Combined use of MILP and multi-linear regression to simplify LCA studies. (2nd November 2015)

• Record Type: Journal Article
• Authors: Pascual-González, Janire; Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Jiménez-Esteller, Laureano
• Is Part Of: Computers & chemical engineering. Volume 82(2015)
• Page Start: 34
• Page End: 43
• Publication Date: 2015-11-02
• Subjects: Multi-linear regression -- Streamlined LCA analysis -- Environmental impact prediction -- Mixed-integer linear programming, Life cycle assessment; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2015.06.002
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: This work introduces a systematic streamlined LCA method. Our method combines multi-linear regression and mixed-integer linear programming. The model predicts impact values from a reduced number of proxy LCIA metrics. Our approach has been applied to data retrieved from ecoinvent. Few impacts suffice to describe the environmental performance of a process. Abstract: Life cycle assessment (LCA) has become the prevalent approach for quantifying the environmental impact of products over their entire life cycle. Unfortunately, LCA studies require large amounts of data that are difficult to collect in practice, which makes them expensive and time consuming. This work introduces a method that simplifies standard LCA studies by using proxy metrics that are identified following a systematic approach. Our method, which combines multi-linear regression and mixed-integer linear programming, builds in an automatic manner simplified multi-linear regression models of impact that predict (with high accuracy) the damage in different environmental categories from a reduced number of proxy metrics. Our approach was applied to data retrieved from ecoinvent. Numerical results show that few indicators suffice to describe the environmental performance of a process with high accuracy. Our findings will help develop general guidelines for simplified LCA studies that will focus on quantifying a reduced number of key indicators.