Search results for: Particle Swarm Optimization

Authors: Ji Xi, Cheng Song Chin, Ehsan Mesbahi

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Structure-borne noise is an important aspect of offshore platform sound field. It can be generated either directly by vibrating machineries induced mechanical force, indirectly by the excitation of structure or excitation by incident airborne noise. Therefore, limiting of the transmission of vibration energy throughout the offshore platform is the key to control the structure-borne noise. This is usually done by introducing damping treatment to the steel structures. Two types of damping treatment using on-board are presented. By conducting a statistical energy analysis (SEA) simulation on a jack-up rig, the noise level in the source room, the neighboring rooms, and remote living quarter cabins are compared before and after the damping treatments been applied. The results demonstrated that, in the source neighboring room and living quarter area, there is a significant noise reduction with the damping treatment applied, whereas in the source room where air-borne sound predominates that of structure-borne sound, the impact is not obvious. The subsequent optimization design of damping treatment in the offshore platform can be made which enable acoustic professionals to implement noise control during the design stage for offshore crews’ hearing protection and habitant comfortability.

Keywords: statistical energy analysis, damping treatment, noise control, offshore platform

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Authors: B. J. Babalola, M. B. Shongwe

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Spark Plasma Sintering technique is a novel processing method that produces limited grain growth and highly dense variety of materials; alloys, superalloys, and carbides just to mention a few. However, initial particle size and spark plasma sintering parameters are factors which influence the grain growth and mechanical properties of sintered materials. Ni-Cr alloys are regarded as the most promising alloys for aerospace turbine blades, owing to the fact that they meet the basic requirements of desirable mechanical strength at high temperatures and good resistance to oxidation. The conventional method of producing this alloy often results in excessive grain growth and porosity levels that are detrimental to its mechanical properties. The effect of sintering temperature was evaluated on the microstructure and mechanical properties of the nanostructured Ni-17Cr alloy. Nickel and chromium powder were milled using high energy ball milling independently for 30 hours, milling speed of 400 revs/min and ball to powder ratio (BPR) of 10:1. The milled powders were mixed in the composition of Nickel having 83 wt % and chromium, 17 wt %. This was sintered at varied temperatures from 800°C, 900°C, 1000°C, 1100°C and 1200°C. The structural characteristics such as porosity, grain size, fracture surface and hardness were analyzed by scan electron microscopy and X-ray diffraction, Archimedes densitometry, micro-hardness tester. The corresponding results indicated an increase in the densification and hardness property of the alloy as the temperature increases. The residual porosity of the alloy reduces with respect to the sintering temperature and in contrast, the grain size was enhanced. The study of the mechanical properties, including hardness, densification shows that optimum properties were obtained for the sintering temperature of 1100°C. The advantages of high sinterability of Ni-17Cr alloy using milled powders and microstructural details were discussed.

Keywords: densification, grain growth, milling, nanostructured materials, sintering temperature

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Authors: Mapatsakon Sarapat, Mongkol Ketwongsa, Somchat Sonasang, Preecha Yupapin

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The designed and performed preliminary tests on a space-time control circuit using a two-level system circuit with a 4-5 cm diameter microstrip for realistic teleportation have been demonstrated. It begins by calculating the parameters that allow a circuit that uses the alternative current (AC) at a specified frequency as the input signal. A method that causes electrons to move along the circuit perimeter starting at the speed of light, which found satisfaction based on the wave-particle duality. It is able to establish the supersonic speed (faster than light) for the electron cloud in the middle of the circuit, creating a timeline and propulsive force as well. The timeline is formed by the stretching and shrinking time cancellation in the relativistic regime, in which the absolute time has vanished. In fact, both black holes and white holes are created from time signals at the beginning, where the speed of electrons travels close to the speed of light. They entangle together like a capsule until they reach the point where they collapse and cancel each other out, which is controlled by the frequency of the circuit. Therefore, we can apply this method to large-scale circuits such as potassium, from which the same method can be applied to form the system to teleport living things. In fact, the black hole is a hibernation system environment that allows living things to live and travel to the destination of teleportation, which can be controlled from position and time relative to the speed of light. When the capsule reaches its destination, it increases the frequency of the black holes and white holes canceling each other out to a balanced environment. Therefore, life can safely teleport to the destination. Therefore, there must be the same system at the origin and destination, which could be a network. Moreover, it can also be applied to space travel as well. The design system will be tested on a small system using a microstrip circuit system that we can create in the laboratory on a limited budget that can be used in both wired and wireless systems.

