Integration of Life Cycle assessment and population balance model for assessing environmental impacts of product population in a social scale case studies for the global warming potential of air conditioners in Japan

Yokota, Kazuhiko; Matsuno, Yasunari; Yamashita, Masaru; Adachi, Yoshihiro

doi:10.1007/bf02978457

Cited by 48 publications

(13 citation statements)

References 5 publications

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“…16) The dynamic analysis technique used in this study is a population balance model 8,17) ; it calculates the amount of disused steel, F out , from the domestic demand, F in , since the start of the period in question, and also the lifetime distribution for each end use category. The demand in year t for a particular end use i is F in (i, t), and the lifetime distribution of that particular end use is G(i, a) as a cumulative distribution function for the lifetime a.…”

Section: Derivation Methods For Materials Stocksmentioning

confidence: 99%

Accounting for Steel Stock in Japan

et al. 2007

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Section: Derivation Methods For Materials Stocksmentioning

confidence: 99%

Accounting for Steel Stock in Japan

et al. 2007

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“…This analytical method estimates the output of an end-of-life material generated by an application by utilizing the input to the usage stage for each application from the past and the lifetime distribution of product use for that application, which is known as the population balance model (PBM). 11,12) The generated amount estimated by this method is a theoretical value. In practice, up to the point when it is collected as scrap, there is the question of yield in product collection, disassembly, and separation processes.…”

Section: Dynamic Materials Flow Analysis (Dynamic Mfa)mentioning

confidence: 99%

Substance Flow and Stock of Chromium Associated with Cyclic Use of Steel in Japan

Oda¹,

Daigo²,

Matsuno³

et al. 2010

ISIJ Int.

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Previous studies have pointed out that some quantity of ferritic stainless steel scrap is mixed with carbon steel scrap, thus resulting in the accumulation of chromium in carbon steel products. Chromium substance flow accounts for 97 % of its consumption in Japan. This paper describes the analysis chromium substance flow in stainless steel, other alloy steels, and carbon steel in Japan. The analysis was carried out using dynamic modeling. To classify the different kinds of alloys, stainless steel is subdivided into 13Cr, 18Cr, Cr-Ni, and Cr-Ni-Mo. Heat-resistant steel, structural alloy steel, bearing steel, and spring steel are also considered as steel alloys that include chromium. Carbon steel is classified into BOF (basic oxygen furnace) carbon steel and EAF (electric arc furnace) carbon steel to reflect differences in the raw materials from which they are composed. It was found that in-use stocks of chromium in the forms of stainless steel and other alloy steel in 2005 were 3.4 and 0.7 Tg, respectively. Other chromium stock as an alloying element in carbon steel was estimated as being 0.7 Tg in 2005, and it is dissipated in the carbon steel cycle. From the results of the dynamic model, the rates of ferritic stainless steel and other alloy steel recovered as carbon steel were approximately 40 and 80 %, respectively, in 2005. Chromium accumulation in EAF carbon steel was dynamically analyzed from 1990 to 2030. On the basis of the assumption that future steel demand will be the same as the current demand, it is predicted that the mean chromium content in EAF carbon steel will gradually increase and reach 0.24 % in 2030.KEY WORDS: recyclability of chromium; recovery rate; chromium contamination; downgraded recycling; material stock accounting.

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“…18) Lifetime distribution functions were set for each end-use product for use in the model. Input to the use stage by enduse was estimated as follows: Yield loss during fabrication processes was subtracted from the demand for semifinished products.…”

Section: Allocating End-use Categoriesmentioning

confidence: 99%

“…18) In this study, they were calculated by multiplying the previous years' inputs of each end-use category by each lifetime distribution. The average lifetime of products were set as follows: "construction", 34.5 years; "buildings", 28.9 years; "machines" and "other products", 12.1 years; while "passenger vehicles" and "trucks" were set annually using statistical data.…”

Section: Dynamic Approachmentioning

confidence: 99%