Search results for: particle union optimization algorithm

Authors: L. Shafiq, A. Rigby

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All polyethylene processes are highly exothermic, and the safe removal of the heat of reaction is a fundamental issue in the process design. In slurry and gas processes, the velocity of the polymer particles in the reactor and external coolers can be very high, and under certain conditions, this can lead to static charging of these particles. Such static charged polymer particles may start building up on the reactor wall, limiting heat transfer, and ultimately leading to severe reactor fouling and forced reactor shut down. Statsafe™ is an FDA approved anti-fouling additive currently used around the world for polyolefin production as an anti-fouling additive. The unique polymer chemistry aids static discharge, which prevents the build-up of charged polyolefin particles, which could lead to fouling. Statsafe™ is being used and trailed in gas, slurry, and a combination of these technologies around the world. We will share data to demonstrate how the use of Statsafe™ allows more stable operation at higher solids level by eliminating static, which would otherwise prevent closer packing of particles in the hydrocarbon slurry. Because static charge generation depends also on the concentration of polymer particles in the slurry, the maximum slurry concentration can be higher when using Statsafe™, leading to higher production rates. The elimination of fouling also leads to less downtime. Special focus will be made on the impact anti-static additives have on catalyst performance within the polymerization process and how this has been measured. Lab-scale studies have investigated the effect on the activity of Ziegler Natta catalysts when anti-static additives are used at various concentrations in gas and slurry, polyethylene and polypropylene processes. An in-depth gas phase study investigated the effect of additives on the final polyethylene properties such as particle size, morphology, fines, bulk density, melt flow index, gradient density, and melting point.

Keywords: anti-static additives, catalyst performance, FDA approved anti-fouling additive, polymerisation

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Authors: Vadanasundari Vedarethinam, Kun Qian

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The metabolic analysis is more distal over proteomics and genomics engaging in clinics and needs rationally distinct techniques, designed materials, and device for clinical diagnosis. Conventional techniques such as spectroscopic techniques, biochemical analyzers, and electrochemical have been used for metabolic diagnosis. Currently, there are four major challenges including (I) long-term process in sample pretreatment; (II) difficulties in direct metabolic analysis of biosamples due to complexity (III) low molecular weight metabolite detection with accuracy and (IV) construction of diagnostic tools by materials and device-based platforms for real case application in biomedical applications. Development of chips with nanomaterial is promising to address these critical issues. Mass spectroscopy (MS) has displayed high sensitivity and accuracy, throughput, reproducibility, and resolution for molecular analysis. Particularly laser desorption/ ionization mass spectrometry (LDI MS) combined with devices affords desirable speed for mass measurement in seconds and high sensitivity with low cost towards large scale uses. We developed a plasmonic chip for clinical metabolic fingerprinting as a hot carrier in LDI MS by series of chips with gold nanoshells on the surface through controlled particle synthesis, dip-coating, and gold sputtering for mass production. We integrated the optimized chip with microarrays for laboratory automation and nanoscaled experiments, which afforded direct high-performance metabolic fingerprinting by LDI MS using 500 nL of serum, urine, cerebrospinal fluids (CSF) and exosomes. Further, we demonstrated on-chip direct in-vitro metabolic diagnosis of early-stage lung cancer patients using serum and exosomes without any pretreatment or purifications. To our best knowledge, this work initiates a bionanotechnology based platform for advanced metabolic analysis toward large-scale diagnostic use.

