IJTEE, volume-17 , Issue 1 (2018), PP 67 - 72
Published: 06 Dec 2018
by Jamil Al Asfar, Laith Mazahreh from Jamil Al Asfar, The University of Jordan, Amman, Jordan , Laith Mazahreh, Ministry of Energy and Mineral Resources, Amman, Jordan
Abstract: Desertification is considered one of the main problems that concerns mankind. Wood stoves and fireplaces mainly use wood as the main source of energy, which would lead to desertification and global warming. This paper presents the design of a domestic automatic stove that primary uses olive cake as the major source of energy. Moreover, a prototype of the theoretical model was built, and experimentally tested for 65 minutes. The efficiency of the prototyped model reached a value of 56.25% read more... read less...
Keywords: Olive Cake, Heating System, Biomass, Biofuels, Automatic.
IJTEE, volume-17 , Issue 1 (2018), PP 59 - 66
Published: 06 Dec 2018
by Ameen El-Sinawi, Mohammed Awadallah, Isam Janajreh from Mechanical Engineering Department, Khalifa University of Science and Technology, Masdar Institute, PO Box 54224, Masdar City, Abu Dhabi, United Arab Emirates
Abstract: Wind turbine blades operate in a harsh environment causing them to always be susceptible to damage. Variable wind loading, debris impact, and thermal gradient, among other factors, can cause damage to the blades. Detection of blade damage at early stages can prevent massive cost associated with turbine down-time and blade replacement. In this work, a vibration-based method is presented to detect damage at early stages. The presented method takes advantage of the effect of crack on modal parameters of the blade’s vibration. Finite element model (FEA) is constructed for both healthy and damage blade to study that effect. Power spectral density (PSD) plots of the blade’s vibration before and after damage are compared and the changes in the resonant modal amplitude’s frequencies are identified. To minimize the number accelerometers needed to monitor the health of the blade and without compromising the accuracy of damage predictions, ordinary kriging method is used to predict cracks in the blade’s structure. Kriging uses modal parameter data, experimental or otherwise, to estimate damage location on the blade. It creates a map of damage predictions throughout the region use measurements from far less sensors than common techniques. Damage characteristics estimates using the proposed method showed damage attributes predictions with accuracy greater than 93 %. Simulation is used to validate the proposed method and the results are discussed. read more... read less...
Keywords: Vibration Analysis, Wind Turbine Damage, Kriging Analysis, Model Parameters, FEA, BEMM method
IJTEE, volume-17 , Issue 1 (2018), PP 51 - 58
Published: 06 Dec 2018
by Mohamed H. Ahmed , Alberto Giaconia , Amr. M. A. Amin from Solar Energy Department, National Research Centre, Dokki, Giza, Egypt Energy Technologies Department, ENEA Casaccia Research Center, Roma, Italy Academy of Scientific Research and Technology, Cairo, Egypt
Abstract: In this work, an analysis of the annual performance of a parabolic trough concentrator has been accomplished. A numerical model was developed and built to study the annual performance of the parabolic trough collector's field at different locations in Egypt. The energy equations were solved using the Engineering Equation Solver EES software. The optical and thermal parameters of the concentrator were considered in the model. The numerical model results showed that temperature rise ranges from 90.5 to 221 °C and the outlet temperate ranges from 442 to 565 ºC at solar noon according to the season and the location. The operating period of the parabolic concentrator reaches its maximum value at summer where it ranges from 76.5 to 82 h/week. The present model was validated with the TRNSYS model. As a result, the presented model can be considered as a meaningful tool for developing the parabolic trough plant in Egypt. read more... read less...
Keywords: Solar Plant, Numerical Model, Parabolic Trough Collector, Collector Efficiency, Annual Performance, Model Validation
IJTEE, volume-17 , Issue 1 (2018), PP 41 - 50
Published: 06 Dec 2018
by Mohamed H. Ahmed from Solar Energy Department, National Research Center, Giza 12622, Egypt
Abstract: Solar energy has a great ability in cooling and air conditioning as the demand for cooling and air conditioning coincides with the availability of solar energy. In this study, a simulation program using TRNSYS platform was built to simulate and optimize the design and operating parameters. The hourly thermal performance of a single stage LiBr/H 2O solar absorption cooling system powered by linear Fresnel Concentrator was investigated under Cairo climate. The components size of the solar absorption cooling plant was optimized. The performance of the cooling system was studied in terms of the rate of useful energy from the concentrator, of the collector outlet temperature, and the coefficient of the performance COP of the absorption chiller. From the study, it was found that the optimum storage tank capacity depends on the area of the solar concentrator. Increasing the storage tank capacity from 3 to 9 m 3 leads to a decrease in the maximum outlet temperature from the collector from 182 to 120 ºC and consequently decreasing the Absorption chiller COP from 0.46 to0.07 respectively. Supplying a gas backup heating unit ensures stability for powering the adsorption cooling system. Increasing the backup unit capacity increase the operating hours of the absorption chiller. read more... read less...
