative of using freshly harvested RCG grown for biogas production on cutover peatland. We measured both biogas production and combustion energy release. The experiments show that the RCG biomass yields in total solids (TS) in both areas, with two cuts a year, were surprisingly small (yields of 2.7 and 4.2 Mg ha-1 [1 Mg ha-1 = 100 g m-2]); having biogas and combustion potentials, on the two areas, of 277–348 dm3 kg-1 VS (volatile solids) and 14.8–16.3 MJ kg-1 TS, and 11.8–21.9 MWh ha-1 in combustion. Fresh RCG may produce larger biomass yields if cut several times a year, together with lower lignin proportion, and better suitability for biogas production compared with spring harvested dry RCG. For cutover peatlands there are several after-use possibilities, however, with different benefits and challenges. For example, peat soil emissions may be affected during the after-use period, and this should be considered when planning the use of cutover peatlands.

In Finland, in recent years, the combustion of dry reed canary grass (RCG, Phalaris arundinacea) grown intensively on cutover peatlands, has decreased markedly. We therefore made experiments in two areas to assess the alternative of using freshly harvested RCG grown for biogas production on cutover peatland. We measured both biogas production and combustion energy release. The experiments show that the RCG biomass yields in total solids (TS) in both areas, with two cuts a year, were surprisingly small (yields of 2.7 and 4.2 Mg ha-1 [1 Mg ha-1 = 100 g m-2]); having biogas and combustion potentials, on the two areas, of 277–348 dm3 kg-1 VS (volatile solids) and 14.8–16.3 MJ kg-1 TS, and 11.8–21.9 MWh ha-1 in combustion. Fresh RCG may produce larger biomass yields if cut several times a year, together with lower lignin proportion, and better suitability for biogas production compared with spring harvested dry RCG. For cutover peatlands there are several after-use possibilities, however, with different benefits and challenges. For example, peat soil emissions may be affected during the after-use period, and this should be considered when planning the use of cutover peatlands.

Abstract Currently, geographic information system (GIS) models are popular for studying location-allocation-related questions concerning bioenergy plants. The aim of this study was to develop a model to investigate optimal locations for two different types of bioenergy plants, for farm and centralized biogas plants, and for wood terminals in rural areas based on minimizing transportation distances. The optimal locations of biogas plants were determined using location optimization tools in R software, and the optimal locations of wood terminals were determined using kernel density tools in ArcGIS. The present case study showed that the utilized GIS tools are useful for bioenergy-related decision-making to identify potential bioenergy areas and to optimize biomass transportation, and help to plan power plant sizing when candidate bioenergy plant locations have not been defined in advance. In the study area, it was possible to find logistically viable locations for 13 farm biogas plants (>100 kW) and for 8 centralized biogas plants (>300 kW) using a 10-km threshold for feedstock supply. In the case of wood terminals, the results identified the most intensive wood reserves near the highest road classes, and two potential locations were determined.

Abstract Currently, geographic information system (GIS) models are popular for studying location-allocation-related questions concerning bioenergy plants. The aim of this study was to develop a model to investigate optimal locations for two different types of bioenergy plants, for farm and centralized biogas plants, and for wood terminals in rural areas based on minimizing transportation distances. The optimal locations of biogas plants were determined using location optimization tools in R software, and the optimal locations of wood terminals were determined using kernel density tools in ArcGIS. The present case study showed that the utilized GIS tools are useful for bioenergy-related decision-making to identify potential bioenergy areas and to optimize biomass transportation, and help to plan power plant sizing when candidate bioenergy plant locations have not been defined in advance. In the study area, it was possible to find logistically viable locations for 13 farm biogas plants (>100 kW) and for 8 centralized biogas plants (>300 kW) using a 10-km threshold for feedstock supply. In the case of wood terminals, the results identified the most intensive wood reserves near the highest road classes, and two potential locations were determined.