Keywords: plasmonic chip, metabolic fingerprinting, LDI MS, in-vitro diagnostics

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Authors: A. Movaghar, R. A. Mahdavinejad

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To analyze the effect of various parameters of fluid on the material properties such as surface and depth defects and/or cracks, it is possible to determine the affection of pressure field on these specifications. Stress tensor analysis is also able to determine the points in which the probability of defection creation is more. Besides, from pressure field, it is possible to analyze the affection of various fluid specifications such as viscosity and density on defect created in the material. In this research, the concerned boundary conditions are analyzed first. Then the solution network and stencil used are mentioned. With the determination of relevant equation on the fluid flow between notch and matrix and their discretion according to the governed boundary conditions, these equations can be solved. Finally, with the variation creations on fluid parameters such as density and viscosity, the affection of these variations can be determined on pressure field. In this direction, the flowchart and solution algorithm with their results as vortex and current function contours for two conditions with most applications in pressing process are introduced and discussed.

Keywords: pressing, notch, matrix, flow function, vortex

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

Procedia PDF Downloads 118

Authors: Afshin Shahbazi, Hadi Shadi Naghadeh, Ahmad Khodadadi Darban

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In the Magnetically Assisted Chemical Separation (MACS) process, tiny ferromagnetic particles coated with solvent extractant are used to selectively separate radionuclides and hazardous metals from aqueous waste streams. The contaminant-loaded particles are then recovered from the waste solutions using a magnetic field. In the present study, Cyanex272 or C272 (bis (2,4,4-trimethylpentyl) phosphinic acid) coated magnetic particles are being evaluated for the possible application in the extraction of Thorium (IV) from nuclear waste streams. The uptake behaviour of Th(IV) from nitric acid solutions was investigated by batch studies. Adsorption of Thorium (IV) from aqueous solution onto adsorbent was investigated in a batch system. Adsorption isotherm and adsorption kinetic studies of Thorium (IV) onto nanoparticles coated Cyanex272 were carried out in a batch system. The factors influencing Thorium (IV) adsorption were investigated and described in detail, as a function of the parameters such as initial pH value, contact time, adsorbent mass, and initial Thorium (IV) concentration. Magnetically Assisted Chemical Separation (MACS) process adsorbent showed best results for the fast adsorption of Th (IV) from aqueous solution at aqueous phase acidity value of 0.5 molar. In addition, more than 80% of Th (IV) was removed within the first 2 hours, and the time required to achieve the adsorption equilibrium was only 140 minutes. Langmuir and Frendlich adsorption models were used for the mathematical description of the adsorption equilibrium. Equilibrium data agreed very well with the Langmuir model, with a maximum adsorption capacity of 48 mg.g-1. Adsorption kinetics data were tested using pseudo-first-order, pseudo-second-order and intra-particle diffusion models. Kinetic studies showed that the adsorption followed a pseudo-second-order kinetic model, indicating that the chemical adsorption was the rate-limiting step.

Keywords: Thorium (IV) adsorption, MACS process, magnetic nanoparticles, Cyanex272

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Authors: Ajoy Kumar Das, Prasenjit Dey

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Heat transfer due to forced convection of copper water based nanofluid has been predicted by Artificial Neural network (ANN). The present nanofluid is formed by mixing copper nano particles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are kept constant at 100. The back propagation algorithm is used to train the network. The present ANN is trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Ansys Fluent. The numerical simulation based results are compared with the back propagation based ANN results. It is found that the forced convection heat transfer of water based nanofluid can be predicted correctly by ANN. It is also observed that the back propagation ANN can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method.

Keywords: forced convection, square cylinder, nanofluid, neural network

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Authors: Lee Myung-Won, Kwak Keun-Chang

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This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation

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Authors: Lin Meng, Xi Lu, Haibo Wang, Yong Song, Lili Cao, Wenfang Song, Yong Hu

