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Titlebook: Life Cycle Impact Assessment; Michael Z. Hauschild,Mark A.J. Huijbregts Book 2015 Springer Science+Business Media Dordrecht 2015 LCIA char

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書(shū)目名稱(chēng)Life Cycle Impact Assessment
編輯Michael Z. Hauschild,Mark A.J. Huijbregts
視頻videohttp://file.papertrans.cn/586/585798/585798.mp4
概述Offers an authoritative reference work on life cycle impact assessment.Thoroughly explores the selection of impact categories and the classification of inventory flows for all the impacts that are fre
叢書(shū)名稱(chēng)LCA Compendium – The Complete World of Life Cycle Assessment
圖書(shū)封面Titlebook: Life Cycle Impact Assessment; Michael Z. Hauschild,Mark A.J. Huijbregts Book 2015 Springer Science+Business Media Dordrecht 2015 LCIA char
描述.This book offers a detailed presentation of the principles and practice of life cycle impact assessment. As a volume of the LCA compendium, the book is structured according to the LCIA framework developed by the International Organisation for Standardisation (ISO)passing through the phases of definition or selection of impact categories, category indicators and characterisation models (Classification): calculation of category indicator results (Characterisation); calculating the magnitude of category indicator results relative to reference information (Normalisation); and converting indicator results of different impact categories by using numerical factors based on value-choices (Weighting)..Chapter one offers a historical overview of the development of life cycle impact assessment and presents the boundary conditions and the general principles and constraints of characterisation modelling in LCA. The second chapter outlines the considerations underlying the selection of impact categories and the classification or assignment of inventory flows into these categories. Chapters three through thirteen exploreall the impact categories that are commonly included in LCIA, discussing the
出版日期Book 2015
關(guān)鍵詞LCIA characterisation; LCIA classification; LCIA normalisation; Life Cycle Impact Assessment (LCIA); Wei
版次1
doihttps://doi.org/10.1007/978-94-017-9744-3
isbn_softcover978-94-024-0404-3
isbn_ebook978-94-017-9744-3Series ISSN 2214-3505 Series E-ISSN 2214-3513
issn_series 2214-3505
copyrightSpringer Science+Business Media Dordrecht 2015
The information of publication is updating

書(shū)目名稱(chēng)Life Cycle Impact Assessment影響因子(影響力)




書(shū)目名稱(chēng)Life Cycle Impact Assessment影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Life Cycle Impact Assessment網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Life Cycle Impact Assessment被引頻次




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978-94-024-0404-3Springer Science+Business Media Dordrecht 2015
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2214-3505 pact categories and the classification or assignment of inventory flows into these categories. Chapters three through thirteen exploreall the impact categories that are commonly included in LCIA, discussing the978-94-024-0404-3978-94-017-9744-3Series ISSN 2214-3505 Series E-ISSN 2214-3513
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mployed in this study. Our experiments using real historical logs confirmed that the proposed deep learning model achieved 0.3–7.9% higher prediction accuracy compared to the boosted decision tree and multi-layer perceptron models. An extensive analysis of the proposed deep learning model was perfor
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Michael Z. Hauschild,Mark A. J. Huijbregts Decomposition of the demand is achieved through the boundary analysis of a continuous relaxation of the problem. Using the metrics defined in terms of the cost and time of completion, we demonstrate excellent performance with respect to optimal solutions. Our method reduced the computational time f
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Jeroen B. Guinéemployed in this study. Our experiments using real historical logs confirmed that the proposed deep learning model achieved 0.3–7.9% higher prediction accuracy compared to the boosted decision tree and multi-layer perceptron models. An extensive analysis of the proposed deep learning model was perfor
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Annie Levasseurof flexibility, and show that these can be solved for realistic problem sizes with today’s ILP solver technology. Experiments with several streaming task graphs derived from real-world applications show that the flexibility for the scheduler can be greatly increased by considering buddy-cores, thus
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