Keywords: Solar Cooling, Absorption, Linear Fresnel Concentrator, Simulation, Storage Tank, TRNSYS
JTTM, volume-03 , Issue 1 (2021), PP 33 - 41
Published: 24 Mar 2021
by Vittorio Astarita, Vincenzo Pasquale Giofrè, Giuseppe Guido, Alessandro Vitale from Department of Civil Engineering, University of Calabria, Arcavacata di Rende (CS) 87036, Italy
Abstract: This paper intends to explore the convergence of some technological innovations that could lead to new cooperative Intelligent Transportation Systems (ITS). The technologies that might soon converge and lead to some new developments are: the Blockchain Technology (BT) concept, Internet of Things (IoT) and Connected and Automated Vehicles (CAV). Advantages and disadvantages of the new concepts founding a new ITS system are discussed in this conceptual paper. Blockchain technology has been recently introduced and many research ideas have been presented for application in the transportation sector. In this paper, we discuss a system that is based on a dedicated blockchain, able to involve both drivers and city administrations in the adoption of promising and innovative technologies that will create cooperation among connected vehicles. The proposed blockchain-based system can allow city administrators to reward drivers when they are willing to share travel data. The system manages in a special way the creation of rewards which are assigned to drivers and institutions participating actively in the system. Moreover, the system allows keeping a complete track of all transactions and interactions between drivers and city management on a completely open and shared platform. The main idea is to combine connected vehicles with BT to promote Cooperative ITS use, a better use of infrastructures and a more sustainable eco-system of cryptocurrencies. A short description of BT is introduced to evidence energy problems of sustainability in the implementation of Proof of Work (PoW) that is adopted by many blockchains. read more... read less...
Keywords: Intelligent Transportation Systems (ITS), Floating Car Data (FCD), Blockchain Technology (BT), traffic management, connected and autonomous vehicles.
JTTM, volume-03 , Issue 1 (2021), PP 25 - 31
Published: 24 Mar 2021
by Peter Krammer, Marcel Kvassay, Ladislav Hluchý from Institute of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia, 845 07
Abstract: In this article, building on our previous work, we engage in spatiotemporal modelling of transport demand in the Montreal metropolitan area over the period of six years. We employ classical machine learning and regression models, which predict bike-sharing demand in the form of daily cumulative sums of bike trips for each considered docking station. Hourly estimates of demand are then determined by considering the statistical distribution of demand across individual hours of an average day. In order to capture seasonal and other regular variation of demand, longer-term distribution characteristics of bike trips, such as their average number falling on each day of the week, month of the year, etc., were also used as input attributes. We initially conjectured that weather would be an important source of irregular variation in bike-sharing demand, and subsequently included several available meteorological variables in our models. We validated our models by Hold-Out and 10-Fold Cross-Validation, with encouraging results. read more... read less...
Keywords: Machine learning, Data mining, Regression, Data distribution, Spatiotemporal data, Modelling, Regression tree
JTTM, volume-03 , Issue 1 (2021), PP 17 - 24
Published: 24 Mar 2021
by Nadia Slimani, Ilham Slimani, Nawal Sbiti, Mustapha Amghar from Computer Systems and Productivity Team, EMI, Mohammed V University, Rabat, Morocco. Laboratory SmartICT, ENSAO, Mohammed I University, Oujda, Morocco.
Abstract: Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%. read more... read less...
Keywords: Road traffic forecasting, artificial neural networks, MLP, statistical forecasting, SMOreg, SARIMA.
JTTM, volume-03 , Issue 1 (2021), PP 11 - 16
Published: 24 Mar 2021
by Uneb Gazder, Ashar Ahmed, Umaira Shahid from Department of Civil Engineering, University of Bahrain, Isa Town, Bahrain, 32038 , Department of Urban and Infrastructure Engineering, NED University of Engineering and Technology, Karachi, Pakistan, 75270
Abstract: This study was aimed at determining the relationships of accident severity using road environment and traveller characteristics. Ordinal logistic regression models were used in this study. The accident data was provided by Malaysian Research Institute of Road Safety (MIROS) for all accidents which occurred in Penang state during 2006-2011. It was observed that motorbikes were predominantly involved in these accidents, hence, it was decided to develop three separate models; one for the overall data, and others for accidents with and without motorbikes. Logistic regression models showed that commercial land use, road width and experience of driver are important factors that may increase severity of accidents. Shoulder width was found to decrease the severity of motorbike accidents. Commercial land use, road width and driver experience have more impact on motorbike accidents as compared to accidents of other vehicles. read more... read less...
