Life cycle assessment on the role of HS-based hydrogen via HS-methane reforming for the production of sustainable fuels.

Meeting current decarbonization targets requires a shift to a hydrogen energy nexus, yet, water is a valuable resource for hydrogen production, shifting the perspective to the use of HS instead within the context of circular economy. A comprehensive understanding of the environmental impacts, using a cradle-to-gate life cycle assessment (LCA), was developed focusing on the operation of hydrogen sulfide-methane reforming (HSMR) for H production benchmarked to conventional technologies, steam methane reforming (SMR) and SMR + carbon capture (CC), as feedstock to produce sustainable fuels (i.e.

Mapping the Flammability Space of Sustainable Refrigerant Mixtures through an Artificial Neural Network Based on Molecular Descriptors.

As the EU's mandates to phase out high-GWP refrigerants come into effect, the refrigeration industry is facing a new, unexpected reality: the introduction of more flammable yet environmentally compliant alternatives. This paradigm shift amplifies the need for a rapid, reliable screening methodology to assess the propensity for flammability of emerging fourth generation blends, offering a pragmatic alternative to laborious and time-intensive traditional experimental assessments. In this study, an artificial neural network (ANN) is meticulously constructed, evaluated, and validated to address this emerging challenge by predicting the normalized flammability index (NFI) for an extensive array of pure, binary, and ternary mixtures, reflecting a substantial diversity of compounds like CO, hydrofluorocarbons (HFCs), hydrofluoroolefins (HFOs), six saturated hydrocarbons (sHCs), hydroolefins (HOs), and others.

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning.

We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins.

Assessment of Low Global Warming Potential Refrigerants for Drop-In Replacement by Connecting their Molecular Features to Their Performance.

The use of hydrofluorocarbons (HFCs) as an alternative for refrigeration units has grown over the past decades as a replacement to chlorofluorocarbons (CFCs), banned by the Montreal's Protocol because of their effect on the depletion of the ozone layer. However, HFCs are known to be greenhouse gases with considerable global warming potential (GWP), thousands of times higher than carbon dioxide. The Kigali Amendment to the Montreal Protocol has promoted an active area of research toward the development of low GWP refrigerants to replace the ones in current use, and it is expected to significantly contribute to the Paris Agreement by avoiding nearly half a degree Celsius of temperature increase by the end of this century.

Quantifying the effect of polar interactions on the behavior of binary mixtures: Phase, interfacial, and excess properties.

In this work, polar soft-Statistical Associating Fluid Theory (SAFT) was used in a systematic manner to quantify the influence of polar interactions on the phase equilibria, interfacial, and excess properties of binary mixtures. The theory was first validated with available molecular simulation data and then used to isolate the effect of polar interactions on the thermodynamic behavior of the mixtures by fixing the polar moment of one component while changing the polar moment of the second component from non-polar to either highly dipolar or quadrupolar, examining 15 different binary mixtures. It was determined that the type and magnitude of polar interactions have direct implications on the vapor-liquid equilibria (VLE), resulting in azeotropy for systems of either dipolar or quadrupolar fluids when mixed with non-polar or low polar strength fluids, while increasing the polar strength of one component shifts the VLE to be more ideal.

Polar soft-SAFT: theory and comparison with molecular simulations and experimental data of pure polar fluids.

The consideration of polar interactions is of vital importance for the development of predictive and accurate thermodynamic models for polar fluids, as they govern most of their thermodynamic properties, making them highly non-ideal fluids. We present here for the first time the extension of the soft-SAFT equation of state (EoS), named polar soft-SAFT, to explicitly model intermolecular polar interactions (dipolar and quadrupolar), using the approach of Jog and Chapman (P. K.