Keywords: quantum teleportation, black-white hole, time, timeline, relativistic electronics

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Authors: Vanessa Morgado, Ricardo Bettencourt da Silva, Carla Palma

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The environmental pollution by microplastics is well recognized. Microplastics were already detected in various matrices from distinct environmental compartments worldwide, some from remote areas. Various methodologies and techniques have been used to determine microplastic in such matrices, for instance, sediment samples from the ocean bottom. In order to determine microplastics in a sediment matrix, the sample is typically sieved through a 5 mm mesh, digested to remove the organic matter, and density separated to isolate microplastics from the denser part of the sediment. The physical analysis of microplastic consists of visual analysis under a stereomicroscope to determine particle size, colour, and shape. The chemical analysis is performed by an infrared spectrometer coupled to a microscope (micro-FTIR), allowing to the identification of the chemical composition of microplastic, i.e., the type of polymer. Creating legislation and policies to control and manage (micro)plastic pollution is essential to protect the environment, namely the coastal areas. The regulation is defined from the known relevance and trends of the pollution type. This work discusses the assessment of contamination trends of a 700 km² oceanic area affected by contamination heterogeneity, sampling representativeness, and the uncertainty of the analysis of collected samples. The methodology developed consists of objectively identifying meaningful variations of microplastic contamination by the Monte Carlo simulation of all uncertainty sources. This work allowed us to unequivocally conclude that the contamination level of the studied area did not vary significantly between two consecutive years (2018 and 2019) and that PET microplastics are the major type of polymer. The comparison of contamination levels was performed for a 99% confidence level. The developed know-how is crucial for the objective and binding determination of microplastic contamination in relevant environmental compartments.

Keywords: measurement uncertainty, micro-ATR-FTIR, microplastics, ocean contamination, sampling uncertainty

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Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

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Authors: Atilla Bayram

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This paper presents the trajectory tracking control of a spatial redundant hybrid manipulator. This manipulator consists of two parallel manipulators which are a variable geometry truss (VGT) module. In fact, each VGT module with 3-degress of freedom (DOF) is a planar parallel manipulator and their operational planes of these VGT modules are arranged to be orthogonal to each other. Also, the manipulator contains a twist motion part attached to the top of the second VGT module to supply the missing orientation of the endeffector. These three modules constitute totally 7-DOF hybrid (parallel-parallel) redundant spatial manipulator. The forward kinematics equations of this manipulator are obtained, then, according to these equations, the inverse kinematics is solved based on an optimization with the joint limit avoidance. The dynamic equations are formed by using virtual work method. In order to test the performance of the redundant manipulator and the controllers presented, two different desired trajectories are followed by using the computed force control method and a switching control method. The switching control method is combined with the computed force control method and genetic algorithm. In the switching control method, the genetic algorithm is only used for fine tuning in the compensation of the trajectory tracking errors.

Keywords: computed force method, genetic algorithm, hybrid manipulator, inverse kinematics of redundant manipulators, variable geometry truss

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Authors: Ahsan Ahmed, Ming Lu, Syed Zaidi, Farhan Khan

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The Integrated Project Delivery (IPD) method has been proposed as the solution to tackle complexity and fragmentation in the real world while addressing the construction industry’s growing needs for productivity and sustainability. Although the private sector has taken the initiative in implementing IPD and taken advantage of new technology such as building information modeling (BIM) in delivering projects, IPD remains less known and rarely used in public sector construction. The focus of this paper is set on the use of IPD in projects in public sector, which is potentially complemented by the use of analytical functionalities for workface planning and construction oriented design enabled by recent research advances in BIM. Experiences and lessons learned from implementing IPD in the private sector and in BIM-based construction automation research would play a vital role in reducing barriers and eliminating issues in connection with project delivery in the public sector. The paper elaborates issues challenges, contractual relationships and the interactions throughout the planning, design and construction phases in the context of implementing IPD on construction projects in the public sector. A slab construction case is used as a ‘sandbox’ model to elaborate (1) the ideal way of communication, integration, and collaboration among all the parties involved in project delivery in planning and (2) the execution of projects by using IDP principles and optimization, simulation analyses.

