Search results for: optimization of routes

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Harshkumar K. Desai, Anuj K. Desai, Jay P. Patel, Snehal V. Trivedi, Yogendrasinh Parmar

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The need of continuous improvement in a product or process in this era of global competition leads to apply value engineering for functional and aesthetic improvement in consideration with economic aspect too. Solar industries located at G.I.D.C., Makarpura, Vadodara, Gujarat, India; a manufacturer of variety of CNC Machines had a challenge to analyze the structural design of column, base, carriage and table of CNC Milling Machine in the account of reduction of overall weight of a machine without affecting the rigidity and accuracy at the time of operation. The identified task is the first attempt to validate and optimize the proposed design of ribbed structure statically using advanced modeling and analysis tools in a systematic way. Results of stress and deformation obtained using analysis software are validated with theoretical analysis and found quite satisfactory. Such optimized results offer a weight reduction of the final assembly which is desired by manufacturers in favor of reduction of material cost, processing cost and handling cost finally.

Keywords: CNC milling machine, optimization, finite element analysis (FEA), weight reduction

Procedia PDF Downloads 259

Authors: Noura Kichou, Manel Tafergguenit, Nabila Ghechtouli, Zakia Hank

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The aim of this work is to synthesize, characterize and evaluate the biological activity of two Ligands : glyoxime and dimethylglyoxime, and their metal Ni(II) chelates. The newly chelates were characterized by elemental analysis, IR, EPR, nuclear magnetic resonances (1H and 13C), and biological activity. The antibacterial and antifungal activities of the ligands and its metal complexes were screened against bacterial species (Staphylococcus aureus, Bacillus subtilis, and Escherichia coli) and fungi (Candida albicans). Ampicillin and amphotericin were used as references for antibacterial and antifungal studies. The activity data show that the metal complexes have a promising biological activity comparable with parent free ligand against bacterial and fungal species. A structural, energetic, and electronic theoretical study was carried out using the DFT method, with the functional B3LYP and the gaussian program 09. A complete optimization of geometries was made, followed by a calculation of the frequencies of the normal modes of vibration. The UV spectrum was also interpreted. The theoretical results were compared with the experimental data.

Keywords: glyoxime, dimetylglyoxime, nickel, antibacterial activity

Procedia PDF Downloads 88

Authors: Noura Kichou, Manel Tafergguenit, Nabila Ghechtouli, Zakia Hank

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The aim of this work is to synthesize, characterize and evaluate the biological activity of two Ligands: glyoxime and dimethylglyoxime, and their metal Ni(II) chelates. The newly chelates were characterized by elemental analysis, IR, EPR, nuclear magnetic resonances (1H and 13C), and biological activity. The antibacterial and antifungal activities of the ligands and its metal complexes were screened against bacterial species (Staphylococcus aureus, Bacillus subtilis, and Escherichia coli) and fungi (Candida albicans). Ampicillin and amphotericin were used as references for antibacterial and antifungal studies. The activity data show that the metal complexes have a promising biological activity comparable with parent free ligand against bacterial and fungal species. A structural, energetic, and electronic theoretical study was carried out using the DFT method, with the functional B3LYP and the gaussian program 09. A complete optimization of geometries was made, followed by a calculation of the frequencies of the normal modes of vibration. The UV spectrum was also interpreted. The theoretical results were compared with the experimental data.

Keywords: glyoxime, dimetylglyoxime, nickel, antibacterial activity

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Authors: Harshith Gowda K. S, Tejaskumar N, Shubhanga R. B, Gowtham N, Deekshith Gowda H. S

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Phasor measurement units (PMU) are playing an important role in the current power system for state estimation. It is necessary to have complete observability of the power system while minimizing the cost. For this purpose, the optimal location of the phasor measurement units in the power system is essential. In a bus system, zero injection buses need to be evaluated to minimize the number of PMUs. In this paper, the optimization problem is formulated using mixed integer programming to obtain the optimal location of the PMUs with increased observability. The formulation consists of with and without zero injection bus as constraints. The formulated problem is simulated using a CPLEX solver in the GAMS software package. The proposed method is tested on IEEE 30, IEEE 39, IEEE 57, and IEEE 118 bus systems. The results obtained show that the number of PMUs required is minimal with increased observability.

