Andrea Corona

Environmental life cycle assessment of producing willow, alfalfa and straw from spring barley as feedstocks for bioenergy or biorefinery systems

The current study aimed at evaluating potential environmental impacts for the production of willow, alfalfa and straw from spring barley as feedstocks for bioenergy or biorefinery systems. A method of Life Cycle Assessment was used to evaluate based on the following impact categories: Global Warming Potential (GWP100), Eutrophication Potential (EP), Non-Renewable Energy (NRE) use, Agricultural Land Occupation (ALO), Potential Freshwater Ecotoxicity (PFWTox) and Soil quality. With regard to the methods, soil organic carbon (SOC) change related to the land occupation was calculated based on the net carbon input to the soil. Freshwater ecotoxicity was calculated using the comparative toxicity units of the active ingredients and their average emission distribution fractions to air and freshwater. Soil quality was based on the change in the SOC stock estimated during the land use transformation and land occupation. Environmental impacts for straw were economically allocated from the impacts obtained for spring barley. The results obtained per ton dry matter showed a lower carbon footprint for willow and alfalfa compared to straw. It was due to higher soil carbon sequestration and lower N2O emissions. Likewise, willow and alfalfa had lower EP than straw. Straw had lowest NRE use compared to other biomasses. PFWTox was lower in willow and alfalfa compared to straw. A critical negative effect on soil quality was found with the spring barley production and hence for straw. Based on the energy output to input ratio, willow performed better than other biomasses. On the basis of carbohydrate content of straw, the equivalent dry matter of alfalfa and willow would be requiring higher. The environmental impacts of the selected biomasses in biorefinery therefore would differ based on the conversion efficiency, e.g. of the carbohydrates in the related biorefinery processes.

Comparative Environmental Sustainability Assessment of Bio-Based Fibre Reinforcement Materials for Wind Turbine Blades

Over the recent decades biomaterials have been marketed successfully supported by the common perception that biomaterials and environmental sustainability de facto represents two sides of the same coin. The development of sustainable composite materials for wind turbine blades for small-scale wind turbines have therefore partially been focused on substitution of conventional fibre materials with bio-fibres assuming that this substitution was in the better for the environment and human health. The major question is if this material substitution, taking into account a multitude of environmental impact categories, not only climate change, actually is supporting sustainable development or if the development of sustainable composite materials is more complex and perhaps even contra-intuitive due to complex trade-offs. Based on a case study 4 different types of fibres and fibre mixtures (flax, carbon, glass and flax/carbon, flax/glass mixed fibres) are compared in terms of environmental sustainability. Applying one of the most recent life cycle impact assessment methods, we demonstrate that the environmental sustainability of natural fibre based composite materials is similar or even lower, within certain impact categories, than the conventional materials. This observation may seem contra-intuitive (i.e. most people would expect the bio-based to be most sustainable), but is primarily caused by the fact that the resin demand of biobased reinforcement materials is by far larger than that of conventional reinforcement materials. Since the environmental burden of the resin in addition is comparable to that of the fibres (especially in terms human health related impacts), the higher resin demand counterbalances the environmental sustainability improvements, obtained with the application of natural fibres.

Techno-environmental assessment of the green biorefinery concept: Combining process simulation and life cycle assessment at an early design stage

The Green biorefinery (GBR) is a biorefinery concept that converts fresh biomass into value-added products. The present study combines a Process Flowsheet Simulation (PFS) and Life Cycle Assessment (LCA) to evaluate the technical and environmental performance of different GBR configurations and the cascading utilization of the GBR output. The GBR configurations considered in this study, test alternatives in the three main steps of green-biorefining: fractionation, precipitation, and protein separation. The different cascade utilization alternatives analyse different options for press-pulp utilization, and the LCA results show that the environmental profile of the GBR is highly affected by the utilization of the press-pulp and thus by the choice of conventional product replaced by the press-pulp. Furthermore, scenario analysis of different GBR configurations shows that higher benefits can be achieved by increasing product yields rather than lowering energy consumption. Green biorefining is shown to be an interesting biorefining concept, especially in a Danish context. Biorefining of green biomass is technically feasible and can bring environmental savings, when compared to conventional production methods. However, the savings will be determined by the processing involved in each conversion stage and on the cascade utilization of the different platform products.

Life Cycle Inventory Analysis

The inventory analysis is the third and often most time-consuming part of an LCA. The analysis is guided by the goal and scope definition, and its core activity is the collection and compilation of data on elementary flows from all processes in the studied product system(s) drawing on a combination of different sources. The output is a compiled inventory of elementary flows that is used as basis of the subsequent life cycle impact assessment phase. This chapter teaches how to carry out this task through six steps: (1) identifying processes for the LCI model of the product system; (2) planning and collecting data; (3) constructing and quality checking unit processes; (4) constructing LCI model and calculating LCI results; (5) preparing the basis for uncertainty management and sensitivity analysis; and (6) reporting.