In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set-based machine learning is used to generate rule-based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules.
In the midst of a climate crisis, alternative and low-carbon energy resources must be put to scale in order to achieve carbon emission reductions in the coming decades. In this respect, hydrogen has gained attention as an alternative energy carrier. Hydrogen can be produced from methods that are commonly classified by a range of colours. However, each hydrogen source has its own challenges in terms of energy security, energy equity, and environmental sustainability. This perspective offers insights about the critical role that Process Systems Engineering (PSE) will play in addressing these key challenges. We also present suggestions on possible future PSE studies in the area of the hydrogen economy.
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