Keywords: integrated project delivery, IPD, building information modeling, BIM

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Authors: Soultana Konstantinidou, Tiziana Schmidt, Elena Landi, Alessandro De Carli, Giovanni Maltinti, Darius Witt, Alicja Dziadosz, Agnieszka Lindstaedt, Michele Lai, Mauro Pistello, Valentina Cappello, Luciana Dente, Chiara Gabellini, Piotr Barski, Vittoria Raffa

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CRISPR/Cas9 technology has gained the interest of researchers in the field of biotechnology for genome editing. Since its discovery as a microbial adaptive immune defense, this system has been widely adopted and is acknowledged for having a variety of applications. However, critical barriers related to safety and delivery are persisting. Here, we propose a new concept of genome engineering, which is based on a nano-formulation of Cas9. The Cas9 enzyme was conjugated to a gold nanoparticle (AuNP-Cas9). The AuNP-Cas9 maintained its cleavage efficiency in vitro, to the same extent as the ribonucleoprotein, including non-conjugated Cas9 enzyme, and showed high gene editing efficiency in vivo in zebrafish embryos. Since CRISPR/Cas9 technology is extensively used in cancer research, melanoma was selected as a validation target. Cell studies were performed in A375 human melanoma cells. Particles per se had no impact on cell metabolism and proliferation. Intriguingly, the AuNP-Cas9 internalized spontaneously in cells and localized as a single particle in the cytoplasm and organelles. More importantly, the AuNP-Cas9 showed a high nuclear localization signal. The AuNP-Cas9, overcoming the delivery difficulties of Cas9, could be used in cellular biology and localization studies. Taking advantage of the plasmonic properties of gold nanoparticles, this technology could potentially be a bio-tool for combining gene editing and photothermal therapy in cancer cells. Further work will be focused on intracellular interactions of the nano-formulation and characterization of the optical properties.

Keywords: CRISPR/Cas9, gene editing, gold nanoparticles, nanotechnology

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Authors: Cian O'Shea, Ross O'Halloran, Peter Haigh

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Wireless Sensor Networks (WSN) are Internet of Things (IoT) edge devices. They are becoming widely adopted in many industries, including health care, building energy management, and conditional monitoring. As the scale of WSN deployments increases, the cost and complexity of battery replacement and disposal become more significant and in time may become a barrier to adoption. Harvesting ambient energies provide a pathway to reducing dependence on batteries and in the future may lead to autonomously powered sensors. This work describes a simulation tool that enables the user to predict the battery life of a wireless sensor that utilizes energy harvesting to supplement the battery power. To create this simulator, all aspects of a typical WSN edge device were modelled including, sensors, transceiver, and microcontroller as well as the energy source components (batteries, solar cells, thermoelectric generators (TEG), supercapacitors and DC/DC converters). The tool allows the user to plug and play different pre characterized devices as well as add user-defined devices. The goal of this simulation tool is to predict the lifetime of a device and scope for extension using ambient energy sources.

Keywords: Wireless Sensor Network, IoT, edge device, simulation, solar cells, TEG, supercapacitor, energy harvesting

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Authors: Shoumodip Roy, Ankit Singhania, Santanu Mallick, Abhiram Jha, M. K. Agarwal, R. V. Ramna, Uttam Singh

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The quality of hot metal at any blast furnace is judged by the silicon content in it. Lower hot metal silicon not only enhances process efficiency at steel melting shops but also reduces hot metal costs. The Hot metal produced at G-Blast furnace Tata Steel Jamshedpur has a significantly higher Si content than Benchmark Blast furnaces. The higher content of hot metal Si is mainly due to inferior raw material quality than those used in benchmark blast furnaces. With minimum control over raw material quality, the only option left to control hot metal Si is via optimizing the furnace parameters. Therefore, in order to identify the levers to reduce hot metal Si, Data mining was carried out, and multiple regression models were developed. The statistical analysis revealed that Slag B3{(CaO+MgO)/SiO2}, Slag Alumina and Hot metal temperature are key controllable parameters affecting hot metal silicon. Contour Plots were used to determine the optimum range of levels identified through statistical analysis. A trial plan was formulated to operate relevant parameters, at G blast furnace, in the identified range to reduce hot metal silicon. This paper details out the process followed and subsequent reduction in hot metal silicon by 15% at G blast furnace.

