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.

Deep Eutectic Solvents as Azeotrope Breakers: Liquid-Liquid Extraction and COSMO-RS Prediction

Abstract: The efficient and sustainable separation of azeotropic mixtures remains a challenge in chemical engineering. In this work, the performance of benign solvents, namely deep eutectic solvents (DES), in the separation of aromatic–aliphatic hydrocarbon azeotropic mixtures via liquid–liquid extraction (LLE) was evaluated. The DES studied in this work were based on different ammonium salts (cholinium chloride, [Ch]Cl, benzylcholinium chloride, [BzCh]Cl, and tetrabutylammonium chloride, [N4444]Cl) as hydrogen bond acceptor (HBA) and one organic acid (levulinic acid, LevA) as hydrogen bond donor (HBD), always in the mole ratio of 1 HBA:2 HBD. The thermophysical properties, namely density and viscosity, of the three used DES were measured in the temperature range T = (293.15 up to 353.15) K and at atmospheric pressure. The phase equilibria diagrams of all ternary systems were determined at T = 298.15 K and at atmospheric pressure using 1H NMR spectroscopy. The results showed that the introduction of a more hydropho...

Recent advances in the pretreatment of lignocellulosic biomass for biofuels and value-added products

Abstract: Many countries in the world aim to achieve sustainable development goals by 2030 following ambitious climate change mitigation, and thus, the concept of sustainable biorefinery has attracted immense research and development around the world. The concept of the biorefinery is centrally based on the conversion of biomass into biofuels and value-added products. Nevertheless, lowering the recalcitrance of the lignocellulosic matrix in a cost-effective and environmentally benign manner is a crucial pretreatment step. Different pretreatment methods have been categorized as physical, chemical, biological, physicochemical, and combined. Recently, some novel ionic liquids have also emerged as promising sustainable pretreatment solutions for use of lignocellulosic waste on a large scale. This review briefly presents recent findings on the pretreatment for the conversion of lignocellulosic materials into fuel and value-added products.

Hydrogen-bond acidity of ionic liquids: an extended scale

Abstract: One of the main drawbacks comprising an appropriate selection of ionic liquids (ILs) for a target application is related to the lack of an extended and well-established polarity scale for these neoteric fluids. Albeit considerable progress has been made on identifying chemical structures and factors that influence the polarity of ILs, there still exists a high inconsistency in the experimental values reported by different authors. Furthermore, due to the extremely large number of possible ILs that can be synthesized, the experimental characterization of their polarity is a major limitation when envisaging the choice of an IL with a desired polarity. Therefore, it is of crucial relevance to develop correlation schemes and a priori predictive methods able to forecast the polarity of new (or not yet synthesized) fluids. In this context, and aiming at broadening the experimental polarity scale available for ILs, the solvatochromic Kamlet–Taft parameters of a broad range of bis(trifluoromethylsulfonyl)imide-([NTf2]−)-based fluids were determined. The impact of the IL cation structure on the hydrogen-bond donating ability of the fluid was comprehensively addressed. Based on the large amount of novel experimental values obtained, we then evaluated COSMO-RS, COnductor-like Screening MOdel for Real Solvents, as an alternative tool to estimate the hydrogen-bond acidity of ILs. A three-parameter model based on the cation–anion interaction energies was found to adequately describe the experimental hydrogen-bond acidity or hydrogen-bond donating ability of ILs. The proposed three-parameter model is also shown to present a predictive capacity and to provide novel molecular-level insights into the chemical structure characteristics that influence the acidity of a given IL. It is shown that although the equimolar cation–anion hydrogen-bonding energies (EHB) play the major role, the electrostatic-misfit interactions (EMF) and van der Waals forces (EvdW) also contribute, admittedly in a lower extent, towards the hydrogen-bond acidity of ILs. The new extended scale provided for the hydrogen-bond acidity of ILs is of high value for the design of new ILs for task-specific applications.

A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification

Abstract: Feature selection is imperative in machine learning and data mining when we have high-dimensional datasets with redundant, nosy and irrelevant features. The area of feature selection deals reducing the dimensionality of data and selecting only the most relevant features to increase the classification performance and reduce the computational cost. This problem has exponential growth, which makes it challenging specially for datasets with a large number of features. To solve this problem, a wide range of optimization algorithms are used of which grey wolf optimizer (GWO) is a recent one. This book chapter provides a brief review of the latest works on feature selection using GWO.

Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Abstract: Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.