Keywords: Traffic Accidents, Severity, Ordinal Logistic Regression, Motorbikes, Malaysia.
JUSPN, volume-15 , Issue 2 (2021), PP 33 - 41
Published: 24 Mar 2021
by Wendy Osborn from Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge, Alberta, Canada, T1K 3M4
Abstract: In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified. read more... read less...
Keywords: spatial data streams, stream query processing, spatial join, performance
JUSPN, volume-15 , Issue 2 (2021), PP 25 - 31
Published: 21 Mar 2021
by Karim Haricha, Azeddine Khiat, Yassine Issaoui, Ayoub Bahnasse, Hassan Ouajji from Lab SSDIA, University Hassan II of Casablanca, Morocco, ENSAM Casablanca, University Hassan II of Casablanca, Morocco
Abstract: Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges. read more... read less...
Keywords: Smart Manufacturing (SM), Industry 4.0, Internet Of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL).
JUSPN, volume-15 , Issue 2 (2021), PP 19 - 24
Published: 22 Mar 2021
by Vishv Patel, Devansh Shah, Nishant Doshi from Pandit Deendayal Energy University, Gandhinagar, India, Gujarat-382426
Abstract: The large deployment of the Internet of Things (IoT) is empowering Smart City tasks and activities everywhere throughout the world. Items utilized in day-by-day life are outfitted with IoT devices and sensors to make them interconnected and connected with the internet. Internet of Things (IoT) is a vital piece of a smart city that tremendously impact on all the city sectors, for example, governance, healthcare, mobility, pollution, and transportation. This all connected IoT devices will make the cities smart. As different smart city activities and undertakings have been propelled in recent times, we have seen the benefits as well as the risks. This paper depicts the primary challenges and weaknesses of applying IoT innovations dependent on smart city standards. Moreover, this paper points the outline of the technologies and applications of the smart cities. read more... read less...
Keywords: Internet of Things (IoT), Smart Cities, IoT Devices and Sensors, Technologies of Smart Cities, Applications of Smart Cities
JUSPN, volume-15 , Issue 2 (2021), PP 11 - 17
Published: 24 Mar 2021
by Meryem Elmoulat, Olivier Debauche, Saïd Mahmoudi, Sidi Ahmed Mahmoudi, Adriano Guttadauria, Pierre Manneback, Frédéric Lebeau from University Mohammed V, Faculty of Sciences, Research Unit GeoRisk: Geological Risks, Battouta Avenue, Rabat, Morocco, 10140, University of Mons, Faculty of Engineering - ILIA/Infortech, Place du Parc 20, Mons, Belgium, 7000, University of Liège - GxABT, TERRA, Passage des déportés 2, Gembloux, Belgium, 5030, University of Liège - GxABT, BioDynE, Gembloux, Belgium, 5030
Abstract: Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models. read more... read less...
Keywords: Landslides Susceptibility, IoT, Artificial Intelligence, Early Warning System, Landslides Monitoring, Edge AI, Edge IoT
SWES, volume-09 , Issue 2 (2017), PP 75 - 81
Published: 13 Nov 2017
by S. Praveen, J. Jegan from M.E (Construction Engineering & Management), Department of Civil Engineering, James College of Engineering & Technology, Nagercoil, Tamil Nadu, India, Professor & Head, Department of Civil Engineering, University College of Engineering Ramanathapuram, Tamil Nadu, India
Abstract: The authors explore transport and trade as two broad service sectors of inland water resources. An attempt is made to find out the key issues and challenges from this sector with the evolving understanding of Indian inland water transportation system. The paper explains the background of inland water transport sector in India along with the discussion of issues and challenges faced by the same. The authors state that co-operation and co-ordination between inter-state governments is a strategic element to expand the network of inland water transport system in India beyond state boundaries. Conclusively, the prospect of inland navigation looks promising, wherein issues on infrastructural gaps and institutional support are addressed suitably. read more... read less...