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China has abundant heavy oil resources, and thermal recovery is the main recovery method for heavy oil reservoirs. However, high energy consumption, high carbon emission and high production costs make heavy oil thermal recovery unsustainable. It is urgent to explore a replacement for developing technology. A low Carbon and cost technology of heavy oil recovery, chemical viscosity-reduction in layer (CVRL), is developed by the petroleum exploration and development research institute of Sinopec via investigated mechanisms, synthesized products, and improved oil production technologies, as follows: (1) Proposed a cascade viscous mechanism of heavy oil. Asphaltene and resin grow from free molecules to associative structures further to bulk aggregations by π - π stacking and hydrogen bonding, which causes the high viscosity of heavy oil. (2) Aimed at breaking the π - π stacking and hydrogen bond of heavy oil, the copolymer of N-(3,4-dihydroxyphenethyl) acryl amide and 2-Acrylamido-2-methylpropane sulfonic acid was synthesized as a viscosity reducer. It achieves a viscosity reduction rate of>80% without shearing for heavy oil (viscosity < 50000 mPa‧s), of which fluidity is evidently improved in the layer. (3) Synthesized hydroxymethyl acrylamide-maleic acid-decanol ternary copolymer self-assembly plugging agent. The particle size is 0.1 μm-2 mm adjustable, and the volume is 10-500 times controllable, which can achieve the efficient transportation of viscosity reducer to enriched oil areas. CVRL has applied 400 wells until now, increasing oil production by 470000 tons, saving 81000 tons of standard coal, reducing CO2 emissions by 174000 tons, and reducing production costs by 60%. It promotes the transformation of heavy oil towards low energy consumption, low carbon emissions, and low-cost development.

Keywords: heavy oil, chemical viscosity-reduction, low carbon, viscosity reducer, plugging agent

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Authors: Rakesh Choudhary, Ajay Kumar, Deepak Sharma

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The main goal of this paper is to investigate the challenges and benefits of IoT integration in biogas production. This overview explains how the inclusion of IoT can enhance biogas production efficiency. Therefore, such collected data can be explored by advanced analytics, including Artificial intelligence (AI) and Machine Learning (ML) algorithms, consequently improving bio-energy processes. To boost biogas generation efficiency, this report examines the use of IoT devices for real-time data collection on key parameters, e.g., pH, temperature, gas composition, and microbial growth. Real-time monitoring through big data has made it possible to detect diverse, complex trends in the process of producing biogas. The Informed by advanced analytics can also help in improving bio-energy production as well as optimizing operational conditions. Moreover, IoT allows remote observation, control and management, which decreases manual intervention needed whilst increasing process effectiveness. Such a paradigm shift in the incorporation of IoT technologies into biogas production systems helps to achieve higher productivity levels as well as more practical biomass quality biomethane through real-time monitoring-based proactive decision-making, thus driving continuous performance improvement.

Keywords: internet of things, biogas, renewable energy, sustainability, anaerobic digestion, real-time monitoring, optimization

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Authors: Baoshan Wei, Shuai Han, Xing Zhang

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Local obstacle avoidance is critical for mobile robot navigation. It is a challenging task to ensure path optimality and safety in cluttered environments. We proposed an Environment Aware Dynamic Window Approach in this paper to cope with the issue. The method integrates environment characterization into Dynamic Window Approach (DWA). Two strategies are proposed in order to achieve the integration. The local goal strategy guides the robot to move through openings before approaching the final goal, which solves the local minima problem in DWA. The adaptive control strategy endows the robot to adjust its state according to the environment, which addresses path safety compared with DWA. Besides, the evaluation shows that the path generated from the proposed algorithm is safer and smoother compared with state-of-the-art algorithms.

Keywords: adaptive control, dynamic window approach, environment aware, local obstacle avoidance, mobile robots

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Authors: I. Djefour, M. Saidi, I. Tlemsani, S. Toubal

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Lignin is a molecule derived from wood and also generated as waste from the paper industry. With a view to its valorization and protection of the environment, we are interested in its use as a superplasticizer-type adjuvant in mortars and concretes to improve their mechanical strengths. The additives of the concrete have a very strong influence on the properties of the fresh and / or hardened concrete. This study examines the development and use of industrial waste and lignin extracted from a renewable natural source (wood) in cementitious materials. The use of these resources is known at present as a definite resurgence of interest in the development of building materials. Physicomechanical characteristics of mortars are determined by optimization quantity of the natural superplasticizer. The results show that the mechanical strengths of mortars based on natural adjuvant have improved by 20% (64 MPa) for a W/C ratio = 0.4, and the amount of natural adjuvant of dry extract needed is 40 times smaller than commercial adjuvant. This study has a scientific impact (improving the performance of the mortar with an increase in compactness and reduction of the quantity of water), ecological use of the lignin waste generated by the paper industry) and economic reduction of the cost price necessary to elaboration of self-compacting mortars and concretes).

