Minimising carbon footprint of regional biomass supply chains

A new method for regional energy targeting and supply chain synthesis is presented. A demand-driven approach is applied to assess the feasible ways for transferring energy from renewable sources to customers in a given region. The studied region is partitioned into a number of clusters by using the developed Regional Energy Clustering (REC) algorithm. The REC targets aim at minimising the system carbon footprint (CFP). The biomass energy supply and management are targeted using new graphical representations. Regional Energy Surplus–Deficit Curves (RESDC) visualises the formation and the sizes of introduced energy clusters. Regional Resource Management Composite Curve (RRMCC) an analogy of the Process Integration approach shows the energy imbalances helping in trading-off resources management. These graphical tools provide straightforward information of how to manage the surplus resources (biomass and land use) in a region.

[1]  Jiří Jaromír Klemeš,et al.  The Environmental Performance Strategy Map: an integrated LCA approach to support the strategic decision-making process , 2009 .

[2]  Dominic C.Y. Foo,et al.  Pinch analysis approach to carbon-constrained energy sector planning , 2007 .

[3]  M. Fergusson,et al.  Energy from biomass in the UK: sources, processes and biodiversity implications , 2006 .

[4]  Ali Sayigh,et al.  Renewable energy — the way forward , 1999 .

[5]  G. Forsberg Biomass energy transport: Analysis of bioenergy transport chains using life cycle inventory method , 2000 .

[6]  P. Rauch,et al.  Designing a regional forest fuel supply network , 2007 .

[7]  Ferenc Friedler,et al.  Combinatorial algorithms for process synthesis , 1992 .

[8]  J. Goldemberg World energy assessment : energy and the challenge of sustainability , 2000 .

[9]  M. Porter Clusters and the new economics of competition. , 1998, Harvard business review.

[10]  Erik Kärrman,et al.  Environmental systems analysis of the use of bottom ash from incineration of municipal waste for road construction , 2006 .

[11]  Mark A. Kramer,et al.  Autoassociative neural networks , 1992 .

[12]  Igor Bulatov,et al.  Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors , 2008 .

[13]  Eric Johnson,et al.  Goodbye to carbon neutral: Getting biomass footprints right , 2009 .

[14]  L. P. Koh,et al.  Biofuels, biodiversity, and people: Understanding the conflicts and finding opportunities , 2008 .

[15]  I. Tatsiopoulos,et al.  Economic aspects of the cotton-stalk biomass logistics and comparison of supply chain methods , 2003 .

[16]  T. van Dijk,et al.  Financing local rural road maintenance. Who should pay what share and why , 2002 .

[17]  Gregg Marland,et al.  Carbon management and biodiversity. , 2003, Journal of Environmental Management.

[18]  Christina Skjöldebrand,et al.  Clusters/networks promote food innovations , 2007 .

[19]  Denny K. S. Ng,et al.  Extended pinch targeting techniques for carbon-constrained energy sector planning , 2009 .

[20]  Bojie Fu,et al.  Analysis on soil nutrient characteristics for sustainable land use in Danangou catchment of the Loess Plateau, China , 2003 .

[21]  Young B. Moon,et al.  A comprehensive clustering algorithm for strategic analysis of supply chain networks , 1999 .

[22]  Michael Narodoslawsky,et al.  SPIonExcel—Fast and easy calculation of the Sustainable Process Index via computer , 2007 .

[23]  Ingwald Obernberger,et al.  Ecological Assessment of Integrated Bioenergy Systems using the Sustainable Process Index , 2000 .

[24]  Petar Sabev Varbanov,et al.  P-graph methodology for cost-effective reduction of carbon emissions involving fuel cell combined cycles , 2008, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization.

[25]  Kor de Jong,et al.  A method to analyse neighbourhood characteristics of land use patterns , 2004, Comput. Environ. Urban Syst..