Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries

Nonisothermal Cure Behavior and Kinetics of Cerium‐doped Fe 3 O 4 /Epoxy Nanocomposites

International audienc

Effect of Surface Treatment of Halloysite Nanotubes (HNTs) on the Kinetics of Epoxy Resin Cure with Amines

International audienc

AN INSIGHT INTO THERMAL PROPERTIES OF BC3-GRAPHENE HETERO-NANOSHEETS: A MOLECULAR DYNAMICS STUDY

Simulation of thermal properties of graphene hetero-nanosheets is a key step in understanding their performance in nano-electronics where thermal loads and shocks are highly likely. Herein we combine graphene and boron-carbide nanosheets (BC3N) heterogeneous structures to obtain BC3N-graphene hetero-nanosheet (BC3GrHs) as a model semiconductor with tunable properties. Poor thermal properties of such heterostructures would curb their long-term practice. BC3GrHs may be imperfect with grain boundaries comprising non-hexagonal rings, heptagons, and pentagons as topological defects. Therefore, a realistic picture of the thermal properties of BC3GrHs necessitates consideration of grain boundaries of heptagon-pentagon defect pairs. Herein thermal properties of BC3GrHs with various defects were evaluated applying molecular dynamic (MD) simulation. First, temperature profles along BC3GrHs interface with symmetric and asymmetric pentagon-heptagon pairs at 300 K, ΔT= 40 K, and zero strain were compared. Next, the efect of temperature, strain, and temperature gradient (ΔT) on Kaptiza resistance (interfacial thermal resistance at the grain boundary) was visualized. It was found that Kapitza resistance increases upon an increase of defect density in the grain boundary. Besides, among symmetric grain boundaries, 5–7–6–6 and 5–7–5–7 defect pairs showed the lowest (2 × ­10–10 m2 K ­W−1) and highest (4.9× ­10–10 m2 K ­W−1) values of Kapitza resistance, respectively. Regarding parameters afecting Kapitza resistance, increased temperature and strain caused the rise and drop in Kaptiza thermal resistance, respectively. However, lengthier nanosheets had lower Kapitza thermal resistance. Moreover, changes in temperature gradient had a negligible efect on the Kapitza resistanc

Kinetics of Cross-Linking Reaction of Epoxy Resin with Hydroxyapatite-Functionalized Layered Double Hydroxides

The cure kinetics analysis of thermoset polymer composites gives useful information about their properties. In this work, two types of layered double hydroxide (LDH) consisting of Mg2+ and Zn2+ as divalent metal ions and CO32− as an anion intercalating agent were synthesized and functionalized with hydroxyapatite (HA) to make a potential thermal resistant nanocomposite. The curing potential of the synthesized nanoplatelets in the epoxy resin was then studied, both qualitatively and quantitatively, in terms of the Cure Index as well as using isoconversional methods, working on the basis of nonisothermal differential scanning calorimetry (DSC) data. Fourier transform infrared spectroscopy (FTIR) was used along with X-ray diffraction (XRD) and thermogravimetric analysis (TGA) to characterize the obtained LDH structures. The FTIR band at 3542 cm−1 corresponded to the O–H stretching vibration of the interlayer water molecules, while the weak band observed at 1640 cm−1 was attributed to the bending vibration of the H–O of the interlayer water. The characteristic band of carbonated hydroxyapatite was observed at 1456 cm−1. In the XRD patterns, the well-defined (00l) reflections, i.e., (003), (006), and (110), supported LDH basal reflections. Nanocomposites prepared at 0.1 wt % were examined for curing potential by the Cure Index as a qualitative criterion that elucidated a Poor cure state for epoxy/LDH nanocomposites. Moreover, the curing kinetics parameters including the activation energy (Eα), reaction order, and the frequency factor were computed using the Friedman and Kissinger–Akahira–Sunose (KAS) isoconversional methods. The evolution of Eα confirmed the inhibitory role of the LDH in the crosslinking reactions. The average value of Eα for the neat epoxy was 54.37 kJ/mol based on the KAS method, whereas the average values were 59.94 and 59.05 kJ/mol for the epoxy containing Zn-Al-CO3-HA and Mg Zn-Al-CO3-HA, respectively. Overall, it was concluded that the developed LDH structures hindered the epoxy curing reactions

Polyurethane/Silane-Functionalized ZrO2 Nanocomposite Powder Coatings: Thermal Degradation Kinetics

International audienc

The effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19:A systematic review and meta-analysis

BACKGROUND: Vaccination, one of the most important and effective ways of preventing infectious diseases, has recently been used to control the COVID-19 pandemic. The present meta-analysis study aimed to evaluate the effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19. METHODS: A systematic search was performed independently in Scopus, PubMed via Medline, ProQuest, and Google Scholar electronic databases as well as preprint servers using the keywords under study. We used random-effect models and the heterogeneity of the studies was assessed using I(2) and χ(2) statistics. In addition, the Pooled Vaccine Effectiveness (PVE) obtained from the studies was calculated by converting based on the type of outcome. RESULTS: A total of 54 studies were included in this meta-analysis. The PVE against SARS-COV 2 infection were 71% [odds ratio (OR) = 0.29, 95% confidence intervals (CI): 0.23–0.36] in the first dose and 87% (OR = 0.13, 95% CI: 0.08–0.21) in the second dose. The PVE for preventing hospitalization due to COVID-19 infection was 73% (OR = 0.27, 95% CI: 0.18–0.41) in the first dose and 89% (OR = 0.11, 95% CI: 0.07–0.17) in the second dose. With regard to the type of vaccine, mRNA-1273 and combined studies in the first dose and ChAdOx1 and mRNA-1273 in the second dose had the highest effectiveness in preventing infection. Regarding the COVID-19-related mortality, PVE was 68% (HR = 0.32, 95% CI: 0.23–0.45) in the first dose and 92% (HR = 0.08, 95% CI: 0.02–0.29) in the second dose. CONCLUSION: The results of this meta-analysis indicated that vaccination against COVID-19 with BNT162b2 mRNA, mRNA-1273, and ChAdOx1, and also their combination, was associated with a favorable effectiveness against SARS-CoV2 incidence rate, hospitalization, and mortality rate in the first and second doses in different populations. We suggest that to prevent the severe form of the disease in the future, and, in particular, in the coming epidemic picks, vaccination could be the best strategy to prevent the severe form of the disease. SYSTEMATIC REVIEW REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42021289937]