Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

High temperature adsorption of carbon dioxide on Cu-Al hydrotalcite-derived mixed oxides: kinetics and equilibria by thermogravimetry

Abstract: The effect of Cu/Al molar ratio on the high-temperature adsorption characteristics of CO2 on the mixed oxides of Cu–Al hydrotalcite skeletal structure has been studied by thermogravimetry. The Cu/Al molar ratio of the hydrotalcites synthesized was varied between 1.0 and 3.0, and the adsorption temperature ranged from ambient to 600 °C. The hydrotalcite with Cu/Al molar ratio of 2.0 was found to be the most suitable adsorbent for high-temperature CO2 adsorption, in both the capacity and the rate of adsorption. The activation energy values suggested that the physical adsorption dominates at low temperatures ( 400 °C).

Thermophysical Properties of 1-Propyronitrile-3-alkylimidazolium Bromide Ionic Liquids at Temperatures from (293.15 to 353.15) K

Abstract: In the present work, a series of 1-propyronitrile-3-alkylimidazolium bromide ionic liquids ([C2CN Cnim]Br, where n = 4, 6, 8, and 10) were synthesized and characterized using Fourier transform infrared spectroscopy (FTIR), NMR, and elemental analysis Physical properties such as density, viscosity, and refractive index are measured and reported for a temperature range of (29315 to 35315) K and at atmospheric pressure The influence of the alkyl chain on these properties is discussed The present synthesized ionic liquids show a weak temperature dependency on the thermal expansion coefficient

Feature selection method based on grey wolf optimization for coronary artery disease classification

Abstract: Cardiovascular disease has been declared as one of the deadly illness that affects humans in the Middle and Old ages across the globe. One of the cardiovascular disease known as Coronary artery, has recorded the highest number of motility rates in the recent years. Machine learning tools have been very effective in investigating the causes of such lethal disease which involve analyzing large amount of dataset. Such datasets might contain redundant and irrelevant features which affect the classification accuracy and processing speed. Hence, applying feature selection technique for the elimination of the said redundant and irrelevant features is necessary. In this paper, a novel wrapper feature selection method is proposed to determine the optimal feature subset for diagnosing coronary artery disease. This proposed method consists of two main stages feature selection and classification. In the first stage, Grey Wolf Optimization (GWO) is used to find the best features in the disease identification dataset. In the second stage, the fitness function of GWO is evaluated using Support Vector Machine classifier (SVM). Cleveland Heart disease dataset is used for performance validation of the proposed method. The experimental results showed that, the proposed GWO-SVM outperforms current existing approaches with an achievement of 89.83% in accuracy, 93% in sensitivity and 91% in specificity rates.