Showing papers by "Universiti Teknologi Malaysia published in 2014"

Hydrogenation of CO2 to value-added products—A review and potential future developments

Abstract: Fossil fuel depletion, global warming, climate change, and steep hikes in the price of fuels are driving scientists to investigate on commercial and environmentally friendly fuels. The process of CO2 conversion to value-added products has been considered as a possible remedy to fulfill the requirements. The present review paper comprehensively discusses two different processes, namely hydrocarbon and methanol synthesis which are extensively used to convert CO2 to value-added products. Reaction mechanisms as well as the effects of catalyst, reactor type and operating conditions on product efficiency enhancement of each process are reviewed. Furthermore a brief overview on the reactor types as the most effective component of the theoretical and experimental reported results on the process improvement is given. All the information is tabulated in order to make the gathered information easily conclusive. Finally, by taking the available information into account the best reactor configuration which is adjustable to reaction mechanism is proposed.

Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

Abstract: Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the

Energy storage systems for renewable energy power sector integration and mitigation of intermittency

Abstract: Currently, the electric power sector is looking forward towards increasing the bent for availability, reliability and security of energy supply to consumers. This pursuit has vehemently increased the intention for integrating renewable energy (RE) into the electricity sector as a strategy to curb the problem of energy deficiency especially in isolated off-grid settlements. However, the variability in the sources of RE supply coupled with conditional changes in the level of energy consumption with respect to time has brought to focus the necessity for energy storage systems (ESSs). Despite the stochastic nature of RE produced from solar and wind energy and to some extent hydro, interest in their exploitation is still growing high due to their sustainability regarding environmental receptiveness. Thus, this paper extensively reviews the state of the art of three different kinds of energy storage technologies (pumped hydroelectricity storage, batteries and fuel cells) suitable for the integration and management of intermittency in RE. Within the context of the review, advantages and disadvantages of the various technologies are also presented. Additionally, it also pin-points on the different areas of applications of ESSs for RE integration and offers review summary on factors to be considered for selecting appropriate energy storage technology for either commercial or domestic applications. Finally, the paper concluded that ESSs selection is based on performance characteristics and fuel source used whereas no single ESS can meet all the possible requirements to be called a supreme ESS.

Highly active Ni-promoted mesostructured silica nanoparticles for CO2 methanation

Abstract: Mesostructured silica nanoparticles (MSN) and Ni loaded onto MSN (Ni/MSN) for the methanation of CO2 were prepared by the sol–gel and impregnation methods. Catalytic testing was conducted in the temperature range of 423–723 K under atmospheric pressure in the presence of H2. Ni supported on MSN was compared with others types of support such as MCM-41 (Mobile Crystalline Material), HY (protonated Y zeolite), SiO2 and γ-Al2O3. The activity of CO2 methanation followed the order: Ni/MSN > Ni/MCM-41 > Ni/HY > Ni/SiO2 > Ni/γ-Al2O3. The nitrogen physisorption and pyrrole adsorbed IR spectroscopy results indicated that the high activity of Ni/MSN is due to the presence of both intra- and inter-particle porosity which led to the high concentration of basic sites. In addition, the correlation between N–H band intensity and the turnover frequency revealed that the methanation activity increased with increasing of the concentration of basic sites. The presence of defect sites or oxygen vacancies in MSN was responsible for the formation of surface carbon species, while Ni sites dissociated hydrogen to form atomic hydrogen. The surface carbon species then interacted with atomic hydrogen to form methane. The Ni/MSN catalyst performed with good stability and no deactivation up to 200 h.

A systematic literature review of software requirements prioritization research

Abstract: Context: During requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases It is a major step taken in making crucial decisions so as to increase the economic value of a system Objective: The purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions Method: Search terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins Results: 73 Primary studies were selected from the search processes Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13 Conclusion: Prioritization has been significantly discussed in the requirements engineering domain However, it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues Also, the applicability of existing techniques in complex and real setting has not been reported yet

Extracting hydroxyapatite and its precursors from natural resources

Abstract: Healing of segmental bone defects remain a difficult problem in orthopedic and trauma surgery. One reason for this difficulty is the limited availability of bone material to fill the defect and promote bone growth. Hydroxyapatite (HA) is a synthetic biomaterial, which is chemically similar to the mineral component of bones and hard tissues in mammals and, therefore, it can be used as a filler to replace damaged bone or as a coating on implants to promote bone in-growth into prosthetic implants when used in orthopedic, dental, and maxillofacial applications. HA is a stoichiometric material with a chemical composition of Ca10(PO4)6(OH)2, while a mineral component of bone is a non-stoichiometric HA with trace amounts of ions such as Na+, Zn2+, Mg2+, K+, Si2+, Ba2+, F−, CO32−, etc. This review looks at the progress being made to extract HA and its precursors containing trace amount of beneficial ions from biological resources like animal bones, eggshells, wood, algae, etc. Properties, such as particle size, morphology, stoichiometry, thermal stability, and the presence of trace ions are studied with respect to the starting material and recovery method used. This review also highlights the importance of extracting HA from natural resources and gives future directions to the researcher so that HA extracted from biological resources can be used clinically as a valuable biomaterial.

Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization

Abstract: Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding areas. In recent years, several researches have been done to predict flyrock and ground vibration by means of conventional backpropagation (BP) artificial neural network (ANN). However, the convergence rate of the BP-ANN is relatively slow and solutions can be trapped at local minima. Since particle swarm optimization (PSO) is a robust global search algorithm, it can be used to improve ANNs' performance. In this study, a novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN. This approach was applied to simulate the flyrock distance and peak particle velocity (PPV) induced by blasting. PSO parameters and optimal network architecture were determined using sensitivity analysis and trial and error method, respectively. Finally, a model was selected, and the proposed model was trained and tested using 44 datasets obtained from three granite quarry sites in Malaysia. Each dataset involved ten inputs, including the most influential parameters on flyrock distance and PPV, and two outputs. The results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy. Sensitivity analysis was also conducted to determine the influence of each parameter on flyrock distance and PPV. The results show that the powder factor and charge per delay are the most effective parameters on flyrock distance, whereas sub-drilling and charge per delay are the most effective parameters on PPV.

Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia

Abstract: An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

A comprehensive review of maximum power point tracking algorithms for photovoltaic systems

Abstract: In recent decades, Photovoltaic (PV) energy has made significant progress towards meeting the continuously increasing world energy demand. Besides that, the issue of conventional fossil fuels depletion as well as environmental pollution both contribute to the growth of PV technology. However, the deployment and implementation of photovoltaic systems remain as a great challenge, since the PV material cost is still very high. The low PV module conversion efficiency is another factor that restricts the wide usage of PV systems, therefore a power converter embedded with the capability of maximum power point tracking (MPPT) integrated with PV systems is essential to further the technology. This paper provides a comprehensive review of the available MPPT techniques, both the uniform insolation and partial shaded conditions. In order to appreciate the knowledge of MPPT concepts, several types of PV cell equivalent models are explained too. Conventional MPPT techniques have proven the ability to track the maximum power point (MPP) under uniform solar irradiance. However, under rapidly changing environments and partially shaded conditions, conventional techniques have failed to track the true MPP. For this reason, stochastic based methods and artificial intelligence have been developed with the ability to seek the true MPP under multiple peaks with good convergence speed. This paper analyses and compares both conventional and stochastic MPPT techniques based on the true MPP tracking capability, design complexity, cost consideration, sensitivity to environmental change and convergence speed. Comparatively, the stochastic algorithms and artificial intelligence show excellent tracking performance. The research on MPPT techniques is ongoing towards achieving a better performance in terms of the ease of implementation, low system cost and better tracking efficiency.

A review on microwave assisted pyrolysis of coal and biomass for fuel production

Abstract: The energy insecurity from oil and natural gas and increased CO2 emission from fossil fuels is driving societies to look for sustainable and renewable energy supply. The huge coal resources can serve as a potential source for fuels. Bio-energy from biomass has been recognized as renewable energy to reduce CO2 emission. Although fast pyrolysis has emerged as the most promising technology to convert organic materials to liquid fuels at shorter duration but it still faces some technical challenges in improving product yield, its quality and process energy efficiency. Microwave assisted pyrolysis of coal and biomass in the presence of microwave absorber provides distinctive environment to resolve these challenges. The microwave absorber can indirectly heat coal and biomass particles which are relatively microwave transparent and influence product yield and its quality by contributing as a catalytic precursor. The microwave heating of coal or biomass particles with microwave absorber shows efficient heating and sufficient contact of volatile or gas phase species with specific microwave absorber can improve fuel quality.

Medical Image Segmentation Methods, Algorithms, and Applications

Abstract: Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.

Impact of Metal Oxide Nanoparticles on Enhanced Oil Recovery from Limestone Media at Several Temperatures

Abstract: Recently, researchers have proved the application of nanoparticles (NPs) for enhanced oil recovery (EOR) in ambient temperature. However, to our knowledge no attempt has been undertaken experimentally to investigate the influence of NPs on EOR at higher temperatures. In this study, aluminum oxide (Al2O3), titanium dioxide (TiO2), and silicon dioxide (SiO2) were selected for EOR purposes from an intermediate-wet limestone sample at 26, 40, 50, and 60 °C. These metal NPs were dispersed in deionized water at concentration of 0.005 wt %. First, transportation of the NPs through limestone was evaluated. It was found that Al2O3 (8.2%) had the lowest and TiO2 (27.8%) and SiO2 (43.4%) had the highest adsorption on the limestone. Consequently, wettability of the limestone was changed into water-wet through NPs adsorption. The contact angle in the presence of Al2O3, TiO2, and SiO2 nanofluids was measured as 71° ± 2°, 57° ± 2°, and 26° ± 2°, respectively. Interfacial tension was also noticeably reduced with these na...

Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models

Abstract: The purpose of this study is to compare the landslide susceptibility mapping models of logistic regression (LR), analytical hierarchy process (AHP) and frequency ratio (FR) applied in the central Zab basin (West Azerbaijan—Iran). Eight factors were used for landslide susceptibility mapping including slope, aspect, land cover, precipitation, lithology and the distance to roads, drainage, and faults that affect the occurrence of landslides. To get more precision, speed and facility in our analysis, all descriptive and spatial information was entered into GIS system. Satellite images (Landsat ETM + and SPOT 5) are also used to prepare for land use and landslide-inventory mapping respectively. Landslide events as used as dependant variable and data layers as independent variable, making use of the correlation between these two variables in landslide susceptibility. The three models are validated using the relative landslide density index (R-index) and the receiver operating characteristic (ROC) curves. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the LR, AHP, and FR methods are 0.8941, 0.8115, and 0.8634, respectively. These results indicate that the LR and FR models are relatively good estimators of landslide susceptibility in the study area. The interpretations of the susceptibility map reveal that precipitation, lithology and slope played major roles in landslide occurrence and distribution in the central Zab basin. In general, all three models were reasonably accurate. The resultant maps would be useful for regional spatial planning as well as for land cover planning.

Electricity consumption from renewable and non-renewable sources and economic growth: Evidence from Latin American countries

Abstract: This study explores the effect of renewable and non-renewable electricity consumption on economic growth in 18 Latin American countries. To achieve the goal of this study a panel Gross Domestic Product (GDP) model was constructed taking the period 1980–2010 into account. From the Pedroni cointegration test results it was found that renewable electricity consumption, non-renewable electricity consumption, labor, gross fixed capital formation, and total trade are cointegrated. Moreover, the panel Dynamic Ordinary Least Squares (DOLS) test results revealed that all above the mentioned variables have a long run positive effect on GDP growth in the investigated countries. The Vector Error-Correction (VEC) Granger causality model results revealed the existence of feedback causality between the variables. The results of the study indicated that renewable electricity consumption is more significant than non-renewable electricity consumption in promoting economic growth in the investigated countries in the long run and the short run. Based on the results of this study, it is recommended that the investigated countries should increase their investment on renewable energy projects to increase the role of electricity consumption from renewable sources. In addition, it is essential that these countries should reduce their non-renewable electricity consumption by increasing their energy efficiency and implementing energy saving projects. By applying these recommendations, these countries would be able to mitigate global warming and reduce their dependency on fossil fuel to increase their energy security.

Removal of heavy metals from wastewater using carbon nanotubes

Abstract: The discovery of carbon nanotubes (CNTs) and the prospect of developing novel carbon-based nanomaterials have attracted researchers worldwide. CNTs have great potential as a novel type of adsorbent due to their unique properties such as chemical stability, mechanical and thermal stability, and the high surface area, which leads to various applications including hydrogen storage, protein purification and water treatment. Removal of heavy metals from industrial wastewater leads to the biggest challenge nowadays. To reduce environmental problems, the CNTs are promising candidates for the adsorption of heavy metals. In this study, extremely brief summaries of liquid pollutant purification are reviewed.

Renewable energy potentials in Nigeria: Meeting rural energy needs

Abstract: Nigeria is endowed with abundant energy resources, both conventional and renewable, which can potentially provide the country with a sufficient capacity to meet the ambitions of both urban and rural Nigerians of a full, nationwide electrification level. Yet, Nigeria has one of the lowest consumption rates of electricity per capita in Africa. With the demand superseding the generation, there is inequitable access of rural communities to the electricity service in the country. There are inherent obstacles militating against the effective implementation of an orderly energy policy in Nigeria. The inefficiencies overshadowing the allocation of energy resources coupled with the near depletion of fossil fuels, make it imperative for the country to exploit its huge natural renewable resources to avoid a worsening energy supply scenario and provide feasible electricity to rural dwellers. This paper presents a review of renewable energy potentials in Nigeria to be tapped for useful and uninterrupted electric energy supply. The extent of renewable energy resources is described and existing government policies are articulated. Various polices, that could possibly incentivize the realization of wider renewable energy applications in rural Nigeria, are proposed. The challenges and future prospects of renewable energy are also discussed. Dissemination of decentralized renewable energy resources will not only improve the wellbeing of rural Nigerian communities, but also enhance Nigeria's energy and economic prospects for potential global investment.