Keywords: PMU, observability, mixed integer programming (MIP), zero injection buses (ZIB)

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Authors: J. J. Rickard, A. Belli, P. Goldberg Oppenheimer

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Medical diagnostics, environmental monitoring, homeland security and forensics increasingly demand specific and field-deployable analytical technologies for quick point-of-care diagnostics. Although technological advancements have made optical methods well-suited for miniaturization, a highly-sensitive detection technique for minute sample volumes is required. Raman spectroscopy is a well-known analytical tool, but has very weak signals and hence is unsuitable for trace level analysis. Enhancement via localized optical fields (surface plasmons resonances) on nanoscale metallic materials generates huge signals in surface-enhanced Raman scattering (SERS), enabling single molecule detection. This enhancement can be tuned by manipulation of the surface roughness and architecture at the sub-micron level. Nevertheless, the development and application of SERS has been inhibited by the irreproducibility and complexity of fabrication routes. The ability to generate straightforward, cost-effective, multiplex-able and addressable SERS substrates with high enhancements is of profound interest for SERS-based sensing devices. While most SERS substrates are manufactured by conventional lithographic methods, the development of a cost-effective approach to create nanostructured surfaces is a much sought-after goal in the SERS community. Here, a method is established to create controlled, self-organized, hierarchical nanostructures using electrohydrodynamic (HEHD) instabilities. The created structures are readily fine-tuned, which is an important requirement for optimizing SERS to obtain the highest enhancements. HEHD pattern formation enables the fabrication of multiscale 3D structured arrays as SERS-active platforms. Importantly, each of the HEHD-patterned individual structural units yield a considerable SERS enhancement. This enables each single unit to function as an isolated sensor. Each of the formed structures can be effectively tuned and tailored to provide high SERS enhancement, while arising from different HEHD morphologies. The HEHD fabrication of sub-micrometer architectures is straightforward and robust, providing an elegant route for high-throughput biological and chemical sensing. The superior detection properties and the ability to fabricate SERS substrates on the miniaturized scale, will facilitate the development of advanced and novel opto-fluidic devices, such as portable detection systems, and will offer numerous applications in biomedical diagnostics, forensics, ecological warfare and homeland security.

Keywords: hierarchical electrohydrodynamic patterning, medical diagnostics, point-of care devices, SERS

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Adeolu O. Dairo

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Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making.

Keywords: geospatial, geo-analytics, self-organizing map, customer-centric

Procedia PDF Downloads 165

Authors: A. Hamidi, S. Khelladi, L. Illoul, A. Tcharkhtchi

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Reactive rotational molding (RRM) is a process to manufacture hollow plastic parts with reactive material has several advantages compared to conventional roto molding of thermoplastic powders: process cycle time is shorter; raw material is less expensive because polymerization occurs during processing and high-performance polymers may be used such as thermosets, thermoplastics or blends. However, several phenomena occur during this process which makes the optimization of the process quite complex. In this study, we have used a mixture of isocyanate and polyol as a reactive system. The chemical transformation of this system to polyurethane has been studied by thermal analysis and rheology tests. Thanks to these results of the curing process and rheological measurements, the kinetic and rheokinetik of polyurethane was identified. Smoothed Particle Hydrodynamics, a Lagrangian meshless method, was chosen to simulate reactive fluid flow in 2 and 3D configurations of the polyurethane during the process taking into account the chemical, and chemiorehological results obtained experimentally in this study.

Keywords: reactive rotational molding, simulation, smoothed particle hydrodynamics, surface tension, rheology, free surface flows, viscoelastic, interpolation

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Authors: Boris Melnikov, Ye Zhang, Dmitrii Chaikovskii

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We continue to consider one of the cybernetic methods in computational biology related to the study of DNA chains. Namely, we are considering the problem of reconstructing the not fully filled distance matrix of DNA chains. When applied in a programming context, it is revealed that with a modern computer of average capabilities, creating even a small-sized distance matrix for mitochondrial DNA sequences is quite time-consuming with standard algorithms. As the size of the matrix grows larger, the computational effort required increases significantly, potentially spanning several weeks to months of non-stop computer processing. Hence, calculating the distance matrix on conventional computers is hardly feasible, and supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains; then, we published algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. In this paper, we propose an algorithm for restoring the distance matrix for DNA chains, and the primary focus is on enhancing the algorithms that shape the greedy function within the branches and boundaries method framework.

