Economies of Scale in Biomass Gasification Systems

Renewable energy sources, such as biomass, may replace the use of fossil fuels and have therefore an active role in reducing carbon dioxide emissions. One conversion technology for energy production from biomass is gasification. The gasification options can differ with regard to scale, biomass fuel, energetic efficiencies, investment and operational costs, as well as energy carriers produced. In this study at atmospheric indirectly fired gasifier is used and the energy carrier produced is methanol. The whole bioenergy chain is described in this study, from when the biomass is extracted in the forest until the produced methanol is distributed to the consumer. Five system-components are distinguished in the chain: biomass extraction and pre-treatment, transportation of biomass, biomass conversion to methanol, transportation of methanol and distribution of methanol. The aim of this paper is to classify the costs and energy efficiencies of the system components when the scale of the system changes. The methanol plants described have a biomass input between 10 MW and 1000 MW. The scale of the gasification plant influences the unit cost of the produced methanol, and large-scale production plants will have the advantage in this respect. On the other hand, large-scale plants are likely to have higher transportation costs per unit biomass transported as a result of longer transportation distances. When using the input variables described for the model the methanol unit cost decreases as plant size increases. The total unit cost of methanol is found to decrease from about 20.6 Euro/GJ MeOH for a 10 MW plant to about 12.5 Euro/GJ MeOH for a 200 MW plant. The unit costs stabilize for plant sizes between 200 MW and 1000 MW, but do however continue to decrease to about 11 Euro/GJ MeOH for a 1000 MW plant. Included in the unit methanol cost are 50 kilometer (km) additional biomass transport by truck and 100 km methanol transport by train and 1000 km methanol transport by ship. This result depend on many different input variables, such as biomass, plant and transportation costs. In order to assess the influence the different variables produce on the final methanol unit cost, a sensitivity analysis is carried out. The energy efficiencies for the different scaled biomass pathways are found to be more or less scale independent. Assuming that produced methanol is transported independent of plant size and using the same transportation means and distances, transportation of biomass is the only scale dependent factor. For truck transportation of biomass this energy consumption varies from 0.1% of the total bioenergy for a 10 MW plant to 1.2% of the total input bioenergy for a 1000 MW plant. Two geographical areas are analyzed using the model. An area in the north-west of Spain demonstrates the model for a large-scale methanol plant (935 MW biomass input) and an area in the west of Greece demonstrates a model for a medium-sized methanol plant (380 MW biomass input)

Organic matter drives high interannual variability in methylmercury concentrations in a subarctic coastal sea

Levels of neurotoxic methylmercury (MeHg) in phytoplankton are strongly associated with water MeHg concentrations. Because uptake by phytoplankton is the first and largest step of bioaccumulation in aquatic food webs many studies have investigated factors driving seasonal changes in water MeHg concentrations. Organic matter (OM) is widely accepted as an important driver of MeHg production and uptake by phytoplankton but is also known for strong interannual variability in concentration and composition within systems. In this study, we explore the role of OM on spatial and interannual variability of MeHg in a subarctic coastal sea, the northern Baltic Sea. Using MeHg (2014: 80 ± 25 fM; 2015: <LOD; 2016: 21 ± 9 fM) and OM measurements during late summer/early fall, we find that dissolved organic carbon (DOC) and humic matter content explain 60% of MeHg variability. We find that while labile DOC increases MeHg levels in the water, humic content reduces it. We propose that the positive association between MeHg and labile DOC shows that labile DOC is a proxy for OM remineralization rate in nearshore and offshore waters. This is consistent with other studies finding that in situ MeHg production in the water column occurs during OM remineralization. The negative association between water humic content and MeHg concentration is most likely due to humic matter decreasing inorganic mercury (HgII) bioavailability to methylating microbes. With these relationships, we develop a statistical model and use it to calculate MeHg concentrations in late summer nearshore and offshore waters between 2006 and 2016 using measured values for water DOC and humic matter content. We find that MeHg concentrations can vary by up to an order of magnitude between years, highlighting the importance of considering interannual variability in water column MeHg concentrations when interpreting both short and long term MeHg trends in biota

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios