GIS-based biomass assessment and supply logistics system for a sustainable biorefinery: A case study with cotton stalks in the Southeastern US
K. Samples and
Applied Energy, 2016, vol. 182, issue C, 260-273
Envisioning a sustainable biorefinery requires reliable information on the sustainable availability of biomass, optimal plant location and delivered cost. In this paper, we have developed an integrated Geographic Information System (GIS) based sustainable biomass assessment, site optimization and supply logistics cost model to assess the spatial and temporal availability of crop residues, to identify optimal plant sites and to calculate the delivered cost. The grid-level (30×30m) assessment model was developed for crop residues using three primary sustainability indicators: (1) Soil Erosion (SE), (2) Soil Conditioning Index (SCI) and (3) Crop residue yield ⩾2.5dryMg/ha. The Artificial Neural Networks (ANNs) prediction models for each indicator were developed and implemented in the GIS platform to assess sustainably available crop residues. A multi-criteria geospatial analysis was used to identify suitable plant sites. GIS-based location-allocation model was used to site biorefineries/plants at optimal locations and generate feedstock supply curves. The developed model was demonstrated with the sustainable assessment of cotton stalk (CS) to produce fuel pellets in the study region (Georgia, USA). The model has estimated that about 1.6milliondryMg of CS is available annually to support seven pellet plants with an average annual plant capacity of 200,000dryMg. The average delivered cost of CS ranged between 68 and 75$/dryMg delivered as large rectangular bales with the transport radii ranged from 31 to 60km. The spatial and temporal variations in the topology and crop yield directly influenced the sustainable availability of CS, the optimal plant location and its capacity and the delivered cost. However, the changes in the optimal plant location and delivered cost were minimal for large capacity plants (>400,000dryMg). The developed model can be used to assess multiple crop residues, to manage and control feedstock supply risks and delivered cost variations for a sustainable biorefinery.
Keywords: Artificial Neural Network; Soil erosion; Soil conditioning index; Geographical information system (GIS); Location-allocation model; Cotton stalk; Delivered cost (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:182:y:2016:i:c:p:260-273
Ordering information: This journal article can be ordered from
http://www.elsevier. ... 405891/bibliographic
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Series data maintained by Dana Niculescu ().