Treatment of biodiesel wastewater using ferric chloride and ferric sulfate

The production of biodiesel through the transesterification method produces a large amount of wastewater that contains high level of chemical oxygen demand (COD) and oil and grease. In this study, coagulation was adopted to treat the biodiesel wastewater. Two types of coagulants were examined using standard jar test apparatus, i.e. ferric chloride and ferric sulfate. The effects of pH and coagulant dosage were examined at 150 rpm of rapid mixing and 20 rpm slow mixing and 30 min settling time, higher removal of SS (over 80%), colour (over 80%), COD (over 50%) and Oil and Grease (over 90%) were achieved at pH 6. Ferric Chloride was found to be superior was observed at reasonable lower amount of coagulant i.e. 300 mg/L. The result indicated that coagulation and flocculation process had contributed bigger roles in integrated treatment system

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

AWARE: Platform for Autonomous self-deploying and operation of Wireless sensor-actuator networks cooperating with unmanned AeRial vehiclEs

This paper presents the AWARE platform that seeks to enable the cooperation of autonomous aerial vehicles with ground wireless sensor-actuator networks comprising both static and mobile nodes carried by vehicles or people. Particularly, the paper presents the middleware, the wireless sensor network, the node deployment by means of an autonomous helicopter, and the surveillance and tracking functionalities of the platform. Furthermore, the paper presents the first general experiments of the AWARE project that took place in March 2007 with the assistance of the Seville fire brigades

Computer vision techniques for forest fire perception

This paper presents computer vision techniques for forest fire perception involving measurement of forest fire properties (fire front, flame height, flame inclination angle, fire base width) required for the implementation of advanced forest fire-fighting strategies. The system computes a 3D perception model of the fire and could also be used for visualizing the fire evolution in remote computer systems. The presented system integrates the processing of images from visual and infrared cameras. It applies sensor fusion techniques involving also telemetry sensors, and GPS. The paper also includes some results of forest fire experiments.European Commission EVG1-CT-2001-00043European Commission IST-2001-34304Ministerio de Educación y Ciencia DPI2005-0229

Silhouette coverage analysis for multi-modal video surveillance

In order to improve the accuracy in video-based object detection, the proposed multi-modal video surveillance system takes advantage of the different kinds of information represented by visual, thermal and/or depth imaging sensors. The multi-modal object detector of the system can be split up in two consecutive parts: the registration and the coverage analysis. The multi-modal image registration is performed using a three step silhouette-mapping algorithm which detects the rotation, scale and translation between moving objects in the visual, (thermal) infrared and/or depth images. First, moving object silhouettes are extracted to separate the calibration objects, i.e., the foreground, from the static background. Key components are dynamic background subtraction, foreground enhancement and automatic thresholding. Then, 1D contour vectors are generated from the resulting multi-modal silhouettes using silhouette boundary extraction, cartesian to polar transform and radial vector analysis. Next, to retrieve the rotation angle and the scale factor between the multi-sensor image, these contours are mapped on each other using circular cross correlation and contour scaling. Finally, the translation between the images is calculated using maximization of binary correlation. The silhouette coverage analysis also starts with moving object silhouette extraction. Then, it uses the registration information, i.e., rotation angle, scale factor and translation vector, to map the thermal, depth and visual silhouette images on each other. Finally, the coverage of the resulting multi-modal silhouette map is computed and is analyzed over time to reduce false alarms and to improve object detection. Prior experiments on real-world multi-sensor video sequences indicate that automated multi-modal video surveillance is promising. This paper shows that merging information from multi-modal video further increases the detection results

Multi-sensor fire detection by fusing visual and non-visual flame features

This paper proposes a feature-based multi-sensor fire detector operating on ordinary video and long wave infrared (LWIR) thermal images. The detector automatically extracts hot objects from the thermal images by dynamic background subtraction and histogram-based segmentation. Analogously, moving objects are extracted from the ordinary video by intensity-based dynamic background subtraction. These hot and moving objects are then further analyzed using a set of flame features which focus on the distinctive geometric, temporal and spatial disorder characteristics of flame regions. By combining the probabilities of these fast retrievable visual and thermal features, we are able to detect the fire at an early stage. Experiments with video and LWIR sequences of lire and non-fire real case scenarios show good results in id indicate that multi-sensor fire analysis is very promising

Laminated Veneer Lumber (LVL) sengon: an innovative sustainable building material in Indonesia

Laminated Veneer Lumber (LVL) is recently available in Indonesian markets. The veneers are majority from Sengon wood (Paraserianthes falcataria), which is a fast-growing timber species native to Indonesia. Their use in practice is limited to non-structural components since Sengon wood species has low engineering properties and less resistance to termite attacks. The LVL production introduced few years ago has significantly improved both mechanical properties and durability as well as has expanded its utilization into various structural components. This remarkable improvement has made LVL Sengon wood as an innovative sustainable building materials in Indonesia. This paper summarized a series of authors’ work conducted since couple years ago to initiate the utilization of LVL Sengon in structural components such as shear walls and floor systems as parts of a project to develop its design standard. In addition, creep behavior of this LVL is also highlihgted here as this phenomenon is essential for designers and engineers to anticipate their designs within their service life. In particular, the test results showed that addition of diagonal members increased both racking resistance and equivalent viscous damping ratio of the developed LVL shear walls. And the LVL floor model which is composed of built-up box joists and plywood sheathing remained liniear-elastic under bearing load up to 18.75 kN/2