EPSRC-funded SUPERGEN Bioenergy Challenge Project Bioenergy value chains: Whole systems analysis and optimisation. Led by Professor Nilay Shah (EP/K036734/1)

win 7 ultimate 32 bit keygen This project is being carried out in collaboration with University College London, University of Southampton, Rothamsted Research and University of Manchester. The core of this project is the models and data integration to lead to cutting edge tools which can identify robust and promising options for the UK bioenergy supply chains. This project is building on partners’ bioenergy system models combined with other models, including the UK-TIMES model and ETI-BVCM (Energy Technologies Institute - Bioenergy Value Chain Model), ecosystem and resource models and international trade models. The integrated modelling framework is able to determine bioenergy value chains best supporting a technologically efficient, economically viable and low-GHG UK energy system. The results of the modelling will feed into a wider policy analysis activity that will examine the dynamics of changing system infrastructure at intermediate time periods between 2010 and 2050.

ivona reader crack download In this project, Imperial College research team led by Prof Nilay Shah, focuses on extending ETI-BVCM to incorporate resource-competing systems (bioenergy vs. non-energy systems) and effects on ecosystem services brought about by the land use transition in response to bioenergy penetration over next decades, thereby enabling a rigorous technology and spatially explicit whole systems analysis. Mixed integer linear programming (MILP) modelling approach is adopted in this research to solve bioenergy value chain optimisation problems and identify the potential trade-offs between conflicting objectives involved in the bioenergy supply chain design e.g. economic development vs. environmental sustainability. The extended model allows for exploring the non-energy sectors (such as food crops, forage/fodder-fed livestock and timber products), which demand the same productive lands as bioenergy sector and accounts for a wide range of ecosystem services. Such extended optimization modelling framework could provide value insights for policy formulation to accelerate bioenergy penetration and support its sustainable development [1, 2].

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