Näytä suppeat kuvailutiedot

dc.contributor.authorLaasasenaho, K.
dc.contributor.authorLensu, Anssi
dc.contributor.authorLauhanen, R.
dc.contributor.authorRintala, J.
dc.date.accessioned2019-05-24T11:58:25Z
dc.date.available2021-05-19T21:35:08Z
dc.date.issued2019
dc.identifier.citationLaasasenaho, K., Lensu, A., Lauhanen, R., & Rintala, J. (2019). GIS-data related route optimization, hierarchical clustering, location optimization, and kernel density methods are useful for promoting distributed bioenergy plant planning in rural areas. <i>Sustainable Energy Technologies and Assessments</i>, <i>32</i>, 47-57. <a href="https://doi.org/10.1016/j.seta.2019.01.006" target="_blank">https://doi.org/10.1016/j.seta.2019.01.006</a>
dc.identifier.otherCONVID_28914676
dc.identifier.otherTUTKAID_80621
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64192
dc.description.abstractCurrently, geographic information system (GIS) models are popular for studying location-allocation-related questions concerning bioenergy plants. The aim of this study was to develop a model to investigate optimal locations for two different types of bioenergy plants, for farm and centralized biogas plants, and for wood terminals in rural areas based on minimizing transportation distances. The optimal locations of biogas plants were determined using location optimization tools in R software, and the optimal locations of wood terminals were determined using kernel density tools in ArcGIS. The present case study showed that the utilized GIS tools are useful for bioenergy-related decision-making to identify potential bioenergy areas and to optimize biomass transportation, and help to plan power plant sizing when candidate bioenergy plant locations have not been defined in advance. In the study area, it was possible to find logistically viable locations for 13 farm biogas plants (>100 kW) and for 8 centralized biogas plants (>300 kW) using a 10-km threshold for feedstock supply. In the case of wood terminals, the results identified the most intensive wood reserves near the highest road classes, and two potential locations were determined.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesSustainable Energy Technologies and Assessments
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherlocation-allocation
dc.subject.othernetwork analysis
dc.subject.otherwood terminal
dc.titleGIS-data related route optimization, hierarchical clustering, location optimization, and kernel density methods are useful for promoting distributed bioenergy plant planning in rural areas
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201905242791
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.oppiaineYmpäristötiedefi
dc.contributor.oppiaineEnvironmental Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-24T09:15:04Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange47-57
dc.relation.issn2213-1388
dc.relation.numberinseries0
dc.relation.volume32
dc.type.versionacceptedVersion
dc.rights.copyright© 2019 Elsevier Ltd. All rights reserved.
dc.rights.accesslevelopenAccessfi
dc.subject.ysokiertotalous
dc.subject.ysobiokaasu
dc.subject.ysobiomassa (teollisuus)
dc.subject.ysotuotantoketjut
dc.subject.ysooptimointi
dc.subject.ysopaikkatietojärjestelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28601
jyx.subject.urihttp://www.yso.fi/onto/yso/p3894
jyx.subject.urihttp://www.yso.fi/onto/yso/p6170
jyx.subject.urihttp://www.yso.fi/onto/yso/p24214
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p16923
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.seta.2019.01.006
dc.type.okmA1


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