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A multi-step balancing procedure for quality assessment of substance flow data
Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Arts and Sciences.
2005 (English)Report (Other academic)
Abstract [en]

All estimates of substance flows are more or less uncertain, which implies that the collected data can violate mass balance constraints that should be valid. In this article, we introduce multi-stage balancing algorithms that can accommodate prior information about mass balance constraints and uncertainty of the collected data. In particular, we formulate the balancing task as an optimization problem for a given set of prior information. If it is suspected that some flows have been overlooked, the balancing is achieved by minimizing the total increase in flows that is required to satisfy the given mass balance constraints. If the major problem consists of errors or uncertainty in the raw data, the sum of squares of all adjustments needed is minimized. We present a software prototype in which the balancing is integrated with a variety of tools for quality assessment of collected data, and use data from a previously published study of nitrogen flows in Sweden to illustrate the steps involved in the proposed algorithms.

Place, publisher, year, edition, pages
2005.
Series
LiTH-MAT-R, ISSN 0348-2960 ; 1
Keyword [en]
substance flows, mass balance, matrix operations, optimization
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-86478OAI: oai:DiVA.org:liu-86478DiVA: diva2:577854
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2012-12-17
In thesis
1. Organizing physical flow data: from input-output tables to data warehouses
Open this publication in new window or tab >>Organizing physical flow data: from input-output tables to data warehouses
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data on flows of materials and substances through the economy and the environment are collected by many different organizations and play a key role in the science of industrial ecology. In this thesis, a framework is suggested for structuring and organizing such data. First, the investigation focuses on the quantities of primary interest in material flow studies and how they can be stored and organized in a data warehouse. This process is shown to provide easy access to data, well-structured data management, a basis for knowledge discovery, and effective analysis of collected data. Secondly, a theoretical framework is proposed for handling and structuring multidimensional flow data, and for facilitating mathematics-assisted modeling in industrial ecology. In particular, it is shown how mathematical operations can be used to merge and compare flow data originating from different studies. Finally, it is illustrated how bootstrap analysis, Bayesian models and balancing procedures can be employed to systematize the quality and uncertainty assessment of physical flow data. Together, these three different aspects of handling physical flow data constitute a new framework that offers better knowledge, quality, and consistency of the data used in industrial ecology.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2005. 42 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 5
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-30083 (URN)15549 (Local ID)91-85297-55-0 (ISBN)15549 (Archive number)15549 (OAI)
Public defence
2005-09-30, Glashuset, B-huset, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-12-17

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Löfving, ErikGrimvall, Anders

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Output format
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