Keywords: blast furnace, optimization, silicon, statistical tools

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Authors: P. Abachi, S. Karami, K. Purazrang

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The amorphous reinforcements (metallic glasses) can be considered as promising options for reinforcing light-weight aluminum and its alloys. By using the proper type of reinforcement, one can overcome to drawbacks such as interfacial de-cohesion and undesirable reactions which can be created at ceramic particle and metallic matrix interface. In this work, the Zr-based amorphous phase was produced via mechanical milling of elemental powders. Based on Miedema semi-empirical Model and diagrams for formation enthalpies and/or Gibbs free energies of Zr-Cu amorphous phase in comparison with the crystalline phase, the glass formability range was predicted. The composite was produced using the powder mixture of the aluminum and metallic glass and spark plasma sintering (SPS) at the temperature slightly above the glass transition Tg of the metallic glass particles. The selected temperature and rapid sintering route were suitable for consolidation of an aluminum matrix without crystallization of amorphous phase. To characterize amorphous phase formation, X-ray diffraction (XRD) phase analyses were performed on powder mixture after specified intervals of milling. The microstructure of the composite was studied by optical and scanning electron microscope (SEM). Uniaxial compression tests were carried out on composite specimens with the dimension of 4 mm long and a cross-section of 2 ˟ 2mm2. The micrographs indicated an appropriate reinforcement distribution in the metallic matrix. The comparison of stress–strain curves of the consolidated composite and the non-reinforced Al matrix alloy in compression showed that the enhancement of yield strength and mechanical strength are combined with an appreciable plastic strain at fracture. It can be concluded that metallic glasses (amorphous phases) are alternative reinforcement material for lightweight metal matrix composites capable of producing high strength and adequate ductility. However, this is in the expense of minor density increase.

Keywords: aluminum matrix composite, amorphous phase, mechanical alloying, spark plasma sintering

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Authors: Yongrong Li, Ralf Domroes

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Due to significant energy savings, enablement of higher machining speed as well as environmentally friendly features, Minimum Quantity Lubrication (MQL) has been used for many machining processes efficiently. However, in the deep hole drilling field (small tool diameter D < 5 mm) and long tool (length L > 25xD) it is always a bottle neck for a single channel MQL system. The single channel MQL, based on the Venturi principle, faces a lack of enough oil quantity caused by dropped pressure difference during the deep hole drilling process. In this paper, a system concept based on a bypass design has explored its possibility to dynamically reach the required pressure difference between the air inlet and the inside of aerosol generator, so that the deep hole drilling demanded volume of oil can be generated and delivered to tool tips. The system concept has been investigated in static and dynamic laboratory testing. In the static test, the oil volume with and without bypass control were measured. This shows an oil quantity increasing potential up to 1000%. A spray pattern test has demonstrated the differences of aerosol particle size, aerosol distribution and reaction time between single channel and bypass controlled single channel MQL systems. A dynamic trial machining test of deep hole drilling (drill tool D=4.5mm, L= 40xD) has been carried out with the proposed system on a difficult machining material AlSi7Mg. The tool wear along a 100 meter drilling was tracked and analyzed. The result shows that the single channel MQL with a bypass control can overcome the limitation and enhance deep hole drilling with a small tool. The optimized combination of inlet air pressure and bypass control results in a high quality oil delivery to tool tips with a uniform and continuous aerosol flow.

Keywords: deep hole drilling, green production, Minimum Quantity Lubrication (MQL), near dry machining

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Authors: Vishal Kumar Singh

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Bioprocess development is a time-constrained activity aimed at harnessing the full potential of culture performance in an ambience that is not natural to cells. Even with the use of chemically defined media and feeds, a significant amount of time is devoted in identifying the apt operating parameters. In addition, the scale-up of these processes is often accompanied by loss of antibody titer and product quality, which further delays the commercialization of the drug product. In such a scenario, the investigation of this disparity of culture performance is done by further experimentation at a smaller scale that is representative of at-scale production bioreactors. These scale-down model developments are also time-intensive. In this study, a computation fluid dynamics-based multi-objective scaling approach has been illustrated to speed up the process transfer. For the implementation of this approach, a transient multiphase water-air system has been studied in Ansys CFX to visualize the air bubble distribution and volumetric mass transfer coefficient (kLa) profiles, followed by the design of experiment based parametric optimization approach to define the operational space. The proposed approach is completely in silico and requires minimum experimentation, thereby rendering a high throughput to the overall process development.