Keywords: biopolymer (lignin), industrial waste, mechanical resistances, self compacting mortars (SCM)

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Authors: Celina Pestano-Gabino, Concepcion Gonzalez-Concepcion, M. Candelaria Gil-Fariña

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Some estimation methods for VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. It is known that if the data sample is populous enough and the dimension of the Hankel matrix is unnecessarily large, this may result in an unnecessary number of computations as well as in numerical problems. In this sense, the aim of this paper is two-fold. First, we provide some theoretical results for these matrices which translate into a lower dimension for the matrices normally used in the algorithms. This contribution thus serves to improve those methods from a numerical and, presumably, statistical point of view. Second, we have chosen an estimation algorithm to illustrate in practice our improvements. The results we obtained in a simulation of VARMA models show that an increase in the size of the Hankel matrix beyond the theoretical bound proposed as valid does not necessarily lead to improved practical results. Therefore, for future research, we propose conducting similar studies using any of the linear system estimation methods that depend on Hankel matrices.

Keywords: covariances Hankel matrices, Kronecker indices, system identification, VARMA models

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Authors: Aditya Kumar

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One of the most fascinating properties of various insects and plant surfaces in nature is their water-repellent (superhydrophobicity) capability. The nature offers new insights to learn and replicate the same in designing artificial superhydrophobic structures for a wide range of applications such as micro-fluidics, micro-electronics, textiles, self-cleaning surfaces, anti-corrosion, anti-fingerprint, oil/water separation, etc. In general, artificial superhydrophobic surfaces are synthesized by creating roughness and then treating the surface with low surface energy materials. In this work, various super-hydrophobic coatings on metallic surfaces (aluminum, steel, copper, steel mesh) were synthesized by chemical etching process using different etchants and fatty acid. Also, SiO2 nano/micro-particles embedded polyethylene, polystyrene, and poly(methyl methacrylate) superhydrophobic coatings were synthesized on glass substrates. Also, the effect of process parameters such as etching time, etchant concentration, and particle concentration on wettability was studied. To know the applications of the coatings, surface morphology, contact angle, self-cleaning, corrosion-resistance, and water-repellent characteristics were investigated at various conditions. Furthermore, durabilities of coatings were also studied by performing thermal, ultra-violet, and mechanical stability tests. The surface morphology confirms the creation of rough microstructures by chemical etching or by embedding particles, and the contact angle measurements reveal the superhydrophobic nature. Experimentally it is found that the coatings have excellent self-cleaning, anti-corrosion and water-repellent nature. These coatings also withstand mechanical disturbances such surface bending, adhesive peeling, and abrasion. Coatings are also found to be thermal and ultra-violet stable. Additionally, coatings are also reproducible. Hence aforesaid durable superhydrophobic surfaces have many potential industrial applications.

Keywords: superhydrophobic, water-repellent, anti-corrosion, self-cleaning

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Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: A. Biran, P. Sobhe Bidari, K. Raahemifar

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Diabetic Retinopathy (DR) is an eye disease that leads to blindness. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness; hence, many automated algorithms have been proposed to extract hemorrhages and exudates. In this paper, an automated algorithm is presented to extract hemorrhages and exudates separately from retinal fundus images using different image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Since Optic Disc is the same color as the exudates, it is first localized and detected. The presented method has been tested on fundus images from Structured Analysis of the Retina (STARE) and Digital Retinal Images for Vessel Extraction (DRIVE) databases by using MATLAB codes. The results show that this method is perfectly capable of detecting hard exudates and the highly probable soft exudates. It is also capable of detecting the hemorrhages and distinguishing them from blood vessels.

