Dataset Information

Survey of Data and Models for Refrigerant Mixtures Containing Halogenated Olefins.

ABSTRACT: We survey existing data for refrigerant blends containing halogenated olefins (hydrofluoroolefins (HFO), hydrochlorofluoroolefins (HCFO) and hydrochloroolefins (HCO)) in the open literature. The data are primarily taken from the NIST SOURCE database and are presented in graphical form to demonstrate the relative coverage of the data. The primary conclusion is that blends containing halogenated olefins remain only sparsely measured in experiments, and some classes of data (e.g., speed-of-sound data) are particularly sparse for blends containing halogenated olefins. The second part of this study compares the thermodynamic models in NIST REFPROP against the experimental datasets and identifies systems (of which there are many) where refitting of the thermodynamic model is required.

SUBMITTER: Bell IH

PROVIDER: S-EPMC9805321 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Survey of Data and Models for Refrigerant Mixtures Containing Halogenated Olefins.

Bell Ian H IH Riccardi Demian D Bazyleva Ala A McLinden Mark O MO

Journal of chemical and engineering data 20210601 6

We survey existing data for refrigerant blends containing halogenated olefins (hydrofluoroolefins (HFO), hydrochlorofluoroolefins (HCFO) and hydrochloroolefins (HCO)) in the open literature. The data are primarily taken from the NIST SOURCE database and are presented in graphical form to demonstrate the relative coverage of the data. The primary conclusion is that blends containing halogenated olefins remain only sparsely measured in experiments, and some classes of data (e.g., speed-of-sound da ...[more]

PMID: 36590016

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Project description: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 CO2, hydrofluorocarbons (HFCs), hydrofluoroolefins (HFOs), six saturated hydrocarbons (sHCs), hydroolefins (HOs), and others. The optimal configuration ([61 (I) × 14 (HL1) × 24 (HL2) × 1 (O)]) demonstrated a profound fit to the data, with metrics like R 2 of 0.999, root-mean-square error (RMSE) of 0.1735, average absolute relative deviation (AARD)% of 0.8091, and SDav of ±0.0434. Exhaustive assessments were conducted to ensure the most efficient architecture without compromising the accuracy. Additionally, the analysis of the standardized residuals (SDR) and applicability domain (AD) exhibited fine control and consistency over the data points. External validation using quaternary mixtures further attested to the model's adaptability and predictive capability. The exploration into the relative contribution of descriptors led to the identification of 23 significant sigma descriptors derived from conductor-like screening model (COSMO), responsible for 90.98% of the total contribution, revealing potential avenues for model simplification without a substantial loss in predictive power. Moreover, the model successfully predicted the behavior of prospective industry-relevant mixtures, reinforcing its reliability and opening the door to experimentation with untested blends. The results collectively manifest the developed ANN's efficiency, robustness, and adaptability in modeling flammability, catering to the demands of industry standards, environmental concerns, and safety requirements.

Project description:BackgroundThere are a variety of bioinformatic pipelines and downstream analysis methods for analyzing 16S rRNA marker-gene surveys. However, appropriate assessment datasets and metrics are needed as there is limited guidance to decide between available analysis methods. Mixtures of environmental samples are useful for assessing analysis methods as one can evaluate methods based on calculated expected values using unmixed sample measurements and the mixture design. Previous studies have used mixtures of environmental samples to assess other sequencing methods such as RNAseq. But no studies have used mixtures of environmental to assess 16S rRNA sequencing.ResultsWe developed a framework for assessing 16S rRNA sequencing analysis methods which utilizes a novel two-sample titration mixture dataset and metrics to evaluate qualitative and quantitative characteristics of count tables. Our qualitative assessment evaluates feature presence/absence exploiting features only present in unmixed samples or titrations by testing if random sampling can account for their observed relative abundance. Our quantitative assessment evaluates feature relative and differential abundance by comparing observed and expected values. We demonstrated the framework by evaluating count tables generated with three commonly used bioinformatic pipelines: (i) DADA2 a sequence inference method, (ii) Mothur a de novo clustering method, and (iii) QIIME an open-reference clustering method. The qualitative assessment results indicated that the majority of Mothur and QIIME features only present in unmixed samples or titrations were accounted for by random sampling alone, but this was not the case for DADA2 features. Combined with count table sparsity (proportion of zero-valued cells in a count table), these results indicate DADA2 has a higher false-negative rate whereas Mothur and QIIME have higher false-positive rates. The quantitative assessment results indicated the observed relative abundance and differential abundance values were consistent with expected values for all three pipelines.ConclusionsWe developed a novel framework for assessing 16S rRNA marker-gene survey methods and demonstrated the framework by evaluating count tables generated with three bioinformatic pipelines. This framework is a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods and will help scientists identify appropriate analysis methods for their marker-gene surveys.

Dataset Information

Survey of Data and Models for Refrigerant Mixtures Containing Halogenated Olefins.

Publications

Survey of Data and Models for Refrigerant Mixtures Containing Halogenated Olefins.

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