Keywords: bioprocess development, scale up, scale down, computation fluid dynamics, multi-objective, Ansys CFX, design of experiment

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Authors: Fawwaz Eniola Fajingbesi, Nur Shahida Midia, Elsheikh M. A. Elsheikh, Siti Hajar Yusoff

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Empirical analysis of lightning related phenomena in real time is extremely dangerous due to the relatively high electric discharge involved. Hence, design and optimization of efficient grounding systems depending on real time empirical methods are impeded. Using numerical methods, the dynamics of complex systems could be modeled hence solved as sets of linear and non-linear systems . In this work, the induced current distribution as lightning strike traverses the soil have been numerically modeled in a 2D axial-symmetry and solved using finite element method (FEM) in COMSOL Multiphysics 5.2 AC/DC module. Stratified and non- stratified electrode system were considered in the solved model and soil conductivity (σ) varied between 10 – 58 mS/m. The result discussed therein were the electric field distribution, current distribution and soil ionization phenomena. It can be concluded that the electric field and current distribution is influenced by the injected electric potential and the non-linearity in soil conductivity. The result from numerical calculation also agrees with previously laboratory scale empirical results.

Keywords: current distribution, grounding systems, lightning discharge, numerical model, soil conductivity, soil ionization

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Authors: Reyhaneh Soltanshah, Hamid R. Zarandi

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In order to be more efficient in industries that are related to computer systems, software testing is necessary despite spending time and money. In the embedded system software test, complete knowledge of the embedded system architecture is necessary to avoid significant costs and damages. Software tests increase the price of the final product. The aim of this article is to provide a method to reduce time and cost in tests based on program structure. First, a complete review of eleven white box test methods based on ISO/IEC/IEEE 29119 2015 and 2021 versions has been done. The proposed algorithm is designed using two versions of the 29119 standards, and some white-box testing methods that are expensive or have little coverage have been removed. On each of the functions, white box test methods were applied according to the 29119 standard and then the proposed algorithm was implemented on the functions. To speed up the implementation of the proposed method, the Unity framework has been used with some changes. Unity framework can be used in embedded software testing due to its open source and ability to implement white box test methods. The test items obtained from these two approaches were evaluated using a mathematical ratio, which in various software mining reduced between 50% and 80% of the test cost and reached the desired result with the minimum number of test items.

Keywords: embedded software, reduce costs, software testing, white-box testing

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Authors: Abderrahmane El Kachani, El Mahjoub Chakir, Anass Ait Laachir, Abdelhamid Niaaniaa, Jamal Zerouaoui, Tarik Jarou

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This paper presents a wind turbine based on the doubly fed induction generator (DFIG) connected to the utility grid through a shunt active power filter (SAPF). The whole system is controlled by a double nonlinear predictive controller (DNPC). A Taylor series expansion is used to predict the outputs of the system. The control law is calculated by optimization of the cost function. The first nonlinear predictive controller (NPC) is designed to ensure the high performance tracking of the rotor speed and regulate the rotor current of the DFIG, while the second one is designed to control the SAPF in order to compensate the harmonic produces by the three-phase diode bridge supplied by a passive circuit (rd, Ld). As a result, we obtain sinusoidal waveforms of the stator voltage and stator current. The proposed nonlinear predictive controllers (NPCs) are validated via simulation on a 1.5 MW DFIG-based wind turbine connected to an SAPF. The results obtained appear to be satisfactory and promising.

Keywords: wind power, doubly fed induction generator, shunt active power filter, double nonlinear predictive controller

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: V. Antevska, B. Dejanova, L. Todorovska, J. Pluncevic, E. Sivevska, S. Petrovska, S. Mancevska, I. Karagjozova

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Introduction: The aim of this study was to optimise the laboratory oxidative stress (OS) markers in soccer players. Material and methods: In a number of 37 soccer players (21±3 years old) and 25 control subjects (sedenters), plasma samples were taken for d-ROMs (reactive oxygen metabolites) and NO (nitric oxide) determination. The d-ROMs test was performed by measurement of hydroperoxide levels (Diacron, Italy). For NO determination the method of nitrate enzyme reduction with the Greiss reagent was used (OXIS, USA). The parameters were taken after the training of the soccer players and were compared with the control group. Training was considered as maximal exercise treadmill test. The criteria of maximum loading for each subject was established as >95% maximal heart rate. Results: The level of d-ROMs was found to be increased in the soccer players vs. control group but no significant difference was noticed. After the training d-ROMs in soccer players showed increased value of 299±44 UCarr (p<0.05). NO showed increased level in all soccer players vs. controls but significant difference was found after the training 102±29 μmol (p<0.05). Conclusion: Due to these results we may suggest that the measuring these OS markers in sport medicine may be useful for better estimation and evaluation of the training program. More oxidative stress should be used to clarify optimization of the training intensity program.