Keywords: diabetic retinopathy, fundus, CHT, exudates, hemorrhages

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Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

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Authors: Abdulkarim Nasir, Alhassan T. Yahaya, Hauwa T. Abdulkarim, Abdussalam El-Suleiman, Yakubu K. Abubakar

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The need for sustainable and reliable electricity supply in rural communities of Nigeria remains a pressing issue, given the country's vast energy deficit and the significant number of inhabitants lacking access to electricity. This research focuses on the development of a portable hybrid renewable energy system designed to provide a sustainable and efficient electricity supply to these underserved regions. The proposed system integrates multiple renewable energy sources, specifically solar and wind, to harness the abundant natural resources available in Nigeria. The design and development process involves the selection and optimization of components such as photovoltaic panels, wind turbines, energy storage units (batteries), and power management systems. These components are chosen based on their suitability for rural environments, cost-effectiveness, and ease of maintenance. The hybrid system is designed to be portable, allowing for easy transportation and deployment in remote locations with limited infrastructure. Key to the system's effectiveness is its hybrid nature, which ensures continuous power supply by compensating for the intermittent nature of individual renewable sources. Solar energy is harnessed during the day, while wind energy is captured whenever wind conditions are favourable, thus ensuring a more stable and reliable energy output. Energy storage units are critical in this setup, storing excess energy generated during peak production times and supplying power during periods of low renewable generation. These studies include assessing the solar irradiance, wind speed patterns, and energy consumption needs of rural communities. The simulation results inform the optimization of the system's design to maximize energy efficiency and reliability. This paper presents the development and evaluation of a 4 kW standalone hybrid system combining wind and solar power. The portable device measures approximately 8 feet 5 inches in width, 8 inches 4 inches in depth, and around 38 feet in height. It includes four solar panels with a capacity of 120 watts each, a 1.5 kW wind turbine, a solar charge controller, remote power storage, batteries, and battery control mechanisms. Designed to operate independently of the grid, this hybrid device offers versatility for use in highways and various other applications. It also presents a summary and characterization of the device, along with photovoltaic data collected in Nigeria during the month of April. The construction plan for the hybrid energy tower is outlined, which involves combining a vertical-axis wind turbine with solar panels to harness both wind and solar energy. Positioned between the roadway divider and automobiles, the tower takes advantage of the air velocity generated by passing vehicles. The solar panels are strategically mounted to deflect air toward the turbine while generating energy. Generators and gear systems attached to the turbine shaft enable power generation, offering a portable solution to energy challenges in Nigerian communities. The study also addresses the economic feasibility of the system, considering the initial investment costs, maintenance, and potential savings from reduced fossil fuel use. A comparative analysis with traditional energy supply methods highlights the long-term benefits and sustainability of the hybrid system.

Keywords: renewable energy, solar panel, wind turbine, hybrid system, generator

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Authors: Obafemi Akinwotu, Aylin Tas, Tony Taylor, Bukola Onarinde

