Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104487
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Title: Sustainability assessment framework for chemical processes selection under uncertainties : a vector-based algorithm coupled with multicriteria decision-making approaches
Authors: Xu, D
Lv, L
Dong, L
Ren, J 
He, C
Manzardo, A
Issue Date: 13-Jun-2018
Source: Industrial and engineering chemistry research, 13 June 2018, v. 57, no. 23, p. 7999-8010
Abstract: This article aims to develop a generic sustainability assessment framework for helping the stakeholders/decision-makers to prioritize chemical process alternatives under uncertainties. A comprehensive evaluation system that consists of both hard and soft criteria from the environmental, economic, social–political, and technical concerns was first constructed in the framework, in which different types of uncertainties with respect to the hard and soft criteria can be properly addressed by using the interval parameter and fuzzy analytic hierarchy process method, respectively. The fuzzy decision-making trial and evaluation laboratory-based analytic network process method, which can tackle the interdependences between the evaluation criteria and the uncertainty among humans’ judgments, was employed for weighting the criteria accurately. Afterward, a novel interval vector-based algorithm was developed for rigorously prioritizing the alternative processes via the integration of both the absolute sustainability performance and relative sustainability balance of each chemical process under uncertainty. The proposed framework was illustrated by a case study to prioritize the sustainability of five ammonia production processes. The robustness of the assessment result was tested by conducting the sensitivity analysis, while the effectiveness and advantages of the proposed framework were demonstrated by comparing the results derived by this framework with those determined using other multicriteria decision-making approaches.
Publisher: American Chemical Society
Journal: Industrial and engineering chemistry research 
ISSN: 0888-5885
EISSN: 1520-5045
DOI: 10.1021/acs.iecr.8b01679
Rights: © 2018 American Chemical Society
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Industrial & Engineering Chemistry Research, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.iecr.8b01679.
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