Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Hydrogen donor solvents in liquefaction of biomass: A review

Abstract: The environmental impact of global warming, caused by greenhouse gases has also fuelled the needs to utilise biomass, as its energy utilisation creates less environmental pollution and fewer health risks than fossil fuel combustion. Liquefaction of biomass using hydrogen donor solvents is a promising route to obtain clean biofuel using various solvents at moderate to high temperature (250–460 °C) and pressure (150–320 bar). Solvents such as sub-and supercritical water, alcohol, decalin, glycerol and tetralin can be used as potential hydrogen donor to enhance liquid oil yield with a reduced of oxygen content. Supercritical water with its excellent transport properties as well as hydrogen donor capability leads to hydrothermal decomposition of biomass and enhancing various compounds depending upon operating parameters. The selection of alcohol as a solvent related to the action of hydrogen donor and to its alkylating ability. The hydrogen donor solvents provide an alternative to hydrogen gas as a reducing gas. The advantage of using hydrogen donor solvent is to stabilise the free radical in the biomass liquefaction and yielding a higher product conversion. Compared with non-hydrogen donor solvents, hydrogen donor solvents such as tetralin and decalin show significant improvement not only in conversion and product distribution to liquid but also on the quality of bio-oil (oxygen content) due to the improvement of hydrogenation and hydrocracking reactions with inhibition of polycondensation. The advantage of hydrogen donor solvents over the molecular hydrogen due to a lower strength bonding of C-H as compared to H-H bond. A review on performances of water, alcohols and other hydrogen donor solvents in liquefaction of biomass has been made. The yield of hydrogen donated in the reaction has also been reported.

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Abstract: Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

Green Synthesis of Gold Nanoparticles Using Aqueous Extract of Garcinia mangostana Fruit Peels

Abstract: The synthesis of gold nanoparticles (Au-NPs) is performed by the reduction of aqueous gold metal ions in contact with the aqueous peel extract of plant, Garcinia mangostana (G. mangostana). An absorption peak of the gold nanoparticles is observed at the range of 540–550źnm using UV-visible spectroscopy. All the diffraction peaks at 2ź = 38.48°, 44.85°, 66.05°, and 78.00° that index to (111), (200), (220), and (311) planes confirm the successful synthesis of Au-NPs. Mostly spherical shape particles with size range of 32.96 ± 5.25źnm are measured using transmission electron microscopy (TEM). From the FTIR results, the peaks obtained are closely related to phenols, flavonoids, benzophenones, and anthocyanins which suggest that they may act as the reducing agent. This method is environmentally safe without the usage of synthetic materials which is highly potential in biomedical applications.