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The study was conducted to explore the methods and tools to improve texture and preserve the total phenolic and antioxidant compounds of dysphagia foods produced from carrot-based puree with decolourised Chlorella algae. Textural properties (Texture profile analysis [TPA]; the International Dysphagia Diet Standardization Initiative, particle size test [PST]) and rheological properties (viscosity and viscoelastic properties) of carrot puree defrosted by different treatments (microwave, steamer, oven), were characterised using hydrocolloids (guar gum, k. carrageenan, and xanthan gum), and the results were compared to a level 4 commercial sample. DPPH (2,2-diphenyl-1-picrylhydrazyl) antiradical scavenging radicals and total phenolic contents were employed to evaluate the total phenolics, and radical scavenging properties of defrosted carrot puree sonicated carrot puree (20 Hz, 30 min, 60 oC), and vacuum-dried carrot powder with the addition of algae. Results show that the viscosity, viscoelasticity test, TPA, and PST of the commercial sample were comparable to those of guar gum and xanthan gum containing puree, suggesting that they could be used as dysphagia diets. There was no noticeable decolourisation of the Chlorella pigment. Additionally, the use of the microwave, stemmer, and oven for defrosting treatment had an impact on the textural characteristics of the moulded samples upon cooling and also contributed to the reduction in the total phenolic and antioxidant properties of the samples. Sonication treatments of algae exposure reduced the cloudiness of the green pigment and lightened the colour of the samples containing algae, and they also reduced the drying time from 2.5 to 1.5 hours during the preliminary work. The low-temperature vacuum- and freeze-dried samples increased the concentration of the powder and resulted in an increase in the total phenolic content of the dry samples. The dried products may therefore have the potential to become more nutrient-dense to benefit the health of individuals with dysphagia.

Keywords: dysphagia, elderly, hydrocolloids, carrot puree

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Authors: Hossein Motaghedi, Kaveh Arkani, Siavash Salamatpoor

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Usually the piles are important tools for safety and economical design of high and heavy structures. For this aim the response of single pile under dynamic load is so effective. Also, the agents which have influence on single pile response are properties of pile geometrical, soil and subjected loads. In this study the finite difference numerical method and by using FLAC 3D software is used for evaluation of single pile behavior under peak ground acceleration (PGA) of El Centro earthquake record in California (1940). The results of this models compared by experimental results of other researchers and it will be seen that the results of this models are approximately coincide by experimental data's. For example the maximum moment and displacement in top of the pile is corresponding to the other experimental results of pervious researchers. Furthermore, in this paper is tried to evaluate the effective properties between soil and pile. The results is shown that by increasing the pile diagonal, the pile top displacement will be decreased. As well as, by increasing the length of pile, the top displacement will be increased. Also, by increasing the stiffness ratio of pile to soil, the produced moment in pile body will be increased and the taller piles have more interaction by soils and have high inertia. So, these results can help directly to optimization design of pile dimensions.

Keywords: pile seismic response, interaction between soil and pile, numerical analysis, FLAC 3D

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Authors: Akif Kutlu, Merve Ermis, Nihal Eratlı, Mehmet H. Omurtag

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The objective of this study is to investigate the forced vibration analysis of a planar curved beam lying on elastic foundation by using the mixed finite element method. The finite element formulation is based on the Timoshenko beam theory. In order to solve the problems in frequency domain, the element matrices of two nodded curvilinear elements are transformed into Laplace space. The results are transformed back to the time domain by the well-known numerical Modified Durbin’s transformation algorithm. First, the presented finite element formulation is verified through the forced vibration analysis of a planar curved Timoshenko beam resting on Winkler foundation and the finite element results are compared with the results available in the literature. Then, the forced vibration analysis of a planar curved beam resting on Winkler-Pasternak foundation is conducted.

Keywords: curved beam, dynamic analysis, elastic foundation, finite element method

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Authors: A. Assif

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This paper presents the modeling of a prototype distribution static compensator (D-STATCOM) for voltage sag and swell mitigation in an unbalanced distribution system. Here the concept that an inverter can be used as generalized impedance converter to realize either inductive or capacitive reactance has been used to mitigate power quality issues of distribution networks. The D-STATCOM is here supposed to replace the widely used StaticVar Compensator (SVC). The scheme is based on the Voltage Source Converter (VSC) principle. In this model PWM based control scheme has been implemented to control the electronic valves of VSC. Phase shift control Algorithm method is used for converter control. The D-STATCOM injects a current into the system to mitigate the voltage sags. In this paper the modeling of D¬STATCOM has been designed using MATLAB SIMULINIC. Accordingly, simulations are first carried out to illustrate the use of D-STATCOM in mitigating voltage sag in a distribution system. Simulation results prove that the D-STATCOM is capable of mitigating voltage sag as well as improving power quality of a system.

