Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes

Marco Cinelli,Stuart R Coles,Mallikarjuna N Nadagouda,Jerzy Błaszczyński,Roman Słowiński,Rajender S Varma,Kerry Kirwan

doi:10.1016/j.jclepro.2017.06.113

Abstract

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds.

Highlights

The comprehensive assessment of the implementation of green chemistry principles (GCP) during nanosynthesis processes is a complex decision-making problem, requiring the consideration of multiple evaluation parameters
Some studies have assessed the environmental impacts of nanomaterials synthesis with life cycle assessment (LCA), though the main limitation is that data are currently lacking or are of low quality to assess the implications of synthesis processes for nanomaterials with quantitative methods, especially those which are based on bio-inspired approaches (Pati et al, 2014; Pourzahedi and Eckelman, 2015)
The challenge of assessing the “greenness” of nanosynthesis processes fits with the Multiple Criteria Decision Aiding (MCDA) methodology, and the authors of this paper previously developed another MCDA model based on Dominance-based Rough Set Approach (DRSA) for the classification of synthesis processes of silver nanoparticles depending on the GCP implementation (Cinelli et al, 2015)

Summary

Introduction

The comprehensive assessment of the implementation of green chemistry principles (GCP) during nanosynthesis processes is a complex decision-making problem, requiring the consideration of multiple evaluation parameters. In order to achieve credible and sound decision support, the uncertainties inherent in these evaluations as well as the resulting modelling strategies need to be accounted for (Dias et al, 2012). This paper proposes a methodology that considers these requirements by employing a decision support method from the Multiple Criteria Decision Aiding (MCDA) research domain. M. Cinelli et al / Journal of Cleaner Production 162 (2017) 938e948 challenging task, due to the limited information available on the specific operating conditions and the impacts associated with the employed materials (Feijoo et al, 2017; Meyer and Upadhyayula, 2014). MCDA methods have been confirmed as excellent candidates to handle heterogeneous information and uncertainties and provide intelligible comprehensive evaluations of comparable materials, processes and technologies (Cinelli et al, 2014; Singh et al, 2012)

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Results

Conclusion