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Chemical precipitation is a consolidated technique applied in wastewater treatment to remove and recover phosphorous and ammonium that remain in the effluent after the anaerobic digestion treatment. The precipitate is magnesium ammonium phosphate hexahydrate (MgNH4PO4·6H2O), also known as struvite, and it is sold as a slow-release fertiliser. However, the value of struvite is quite low and has a limited market. Furthermore, it precipitates with heavy metals and other impurities that need to be removed to make the fertiliser commercially viable. This study looked at the thermal decomposition of struvite to recover added value products and recycle the magnesium for further precipitation. A kinetic study was carried out to understand the mechanism of decomposition and the formation of the different solid phases, which is fundamental for the design and optimisation of the technology. The thermogravimetric study confirmed that thermal decomposition is possible, but ammonia could not be completely released below 250 °C. The thermal analysis also led to the determination of the energy required for the decomposition, found to be 1.87 kJ g−1, which also includes the evaporation of water and ammonia. The kinetic study through the isoconversional method showed the presence of two major reactions, and the model-fitting approach identified the diffusion model as the best fit for the first reaction. The activation energy of the first reaction found with this method was 0.24 kJ g−1, comparable with the data obtained from the isoconversional method. The two-stage decomposition reactions were proposed, and the final calcination product was confirmed as magnesium pyrophosphate, which could be used in agriculture or dissolved in diluted mineral acids solution to separate the phosphate from the magnesium.\ud \ud

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., Journal of Cleaner Production, 162, ISSN:0959-6526