Keywords: DNA chains, distance matrix, optimization problem, restoring algorithm, greedy algorithm, heuristics

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Authors: Ahmed Aisa, Tariq Iqbal

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This letter talks about the ready-to-use design of a solar water heating system because, in Canada, the average consumption of hot water per person is approximately 50 to 75 L per day and the average Canadian household uses 225 L. Therefore, this paper will demonstrate the method of designing a solar water heating system with thermal storage. It highlights the renewable hybrid power system, allowing you to obtain a reliable, independent system with the optimization of the ingredient size and at an improved capital cost. The system can provide hot water for a big building. The main power for the system comes from solar panels. Solar Advisory Model (SAM) and HOMER are used. HOMER and SAM are design models that calculate the consumption of hot water and cost for a house. Some results, obtained through simulation, were for monthly energy production, annual energy production, after tax cash flow, the lifetime of the system and monthly energy usage represented by three types of energy. These are system energy, electricity load electricity and net metering credit.

Keywords: water heating, thermal storage, capital cost solar, consumption

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Authors: Juan Portoviejo Brito, Daniel Icaza Alvarez, Christian Castro Samaniego

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In this article, we present the modeling, simulations, and energy conversion analysis of the solar-wind system for the Quingeo Heritage Center in Ecuador. A numerical model was constructed based on the 19 equations, it was coded in MATLAB R2017a, and the results were compared with the experimental data of the site. The model is built with the purpose of using it as a computer development for the optimization of resources and designs of hybrid systems in the Parish of Quingeo and its surroundings. The model obtained a fairly similar pattern compared to the data and curves obtained in the field experimentally and detailed in manuscript. It is important to indicate that this analysis has been carried out so that in the near future one or two of these power generation systems can be exploited in a massive way according to the budget assigned by the Parish GAD of Quingeo or other national or international organizations with the purpose of preserving this unique colonial helmet in Ecuador.

Keywords: hybrid system, wind turbine, modeling, simulation, Smart Grid, Quingeo Azuay Ecuador

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Authors: Peter Baji

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In recent years, a growing body of literature emphasizes the increasingly negative impacts of urban road congestion in the everyday life of citizens. Although there are different responses from the public sector to decrease traffic congestion in urban regions, the most effective public intervention is using congestion charges. Because travel is an economic asset, its consumption can be controlled by extra taxes or prices effectively, but this demand-side intervention is often unpopular. Measuring traffic flows with the help of different methods has a long history in transport sciences, but until recently, there was not enough sufficient data for evaluating road traffic flow patterns on the scale of an entire road system of a larger urban area. European cities (e.g., London, Stockholm, Milan), in which congestion charges have already been introduced, designated a particular zone in their downtown for paying, but it protects only the users and inhabitants of the CBD (Central Business District) area. Through the use of Google Maps data as a resource for revealing urban road traffic flow patterns, this paper aims to provide a solution for a fairer and smarter congestion pricing method in cities. The case study area of the research contains three bordering districts of Budapest which are linked by one main road. The first district (5th) is the original downtown that is affected by the congestion charge plans of the city. The second district (13th) lies in the transition zone, and it has recently been transformed into a new CBD containing the biggest office zone in Budapest. The third district (4th) is a mainly residential type of area on the outskirts of the city. The raw data of the research was collected with the help of Google’s Distance Matrix API (Application Programming Interface) which provides future estimated traffic data via travel times between freely fixed coordinate pairs. From the difference of free flow and congested travel time data, the daily congestion patterns and hot spots are detectable in all measured roads within the area. The results suggest that the distribution of congestion peak times and hot spots are uneven in the examined area; however, there are frequently congested areas which lie outside the downtown and their inhabitants also need some protection. The conclusion of this case study is that cities can develop a real-time and place-based congestion charge system that forces car users to avoid frequently congested roads by changing their routes or travel modes. This would be a fairer solution for decreasing the negative environmental effects of the urban road transportation instead of protecting a very limited downtown area.

Keywords: Budapest, congestion charge, distance matrix API, application programming interface, pilot study

Procedia PDF Downloads 181

Authors: Rupu Yang, Cong Toan Tran, Assaad Zoughaib

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The work provides an iterative nonlinear programming method to synthesize a heat exchanger network by manipulating the trade-offs between the heat load of process heat exchangers (HEs) and utilities. We consider for the synthesis problem two cases, the first one without fixed cost for HEs, and the second one with fixed cost. For the no fixed cost problem, the nonlinear programming (NLP) model with all the potential HEs is optimized to obtain the global optimum. For the case with fixed cost, the NLP model is iterated through adding/removing HEs. The method was applied in five case studies and illustrated quite well effectiveness. Among which, the approach reaches the lowest TAC (2,904,026$/year) compared with the best record for the famous Aromatic plants problem. It also locates a slightly better design than records in literature for a 10 streams case without fixed cost with only 1/9 computational time. Moreover, compared to the traditional mixed-integer nonlinear programming approach, the iterative NLP method opens a possibility to consider constraints (such as controllability or dynamic performances) that require knowing the structure of the network to be calculated.

Keywords: heat exchanger network, synthesis, NLP, optimization

Procedia PDF Downloads 146

Authors: A. W. Zularisam, Sveta Thakur, Lakhveer Singh, Mimi Sakinah Abdul Munaim

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Clostridium sp. LS2 was immobilised by entrapment in polyethylene glycol (PEG) gel beads to improve the biohydrogen production rate from palm oil mill effluent (POME). We sought to explore and optimise the hydrogen production capability of the immobilised cells by studying the conditions for cell immobilisation, including PEG concentration, cell loading and curing times, as well as the effects of temperature and K2HPO4 (500–2000 mg/L), NiCl2 (0.1–5.0 mg/L), FeCl2 (100–400 mg/L) MgSO4 (50–200 mg/L) concentrations on hydrogen production rate. The results showed that by optimising the PEG concentration (10% w/v), initial biomass (2.2 g dry weight), curing time (80 min) and temperature (37 °C), as well as the concentrations of K2HPO4 (2000 mg/L), NiCl2 (1 mg/L), FeCl2 (300 mg/L) and MgSO4 (100 mg/L), a maximum hydrogen production rate of 7.3 L/L-POME/day and a yield of 0.31 L H2/g chemical oxygen demand were obtained during continuous operation. We believe that this process may be potentially expanded for sustained and large-scale hydrogen production.

Keywords: hydrogen, polyethylene glycol, immobilised cell, fermentation, palm oil mill effluent

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Authors: Zongjie Wang, Zhizhong Guo

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While the regular optimal power flow model focuses on a single time scan, the optimization of power systems is typically intended for a time duration with respect to a desired objective function. In this paper, a temporal optimal power flow model for a time period is proposed. To reduce the computation burden needed for calculating temporal optimal power flow, a characteristic optimal power flow model is proposed, which employs different characteristic load patterns to represent the objective function and security constraints. A numerical method based on the interior point method is also proposed for solving the characteristic optimal power flow model. Both the temporal optimal power flow model and characteristic optimal power flow model can improve the systems’ desired objective function for the entire time period. Numerical studies are conducted on the IEEE 14 and 118-bus test systems to demonstrate the effectiveness of the proposed characteristic optimal power flow model.

Keywords: optimal power flow, time period, security, economy

Procedia PDF Downloads 432

Authors: Soma Kanta Baral

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Background: Since 1981, when the first AIDS case was reported, worldwide, more than 34 million people have been infected with HIV. Almost 95 percent of the people infected with HIV live in developing countries. As HBV & HIV share similar routes of transmission by sexual intercourse or drug use by parenteral injection, co-infection is common. Because of the limited access to healthcare & HIV treatment in developing countries, HIV-infected individuals are present late for care. Enumeration of CD4+ T cell count at the time of diagnosis has been useful to initiate the therapy in HIV infected individuals. The baseline CD4+ T cell count shows high immunological variability among patients. Methods: This prospective study was done in the serology section of the Department of Microbiology over a period of one year from august 2012 to July 2013. A total of 13037 individuals subjected for HIV test were included in the study comprising of 4982 males & 8055 females. Blood sample was collected by vein puncture aseptically with standard operational procedure in clean & dry test-tube. All blood samples were screened for HIV as described by WHO algorithm by Immuno-chromatography rapid kits. Further confirmation was done by biokit ELISA method as per the manufacturer’s guidelines. After informed consent, HIV positive individuals were screened for HBsAg by Immuno-chromatography rapid kits (Hepacard). Further confirmation was done by biokit ELISA method as per the manufacturer’s guidelines. EDTA blood samples were collected from the HIV sero-positive individuals for baseline CD4+ T count. Then, CD4+ T cells count was determined by using FACS Calibur Flow Cytometer (BD). Results: Among 13037 individuals screened for HIV, 104 (0.8%) were found to be infected comprising of 69(66.34%) males & 35 (33.65%) females. The study showed that the high infection was noted in housewives (28.7%), active age group (30.76%), rural area (56.7%) & in heterosexual route (80.9%) of transmission. Out of total HIV infected individuals, distribution of HBV co-infection was found to be 6(5.7%). All co- infected individuals were married, male, above the age of 25 years & heterosexual route of transmission. Baseline CD4+ T cell count of HIV infected patient was found higher (mean CD4+ T cell count; 283cells/cu.mm) than HBV co-infected patients (mean CD4+ T cell count; 91 cells/cu.mm). Majority (77.2%) of HIV infected & all co-infected individuals were presented in our center late (CD4+ T cell count;< 350/cu. mm) for diagnosis and care. Majority of co- infected 4 (80%) were late presented with advanced AIDS stage (CD4+ count; <200/cu.mm). Conclusions: The study showed a high percentage of HIV sero-positive & co- infected individuals. Baseline CD4+ T cell count of majority of HIV infected individuals was found to be low. Hence, more sustained and vigorous awareness campaigns & counseling still need to be done in order to promote early diagnosis and management.