Keywords: Inland Water, Navigation, Transportation, National Waterways
SWES, volume-09 , Issue 2 (2017), PP 67 - 74
Published: 18 Oct 2017
by Otu, Ubong Etop from Department Of Marine Biology, Akwa Ibom State University P.M.B 1167, Uyo, Nigeria
Abstract: Wastewater treatment is an efficient technique that increases the reclamation and reuse of wastewater for other productive uses, thereby, reducing the demand for freshwater resources, conservation of aquatic habitat, and sustainable utilization of water resources. Concerns for wastewater in Sweden began in the 1930s with only mechanical treatment but efficiently implemented in the 1960s as a result of significant eutrophication observed in open waters such as the Baltic Sea. Although prevailing wastewater treatment is fairly efficient, there is need to upgrade and improve existing treatment facilities (constructed in the 1970s) to mitigate potentially degradation of hygienic conditions due to the estimated increase in Swedish population. Thus, this paper will critically analyse prevailing treatment of wastewater in Sweden, the technology used and possible challenges encountered in the process. Small scale treatment of wastewater particularly practiced for dwellings not connected to municipal treatment plant will be discussed including sludge management in Sweden. The report further presented the significant issues including regulations, challenges, health hazards and constraints associated with wastewater treatment and reclamation. In addition, background information relating to potential technology to meet future wastewater treatment in Sweden were highlighted because current wastewater treatment facilities were constructed in the 1970s to provide services to Swedish population at that time. read more... read less...
Keywords: Water, Wastewater treatment, Grey wastewater, Sludge Management
SWES, volume-09 , Issue 2 (2017), PP 59 - 65
Published: 04 Sep 2017
by Gathagu John Ng’ang’a, Mainya Johnstone Isiah, Oduor Brian Omondi, Khaldoon A. Mourad from Pan African University Institute for water and Energy Sciences, Tlemcen, Algeria, Center for Middle Eastern Studies, Lund University, Lund, 22100
Abstract: Soil and water conservation measures are widely practiced in Kenya to tackle the degradation of the ecosystems and to improve land productivity. Local government and NGOs have developed programs and campaigns about soil and water conservation measures. The aim of this study is to assess the need for soil and water conservation measures in Thika- Chania catchment by conducting a household survey using QuickTap Survey software. A total of 200 respondents were successfully interviewed and results analyzed in SPSS program. GIS tools were also used to do a classification of slopes in the study area. Results indicate that more than 90% of the people in the catchment area are farmers. In the recent years, 50% of the respondents have noted a decline in the vegetation. During the rain seasons, the intensity of color in the local rivers due to sediments have been observed to increase by 75% of the respondent while 9% said there was no change. More than 70% of the respondents indicated that the water levels have been on the declining trend especially during the low flows. Terraces and grass strips were the common soil and water conservation measures although some of them were severely degraded. We concluded that there is an immediate need to implement soil and water conservation measures in the catchment to enhance and restore the optimum functioning of the ecosystems. Capacity building and frequent extension services are needed to increase awareness on the impacts of the respective conservation methods. Incentives programs need to be established to encourage more farmers to participate in conserving and protecting their lands from degradation. read more... read less...
Keywords: Baseline survey, soil and water conservation, capacity building, degradation.
SWES, volume-09 , Issue 1 (2017), PP 49 - 57
Published: 03 Apr 2017
by J. Nyika, G.N. Karuku, and R. N. Onwonga from Department of Land Resource Management and Agricultural Technology, Faculty of Agriculture, University of Nairobi, P.O. Box 29053-00625, Nairobi, Kenya
Abstract: Water systems have complex component interactions necessitating development and evaluation of management amidst uncertainties of climate and constrained natural resources. Conceptual models such as WEAP when used are effective planning and management tools as they forecast future effects of resource use efficiency at sub-catchment level using existent hydrological and climate data thereby acting as corrective measure to poor resources management. This study aimed at using WEAP model to forecast demand and analyze scenarios on efficient water use in Mbagathi subcatchment. WEAP model schematic was set to develop current and reference scenarios. Parameters used to run WEAP model were a GIS map of the sub-catchment, climate data from Kenya Meteorological Department at Dagorretti Corner Station, hydrological and water demand data from WRMA databases. High population growth and prolonged drought were predicted to increase water demand while reuse though not practised, was found by the model to be the most effective approach to manage unmet demands as compared to reduced conveyance losses and increased reservoir capacity. The study concluded that water reuse through exploitation of wastewater could be a viable solution to Mbagathi sub-catchment's water problems. read more... read less...
Keywords: water problems, necessitating development, evaluation of management, climate and constrained natural resources