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.

Review of municipal solid waste management options in Malaysia, with an emphasis on sustainable waste-to-energy options

Abstract: A beautiful and clean environment is the desire of every society. Malaysia is facing an uncontrolled increase in municipal solid waste (MSW) generation due to population growth, economic advancement, and industrialization, but the current, most common waste disposal practice of landfilling is not sustainable. The increasing standard of living also saps more energy from the power generation systems in which fossil fuels are the major source of fuel for the plants. Malaysia generates about 0.5–1.9 kg/capita/day of MSW; a total of about 25,000 tonnes/day of MSW is currently generated and is estimated to exceed 30,000 tonnes/day by 2020. Malaysian MSW is mainly composed of 45 % food waste, 24 % plastic, 7 % paper materials, 6 % metal, 4 % wood and 3 % glass, which are commingled, and is thus characterised by 52–66 % moisture content. Currently, 80–95 % of collected MSW is landfilled and 5 % is recycled, while composting and energy recovery are rarely practiced. This paper reviews the solid waste practice in Malaysia and looks into alternative management options for sustainability. Malaysia MSW represents recyclable power and energy potential if properly sorted. This study considered the practice of sorting at the source and the use of combustible MSW components as fuel to generate heat for a hybrid solar, flue gas, chimney power plant.

Human Activities Recognition in Android Smartphone Using Support Vector Machine

Abstract: In this study, we designed and constructed a system to identify human actions using integrated sensors in smartphones. There are six actions that are selected for recognition include: walking, standing, sitting, lying down, up the stairs, down the stairs. In this system, Support Vector Machine (SVM) is used to classify and identify action. Collected data from sensors are analyzed for the classification model - the model file. The classification models are optimized to bring the best results for the identified human activity. After forming the classify model, the model will be integrated into the system to identify the human activities. Human activities recognition system is written on Windows and Android platforms and operate in real time. The accuracy of the system depends on selected features and the quality of the training model. On the Android system running on smartphone with 248 features achieve 89.59% accurate rate.

Image processing based vehicle detection and tracking method

Abstract: Vehicle detection and tracking plays an effective and significant role in the area of traffic surveillance system where efficient traffic management and safety is the main concern. In this paper, we discuss and address the issue of detecting vehicle / traffic data from video frames. Although various researches have been done in this area and many methods have been implemented, still this area has room for improvements. With a view to do improvements, it is proposed to develop an unique algorithm for vehicle data recognition and tracking using Gaussian mixture model and blob detection methods. First, we differentiate the foreground from background in frames by learning the background. Here, foreground detector detects the object and a binary computation is done to define rectangular regions around every detected object. To detect the moving object correctly and to remove the noise some morphological operations have been applied. Then the final counting is done by tracking the detected objects and their regions. The results are encouraging and we got more than 91% of average accuracy in detection and tracking using the Gaussian Mixture Model and Blob Detection methods.

Nanosilica composite asphalt mixtures performance-based design and optimisation using response surface methodology

Abstract: In recent times, the use of polymer nanocomposite for construction of durable asphalt mixtures has been gradually replacing the application of polymer-modified binders. In this investigatio...

A Critical Review on Metal-Organic Frameworks and Their Composites as Advanced Materials for Adsorption and Photocatalytic Degradation of Emerging Organic Pollutants from Wastewater.

Abstract: Water-borne emerging pollutants are among the greatest concern of our modern society. Many of these pollutants are categorized as endocrine disruptors due to their environmental toxicities. They are harmful to humans, aquatic animals, and plants, to the larger extent, destroying the ecosystem. Thus, effective environmental remediations of these pollutants became necessary. Among the various remediation techniques, adsorption and photocatalytic degradation have been single out as the most promising. This review is devoted to the compilations and analysis of the role of metal-organic frameworks (MOFs) and their composites as potential materials for such applications. Emerging organic pollutants, like dyes, herbicides, pesticides, pharmaceutical products, phenols, polycyclic aromatic hydrocarbons, and perfluorinated alkyl substances, have been extensively studied. Important parameters that affect these processes, such as surface area, bandgap, percentage removal, equilibrium time, adsorption capacity, and recyclability, are documented. Finally, we paint the current scenario and challenges that need to be addressed for MOFs and their composites to be exploited for commercial applications.