Keywords: HIV/AIDS, HBsAg, co-infection, CD4+

Procedia PDF Downloads 200

Authors: Jatinder Kumar, Ajay Bansal

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Simulation of a photocatalytic reactor helps in understanding the complex behavior of the photocatalytic degradation. Simulation also aids the designing and optimization of the photocatalytic reactor. Lack of simulation strategies is a huge hindrance in the commercialization of the photocatalytic technology. With the increased performance of computational resources, and development of simulation software, computational fluid dynamics (CFD) is becoming an affordable engineering tool to simulate and optimize reactor designs. In the present paper, a CFD (Computational fluid dynamics) model for simulating the performance of an immobilized-titanium dioxide based annular photocatalytic reactor was developed. The computational model integrates hydrodynamics, species mass transport, and chemical reaction kinetics using a commercial CFD code Fluent 6.3.26. The CFD model was based on the intrinsic kinetic parameters determined experimentally in a perfectly mixed batch reactor. Rhodamine B, a complex organic compound, was selected as a test pollutant for photocatalytic degradation. It was observed that CFD could become a valuable tool to understand and improve the photocatalytic systems.

Keywords: simulation, computational fluid dynamics (CFD), annular photocatalytic reactor, titanium dioxide

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Authors: Guilherme Gorgulho, Carlos Roberto Camello Lima

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Due to the competitive market in which companies are currently engaged, the constant changes require companies to react quickly regarding the variability of demand and process. The changes are caused by customers, or by demand fluctuations or variations of products, or the need to serve customers within agreed delivery taking into account the continuous search for quality and competitive prices in products. These changes end up influencing directly or indirectly the activities of the Planning and Production Control (PPC), which does business in strategic, tactical and operational levels of production systems. One area of concern for organizations is in the short term (operational level), because this planning stage any error or divergence will cause waste and impact on the delivery of products on time to customers. Thus, this study aims to optimize the efficiency of production scheduling, using different sequencing strategies in an automotive company. Seeking to aim the proposed objective, we used the computer simulation in conjunction with lean manufacturing to build and validate the current model, and subsequently the creation of future scenarios.

Keywords: computational simulation, lean manufacturing, production scheduling, sequencing strategies

Procedia PDF Downloads 254

Authors: Khaled A. Madi, Rema A. Mousbahi, Mostafa B. Abuitbel, Abdualhakim O. Nagi

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Antenna perhaps is the most important part of any communication system, it determines the overall efficiency and the direction of radiation of the system. Antennas vary in shape and size on a very wide range. For fast moving vehicles, the antenna should offer as litter aerodynamic resistance as possible. Slot antenna is best suited for this purpose. It offers very little aerodynamic resistance, compact, easy to feed and fabricate. This work presented in this paper deals with the investigation of a half wave slot antenna. The antenna has been studied, analyzed, designed, simulated, fabrication, and tested at the X-band. The field of antenna study is an extremely vast one, and to grasp the fundamentals, two pronged approaches have been used, and the focus was on the fabrication and testing of a slot waveguide directional antenna. Focuses on the design and simulation of slot antennas with an emphasis on optimization of a 9.1 GHz a rectangular waveguide have been used to feed slot antenna. A microwave fed slot antenna used in the communication lab was also simulated. The results have been presented and compared with the expected values, where a good agreement was achieved between the simulation and experimental results.