Keywords: D-STATCOM, voltage sag, voltage source converter (VSC), phase shift control

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Authors: Al Ameen H., Rakesh P.

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To improve the combustion efficiency of fuels and to reduce the emissions of pollutants as well as to improve heat transfer characteristics of fuels, both non-metallic and metallic nanoparticles can be added into it. By varying the concentration and size of nano particles added into the fuels, behaviour of droplet combustion and hence heat generated can be altered. In case of solid or liquid fuels, surface area of the fuel in contact with oxidizer(gaseous) is small because of higher density compared to gases. If the surface area of fuel exposed to the oxidizer is very small, then the combustion will not occur, because the combustion rate is proportional to the surface area of fuel droplet. To avoid such instance there is a way to increase the exposed surface area. To increase the specific surface area available for reaction, the particle size can be reduced. If the additives are solid then by reducing the particles size the specific surface area of liquid fuel can be increased. For the liquid fuels the exposed surface area available for combustion can be increased by suspending nanoparticles. Addition of non-metallic and metallic nanoparticles in fuels improves its combustion efficiency by enhancing the thermo-physical properties. The burn rate constants and temperatures of Kerosene-Oxygen combustion for fuel droplet sizes of 50μm, 75μm, 100μm and 125μm under varying concentrations of 25%, 50%, 75% and 100% are studied numerically and its characteristic velocities are determined. Later the burn rate constants of fuel with concentrations of 0.5%, 1.0% and 2.0% by weight of aluminium nanoparticles are added. The spray combustion characteristics of such nano-fuel has improved the combustion temperature by the addition of aluminium nanoparticles. Thus, aluminium nanoparticles have improved burn rate and characteristic velocity of Kerosene-Oxygen combustion. An increase of 40% in characteristic velocity is observed.

Keywords: burn rate, characteristic velocity, combustion, thermo-physical properties

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Authors: Naser Ahmadi Sani, Karim Solaimani, Lida Razaghnia, Jalal Zandi

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In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.

Keywords: Haraz basin, change detection, land-use, satellite data

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Authors: Oleg A. Romanovskii, Vladimir D. Burlakov, Sergey I. Dolgii, Olga V. Kharchenko, Alexey A. Nevzorov, Alexey V. Nevzorov

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The paper presents the results of DIAL measurements of the vertical ozone distribution. The ozone lidar operate as part of the measurement complex at Siberian Lidar Station (SLS) of V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk (56.5ºN; 85.0ºE) and designed for study of the vertical ozone distribution in the upper troposphere–lower stratosphere. Most suitable wavelengths for measurements of ozone profiles are selected. We present an algorithm for retrieval of vertical distribution of ozone with temperature and aerosol correction during DIAL lidar sounding of the atmosphere. The temperature correction of ozone absorption coefficients is introduced in the software to reduce the retrieval errors. Results of lidar measurement at wavelengths of 299 and 341 nm agree with model estimates, which point to acceptable accuracy of ozone sounding in the 6–18 km altitude range.

