{"title":"A framework to evaluate the calorific efficiency of hardwoods based on DEA and AHP methods","authors":"Hilal Singer","doi":"10.1016/j.asems.2025.100152","DOIUrl":null,"url":null,"abstract":"<div><div>This study proposes a two-phase decision-making framework to evaluate the calorific efficiencies of wood and bark samples obtained from the trunks and branches of fifteen hardwood tree species. The proposed framework integrates the data envelopment analysis (DEA) with the analytic hierarchy process (AHP). The DEA method is used to perform pairwise comparisons of the wood and bark samples. Ash content, volatile matter content, and fixed carbon content are selected as inputs, while the calorific value is used as the output. The results from the DEA analysis are analyzed using the AHP method to determine precise efficiency ranking indexes. The ranking order of the trunk wood samples is determined as follows: beech, oak, eucalyptus, hophornbeam, hazelnut, poplar, alder, maple, rhododendron, elm, ash, hornbeam, chestnut, linden, and plane. The ranking of the branch wood samples in descending order with the respective DEA-AHP scores is beech, poplar, alder, hophornbeam, oak, hornbeam, ash, rhododendron, eucalyptus, maple, linden, chestnut, plane, elm, and hazelnut. The sequence of the trunk bark samples is poplar, hornbeam, beech, chestnut, ash, alder, rhododendron, linden, hazelnut, maple, hophornbeam, oak, plane, elm, and eucalyptus. Lastly, the priority order of the branch bark samples is as follows: rhododendron, poplar, beech, linden, hornbeam, hazelnut, chestnut, hophornbeam, ash, alder, maple, oak, elm, plane, and eucalyptus. Additionally, comparative and sensitivity analyses are conducted to shed light on efficiency changes between the samples. The proposed framework can be used as a technical reference for selecting the most efficient materials and determining the effects of different input values on efficiency levels.</div></div>","PeriodicalId":100036,"journal":{"name":"Advanced Sensor and Energy Materials","volume":"4 3","pages":"Article 100152"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Sensor and Energy Materials","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2773045X25000196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposes a two-phase decision-making framework to evaluate the calorific efficiencies of wood and bark samples obtained from the trunks and branches of fifteen hardwood tree species. The proposed framework integrates the data envelopment analysis (DEA) with the analytic hierarchy process (AHP). The DEA method is used to perform pairwise comparisons of the wood and bark samples. Ash content, volatile matter content, and fixed carbon content are selected as inputs, while the calorific value is used as the output. The results from the DEA analysis are analyzed using the AHP method to determine precise efficiency ranking indexes. The ranking order of the trunk wood samples is determined as follows: beech, oak, eucalyptus, hophornbeam, hazelnut, poplar, alder, maple, rhododendron, elm, ash, hornbeam, chestnut, linden, and plane. The ranking of the branch wood samples in descending order with the respective DEA-AHP scores is beech, poplar, alder, hophornbeam, oak, hornbeam, ash, rhododendron, eucalyptus, maple, linden, chestnut, plane, elm, and hazelnut. The sequence of the trunk bark samples is poplar, hornbeam, beech, chestnut, ash, alder, rhododendron, linden, hazelnut, maple, hophornbeam, oak, plane, elm, and eucalyptus. Lastly, the priority order of the branch bark samples is as follows: rhododendron, poplar, beech, linden, hornbeam, hazelnut, chestnut, hophornbeam, ash, alder, maple, oak, elm, plane, and eucalyptus. Additionally, comparative and sensitivity analyses are conducted to shed light on efficiency changes between the samples. The proposed framework can be used as a technical reference for selecting the most efficient materials and determining the effects of different input values on efficiency levels.