Decision-making based on sustainability analysis using GREENSCOPE

Dias R.N.; Filipe R.M.; Matos H.A.

Key information

Authors:

Dias R.N. (Ricardo Nunes Dias); Filipe R.M. (Rui Manuel Gouveia Filipe); Matos H.A. (Henrique Aníbal Santos de Matos)

Published in

03/01/2024

Abstract

<jats:title>Abstract</jats:title><jats:p>Current economic and environmental pressure encourages companies to invest and reform processes in a sustainable way. However, the decision on the best sustainable investment in a given industry may not be as straightforward as given by the independent economic and environmental indicators. Analyzing such indicators for investment in process alternatives is a complex multi-objective decision-making process. Many methods can help solve multi-objective problems, GREENSCOPE suggests using the Addictive Utility Method, which may have some shortcomings. In this work, the Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are applied to the indicator scores obtained with the GREENSCOPE methodology. The Analytical Hierarchy Process is applied with Marginal Rate of Substitution, to obtain the priority matrices. MATLAB was used to apply the methods and through an algorithm, to determine the adequate number of indicators to be used. Three cases were studied, namely a biorefinery plant, and two academic case studies on the production of acetaldehyde from ethanol and propylene glycol from glycerol. The two academic cases were modeled in Aspen Plus, to obtain process data, and linked to GREENSCOPE using MATLAB. The methodology was applied to some proposed alternatives where the sustainability improvement of energy integration and material recycling was tested. The new methodology was able to identify the more sustainable case among the alternatives. This methodology based on TOPSIS proved to be the best choice, avoiding the weighting of indicators, and providing an expected analytical decision among alternatives.</jats:p> <jats:p><jats:bold>Graphical abstract</jats:bold></jats:p>