Keywords: lidar, ozone distribution, atmosphere, DIAL

Procedia PDF Downloads 495

Authors: Adefarati Oloruntoba, Yongmin Zhang, Hongliang Xiao

Abstract:

An annular fluidized bed (AFB) is gaining extensive application in the process industry due to its efficient gas-solids contacting. But a direct evaluation of its reaction performance is still lacking. In this paper, comparative 3D Euler–Lagrange multiphase-particle-in-cell (MP-PIC) computations are performed to assess the reaction performance of AFB relative to a bubbling fluidized bed (BFB) in an FCC regeneration process. By using the energy-minimization multi-scale (EMMS) drag model with a suitable heterogeneity index, the MP-PIC simulation predicts the typical fountain region in AFB and solids holdup of BFB, which is consistent with an experiment. Coke combustion rate, flue gas and temperature profile are utilized as the performance indicators, while related bed hydrodynamics are explored to account for the different performance under varying superficial gas velocities (0.5 m/s, 0.6 m/s, and 0.7 m/s). Simulation results indicate that the burning rates of coke and its species are relatively the same in both beds, albeit marginal increase in BFB. Similarly, the shape and evolution time of flue gas (CO, CO₂, H₂O and O₂) curves are indistinguishable but match the coke combustion rates. However, AFB has high proclivity to high temperature-gradient as higher gas and solids temperatures are predicted in the freeboard. Moreover, for both beds, the effect of superficial gas velocity is only conspicuous on the temperature but negligible on combustion efficiency and effluent gas emissions due to constant gas volumetric flow rate and bed loading criteria. Cross-flow of solids from the annulus to the spout region as well as the high primary gas in the AFB directly assume the underlying mechanisms for its unique gas-solids hydrodynamics (pressure, solids holdup, velocity, mass flux) and local spatial homogeneity, which in turn influence the reactor performance. Overall, the study portrays AFB as a cheap alternative reactor to BFB for catalyst regeneration.

Keywords: annular fluidized bed, bubbling fluidized bed, coke combustion, flue gas, fountaining, CFD, MP-PIC, hydrodynamics, FCC regeneration

Procedia PDF Downloads 162

Authors: Chol Min Yu, Sok Chol Ri

Abstract:

The optimization of formation and growth of metallic nuggets during self-reduction of coal composite briquette (CCB here) is essential to increase the yield of valuable metals. The formation of metallic nuggets was investigated theoretically and experimentally during the reduction of coal composite briquette made from stainless steel dust and coal. The formation of metallic nuggets is influenced by slag viscosity and interfacial tension between the liquid metal and the slag in the reduced product. Surface tensions of liquid metal and slag are rather strong, respectively, due to the high basicity of its slag. Strong surface tensions of them lead to increase of interfacial tension between the liquid metal and the slag to be favorable to the growth of metallic nuggets. The viscosity of slag and interfacial tension between the liquid metal and the slag depends on the temperature and composition of the slag. The formation and the growth of metallic nuggets depend on carbon to oxygen ratio FC/O and temperature.

Keywords: stainless steel dust, coal-composite briquette, temperature, high basicity, interfacial tension

Procedia PDF Downloads 79

Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

Abstract:

Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

Procedia PDF Downloads 109

Authors: Tolga Barisik, Ali Fuat Guneri, K. Dastan

Abstract:

Major industrial facilities have a high potential for fire risk. In particular, the indices used for the detection of hidden fire are used very effectively in order to prevent the fire from becoming dangerous in the initial stage. These indices provide the opportunity to prevent or intervene early by determining the stage of the fire, the potential for hazard, and the type of the combustion agent with the percentage values of the ambient air components. In this system, artificial neural network will be modeled with the input data determined using the Levenberg-Marquardt algorithm, which is a multi-layer sensor (CAA) (teacher-learning) type, before modeling the modeling methods in the literature. The actual values produced by the indices will be compared with the outputs produced by the network. Using the neural network and the curves to be created from the resulting values, the feasibility of performance determination will be investigated.

Keywords: artifical neural networks, fire, Graham Index, levenberg-marquardt algoritm, oxygen decrease percentage index, risk assessment, Trickett Index

Procedia PDF Downloads 135

Authors: M. A. Alavianmehr, A. Tashk, A. Sodagaran

Abstract:

Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive mixture Gaussian model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method.

Keywords: image processing, background models, video surveillance, foreground detection, Gaussian mixture model

Procedia PDF Downloads 514