Approximate life-cycle assessment of product concepts using learning systems

This thesis develops an approximate, analytically based environmental assessment method that provides fast evaluations of product concepts. Traditional life-cycle assessment (LCA) studies and their streamlined analytical versions are costly, time-consuming, and data intensive. Thus, they are not practical to apply during early concept design phases where little information is available and ideas change quickly. Alternatives currently used are mostly qualitative, ad-hoc approaches that often provide overly simplistic assessments difficult to trade-off with other design objectives. The Learning Surrogate LCA method is an alternative approach that uses simple, high-level, and accessible descriptive information about a product to provide approximate, yet useful, analytical LCA results during early concept design stages. The method relies on a general artificial neural network (ANN) trained on high-level product descriptors and environmental performance data from pre-existing detailed life-cycle assessment studies or related data. To quickly obtain an approximate environmental impact assessment for a product concept, the design team queries the trained artificial model with new set of descriptors, without requiring the development of a new model. The predicted environmental performance, along with other key performance measures, can be used in tradeoff analysis and concept selection. Foundations for the approach were established by investigating: (1) model inputs in the form of a compact, and meaningful set of product concept descriptors; (2) ability to gather data and appropriately train an ANN-based surrogate LCA model.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2002

Author dc:creator

• Sousa, Inês (Maria Inês Silva Sousa), 1972-

Advisor dc:contributor.advisor

• David R. Wallace.

• Technology, Management, and Policy Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/29917

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29917