Fuzzy data reconciliation in reacting and non-reacting process data for life cycle inventory analysis

College

Gokongwei College of Engineering

Department/Unit

Chemical Engineering

Document Type

Article

Source Title

Journal of Cleaner Production

Volume

15

Issue

10

First Page

944

Last Page

949

Publication Date

2007

Abstract

Data uncertainty is a critical issue in life cycle inventory analysis (LCI). Recent work has demonstrated that fuzzy mathematics provides

a computationally efficient alternative to probabilistic methods for representing data uncertainty. One specific problem is the utilization of dif- ferent, and potentially conflicting, LCI data sources such as physical measurements, estimates or databases. A fundamental requirement of a valid

LCI is that the data must not violate material and energy balance principles; however, data from diverse sources may result in inconsistencies. Normally such inconsistencies in LCI data can be addressed through the use of data reconciliation methods based on probability theory. This paper presents an alternative data reconciliation method based on fuzzy mathematical programming. Two LCI case studies are included to illustrate the methodology.

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Digitial Object Identifier (DOI)

10.1016/j.jclepro.2005.09.001

Disciplines

Chemical Engineering

Keywords

Fuzzy systems

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