Keywords: microwave, slot antenna, simulation, fabrication

Procedia PDF Downloads 111

Authors: Shiksha Jain, Ramesh Mishra

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One of the most widely used complex signals processing operation is filtering. The most important FIR digital filter are widely used in DSP for filtering to alter the spectrum according to some given specifications. Power consumption and Area complexity in the algorithm of Finite Impulse Response (FIR) filter is mainly caused by multipliers. So we present a multiplier less technique (DA technique). In this technique, precomputed value of inner product is stored in LUT. Which are further added and shifted with number of iterations equal to the precision of input sample. But the exponential growth of LUT with the order of FIR filter, in this basic structure, makes it prohibitive for many applications. The significant area and power reduction over traditional Distributed Arithmetic (DA) structure is presented in this paper, by the use of slicing of LUT to the desired length. An architecture of 16 tap FIR filter is presented, with different length of slice of LUT. The result of FIR Filter implementation on Xilinx ISE synthesis tool (XST) vertex-4 FPGA Tool by using proposed method shows the increase of the maximum frequency, the decrease of the resources as usage saving in area with more number of slices and the reduction dynamic power.

Keywords: multiplier less technique, linear phase symmetric FIR filter, FPGA tool, look up table

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Authors: S. Toufigh Bararpour, Davood Karami, Nader Mahinpey

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Dependence of global economics on fossil fuels has led to a large growth in the emission of greenhouse gases (GHGs). Among the various GHGs, carbon dioxide is the main contributor to the greenhouse effect due to its huge emission amount. To mitigate the threatening effect of CO₂, carbon capture and sequestration (CCS) technologies have been studied widely in recent years. For the combustion processes, three main CO₂ capture techniques have been proposed such as post-combustion, pre-combustion and oxyfuel combustion. Post-combustion is the most commonly used CO₂ capture process as it can be readily retrofit into the existing power plants. Multiple advantages have been reported for the post-combustion by solid sorbents such as high CO₂ selectivity, high adsorption capacity, and low required regeneration energy. Chemical adsorption of CO₂ over alkali-metal-based solid sorbents such as K₂CO₃ is a promising method for the selective capture of diluted CO₂ from the huge amount of nitrogen existing in the flue gas. To improve the CO₂ capture performance, K₂CO₃ is supported by a stable and porous material. Al₂O₃ has been employed commonly as the support and enhanced the cyclic CO₂ capture efficiency of K₂CO₃. Different phases of alumina can be obtained by setting the calcination temperature of boehmite at 300, 600 (γ-alumina), 950 (δ-alumina) and 1200 °C (α-alumina). By increasing the calcination temperature, the regeneration capacity of alumina increases, while the surface area reduces. However, sorbents with lower surface areas have lower CO₂ capture capacity as well (except for the sorbents prepared by hydrophilic support materials). To resolve this issue, a highly efficient alumina-aerogel support was synthesized with a BET surface area of over 2000 m²/g and then calcined at a high temperature. The synthesized alumina-aerogel was impregnated on K₂CO₃ based on 50 wt% support/K₂CO₃, which resulted in the preparation of a sorbent with remarkable CO₂ capture performance. The effect of synthesis conditions such as types of alcohols, solvent-to-co-solvent ratios, and aging times was investigated on the performance of the support. The best support was synthesized using methanol as the solvent, after five days of aging time, and at a solvent-to-co-solvent (methanol-to-toluene) ratio (v/v) of 1/5. Response surface methodology was used to investigate the effect of operating parameters such as carbonation temperature and H₂O-to-CO₂ flowrate ratio on the CO₂ capture capacity. The maximum CO₂ capture capacity, at the optimum amounts of operating parameters, was 7.2 mmol CO₂ per gram K₂CO₃. Cyclic behavior of the sorbent was examined over 20 carbonation and regenerations cycles. The alumina-aerogel-supported K₂CO₃ showed a great performance compared to unsupported K₂CO₃ and γ-alumina-supported K₂CO₃. Fundamental performance analyses and long-term thermal and chemical stability test will be performed on the sorbent in the future. The applicability of the sorbent for a bench-scale process will be evaluated, and a corresponding process model will be established. The fundamental material knowledge and respective process development will be delivered to industrial partners for the design of a pilot-scale testing unit, thereby facilitating the industrial application of alumina-aerogel.

Keywords: alumina-aerogel, CO₂ capture, K₂CO₃, optimization

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Authors: Caroline Atef Shoukry Tadros

Abstract:

Massive Urbanization growth threatens the sustainability of cities and the quality of city life. This raised the need for an alternate model of sustainability, so we need to plan the future cities in a smarter way with smarter mobility. Smart Mobility planning applications are solutions that use digital technologies and infrastructure advances to improve the efficiency, sustainability, and inclusiveness of urban transportation systems. They can contribute to meeting the needs of Urbanization growth by addressing the challenges of traffic congestion, pollution, accessibility, and safety in cities. Some example of a Smart Mobility planning application are Mobility-as-a-service: This is a service that integrates different transport modes, such as public transport, shared mobility, and active mobility, into a single platform that allows users to plan, book, and pay for their trips. This can reduce the reliance on private cars, optimize the use of existing infrastructure, and provide more choices and convenience for travelers. MaaS Global is a company that offers mobility-as-a-service solutions in several cities around the world. Traffic flow optimization: This is a solution that uses data analytics, artificial intelligence, and sensors to monitor and manage traffic conditions in real-time. This can reduce congestion, emissions, and travel time, as well as improve road safety and user satisfaction. Waycare is a platform that leverages data from various sources, such as connected vehicles, mobile applications, and road cameras, to provide traffic management agencies with insights and recommendations to optimize traffic flow. Logistics optimization: This is a solution that uses smart algorithms, blockchain, and IoT to improve the efficiency and transparency of the delivery of goods and services in urban areas. This can reduce the costs, emissions, and delays associated with logistics, as well as enhance the customer experience and trust. ShipChain is a blockchain-based platform that connects shippers, carriers, and customers and provides end-to-end visibility and traceability of the shipments. Autonomous vehicles: This is a solution that uses advanced sensors, software, and communication systems to enable vehicles to operate without human intervention. This can improve the safety, accessibility, and productivity of transportation, as well as reduce the need for parking space and infrastructure maintenance. Waymo is a company that develops and operates autonomous vehicles for various purposes, such as ride-hailing, delivery, and trucking. These are some of the ways that Smart Mobility planning applications can contribute to meeting the needs of the Urbanization growth. However, there are also various opportunities and challenges related to the implementation and adoption of these solutions, such as the regulatory, ethical, social, and technical aspects. Therefore, it is important to consider the specific context and needs of each city and its stakeholders when designing and deploying Smart Mobility planning applications.

Keywords: smart mobility planning, smart mobility applications, smart mobility techniques, smart mobility tools, smart transportation, smart cities, urbanization growth, future smart cities, intelligent cities, ICT information and communications technologies, IoT internet of things, sensors, lidar, digital twin, ai artificial intelligence, AR augmented reality, VR virtual reality, robotics, cps cyber physical systems, citizens design science

Procedia PDF Downloads 63

Authors: Sanjoy Mukherjee

Abstract:

Discussions on concepts of Single Axis Tracker (SAT) are becoming more and more apt for developing countries like India not just as an advancement in racking technology but due to the utmost necessity of reaching at the lowest Levelized Cost of Energy (LCOE) targets. With this increasing competition and significant fall in feed-in tariffs of solar PV projects, developers are under constant pressure to secure investment for their projects and eventually earn profits from them. Moreover, being the second largest populated country, India suffers from scarcity of land because of higher average population density. So, to mitigate the risk of this dual edged sword with reducing trend of unit (kWh) cost at one side and utilization of land on the other, tracking evolved as the call of the hour. Therefore, the prime objectives of this paper are not only to showcase how STT proves to be an effective mechanism to get more gain in Global Incidence in collector plane (G_inc) with respect to traditional mounting systems but also to introduce Seasonally Tilted Tracker (STT) technology as a possible option for high latitude locations.

Keywords: tracking system, grid connected solar PV plant, CAPEX reduction, levelized cost of energy

Procedia PDF Downloads 238

Authors: S. Mat Radzi, N. J. Abd Rahman, H. Mohd Noor, N. Ariffin

Abstract:

Ferulic acid has widespread industrial potential by virtue of its antioxidant properties. However, it is partially soluble in aqueous media, limiting their usefulness in oil-based processes in food, cosmetic, pharmaceutical, and material industry. Therefore, modification of ferulic acid should be made by producing of more lipophilic derivatives. In this study, a preliminary investigation of lipase-catalyzed trans-esterification reaction of ethyl ferulate and olive oil was investigated. The reaction was catalyzed by immobilized lipase from Candida antarctica (Novozym 435), to produce ferulate ester, a sunscreen agent. A statistical approach of Response surface methodology (RSM) was used to evaluate the interactive effects of reaction temperature (40-80°C), reaction time (4-12 hours), and amount of enzyme (0.1-0.5 g). The optimum conditions derived via RSM were reaction temperature 60°C, reaction time 2.34 hours, and amount of enzyme 0.3 g. The actual experimental yield was 59.6% ferulate ester under optimum condition, which compared well to the maximum predicted value of 58.0%.

Keywords: ferulic acid, enzymatic synthesis, esters, RSM

Procedia PDF Downloads 315

Authors: Fatiha Teklal, Bachir Kacimi, Arezki Djebbar

Abstract:

The study and application of composite materials are a truly interdisciplinary endeavor that has been enriched by contributions from chemistry, physics, materials science, mechanics and manufacturing engineering. The understanding of the interface (or interphase) in composites is the central point of this interdisciplinary effort. From the early development of composite materials of various nature, the optimization of the interface has been of major importance. Even more important, the ideas linking the properties of composites to the interface structure are still emerging. In our study, we need a direct characterization of the interface; the micromechanical tests we are addressing seem to meet this objective and we chose to use two complementary tests simultaneously. The microindentation test that can be applied to real composites and the drop test, preferred to the pull-out because of the theoretical possibility of studying systems with high adhesion (which is a priori the case with our systems). These two tests are complementary because of the principle of the model specimen used for both the first "compression indentation" and the second whose fiber is subjected to tensile stress called the drop test. Comparing the results obtained by the two methods can therefore be rewarding.

Keywords: Fiber, Interface, Matrix, Micromechanics, Pull-out

Procedia PDF Downloads 99

Authors: Urvah-Til-Vusqa, Unsa Noreen, Ayesha Hussain, Abdul Hafeez, Rafia Asghar, Sidrat Nasir

Abstract:

Natural dyes offer an alternative better application in textiles than synthetic ones. The present study will be aimed to employ natural dye extracted from Cordyline terminalis plant and its application into viscose under the influence of gamma radiations. The colorant extraction will be done by boiling dracaena leaves powder in aqueous, alkaline and ethyl acetate mediums. Both dye powder and fabric will be treated with different doses (5-20 kGy) of gamma radiations. The antioxidant, antimicrobial and hemolytic activities of the extracts will also be determined. Different tests of fabric characterization (before and after radiations treatment) will be employed. Dyeing variables just as time, temperature and M: L will be applied for optimization. Standard methods for ISO to evaluate color fastness to light, washing and rubbing will be employed for improvement of color strength 1.5-15.5% of Al, Fe, Cr, and Cu as mordants will be employed through pre, post and meta mordanting. Color depth % & L*, a*, b* and L*, C*, h values will be recorded using spectra flash SF650.

Keywords: natural dyes, gamma radiations, Cordyline terminalis, ecofriendly dyes

Procedia PDF Downloads 580

Authors: Yalong Jiang, Zheru Chi

Abstract:

In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

Procedia PDF Downloads 135

Authors: A. Zaher, A. Traore, F. Thiéry, T. Talbert, B. Shaer

Abstract:

In this paper, an intelligent approach is proposed to optimize the orientation of continuous solar tracking systems on cloudy days. Considering the weather case, the direct sunlight is more important than the diffuse radiation in case of clear sky. Thus, the panel is always pointed towards the sun. In case of an overcast sky, the solar beam is close to zero, and the panel is placed horizontally to receive the maximum of diffuse radiation. Under partly covered conditions, the panel must be pointed towards the source that emits the maximum of solar energy and it may be anywhere in the sky dome. Thus, the idea of our approach is to analyze the images, captured by ground-based sky camera system, in order to detect the zone in the sky dome which is considered as the optimal source of energy under cloudy conditions. The proposed approach is implemented using experimental setup developed at PROMES-CNRS laboratory in Perpignan city (France). Under overcast conditions, the results were very satisfactory, and the intelligent approach has provided efficiency gains of up to 9% relative to conventional continuous sun tracking systems.

Keywords: clouds detection, fuzzy inference systems, images processing, sun trackers

Procedia PDF Downloads 174

Authors: Shuen-Tai Wang, Yu-Ching Lin

Abstract:

With more and more workflow systems adopting cloud as their execution environment, it presents various challenges that need to be addressed in order to be utilized efficiently. This paper introduces a method for resource provisioning based on our previous research of dynamic allocation and its pipeline processes. We present an abstraction for workload scheduling in which independent tasks get scheduled among various available processors of distributed computing for optimization. We also propose an integrated web-based workflow designer by taking advantage of the HTML5 technology and chaining together multiple tools. In order to make the combination of multiple pipelines executing on the cloud in parallel, we develop a script translator and an execution engine for workflow management in the cloud. All information is known in advance by the workflow engine and tasks are allocated according to the prior knowledge in the repository. This proposed effort has the potential to provide support for process definition, workflow enactment and monitoring of workflow processes. Users would benefit from the web-based system that allows creation and execution of pipelines without scripting knowledge.

Keywords: workflow systems, resources provisioning, workload scheduling, web-based, workflow engine

Procedia PDF Downloads 140