Keywords: oxidative stress markers, soccer players, training, sport

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Authors: Scott Nielsen, Luca Longanesi, Chris Chuck

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Palm oil is obtained from the flesh and kernel of the fruit of oil palms and is the most productive and inexpensive oil crop. The global demand for palm oil is approximately 75 million metric tonnes, a 29% increase in global production of palm oil since 2016. This expansion of oil palm cultivation has resulted in mass deforestation, vast biodiversity destruction and increasing net greenhouse gas emissions. One possible alternative is to produce a saturated oil, similar to palm, from microbes such as oleaginous yeast. The yeasts can be cultured on sugars derived from second-generation sources and do not compete with tropical forests for land. One highly promising oleaginous yeast for this application is Metschnikowia pulcherrima. However, recent techno-economic modeling has shown that cell lysis and standard lipid extraction are major contributors to the cost of the oil. Typical cell disruption techniques to extract either single cell oils or proteins have been based around bead-beating, homogenization and acid lysis. However, these can have a detrimental effect on lipid quality and are energy-intensive. In this study, a vortex separator, which produces high sheer with minimal energy input, was investigated as a potential low energy method of lysing cells. This was compared to four more traditional methods (thermal lysis, acid lysis, alkaline lysis, and osmotic lysis). For each method, the yeast loading was also examined at 1 g/L, 10 g/L and 100 g/L. The quality of the cell disruption was measured by optical cell density, cell counting and the particle size distribution profile comparison over a 2-hour period. This study demonstrates that the vortex separator is highly effective at lysing the cells and could potentially be used as a simple apparatus for lipid recovery in an oleaginous yeast process. The further development of this technology could potentially reduce the overall cost of microbial lipids in the future.

Keywords: palm oil substitute, metschnikowia pulcherrima, cell disruption, cell lysis

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Kira Toxopeus, Kamran Siddiqui

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Channel flows are common in a wide range of engineering applications. In some types of channel flows, particularly the ones involving chemical or biological processes, the control of the flow temperature is crucial to maintain the optimal conditions for the chemical reaction or to control the growth of biological species. This often becomes an issue when the flow experiences temperature fluctuations due to external conditions. While active heating and cooling could regulate the channel temperature, it may not be feasible logistically or economically and is also regarded as a non-sustainable option. Thermal energy storage utilizing phase change material (PCM) could provide the required thermal regulation sustainably by storing the excess heat from the channel and releasing it back as required, thus regulating the channel temperature within a range in the proximity of the PCM melting temperature. However, in designing such systems, the configuration of the PCM storage within the channel is critical as it could influence the channel flow dynamics, which would, in turn, affect the heat exchange between the channel fluid and the PCM. The present research is focused on the investigation of the flow dynamical behavior in the channel during heat transfer from the channel flow to the PCM thermal energy storage. Offset vertical columns in a narrow channel were used that contained the PCM. Two different column shapes, square and circular, were considered. Water was used as the channel fluid that entered the channel at a temperature higher than that of the PCM melting temperature. Hence, as the water was passing through the channel, the heat was being transferred from the water to the PCM, causing the PCM to store the heat through a phase transition from solid to liquid. Particle image velocimetry (PIV) was used to measure the two-dimensional velocity field of the channel flow as it flows between the PCM columns. Thermocouples were also attached to the PCM columns to measure the PCM temperature at three different heights. Three different water flow rates (0.5, 0.75 and 1.2 liters/min) were considered. At each flow rate, experiments were conducted at three different inlet water temperatures (28ᵒC, 33ᵒC and 38ᵒC). The results show that the flow rate and the inlet temperature influenced the flow behavior inside the channel.

Keywords: channel flow, phase change material, thermal energy storage, thermal regulation

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Authors: Takashi Shimizu, Tomoaki Hashimoto

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A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems.

Keywords: optimal control, nonlinear systems, state estimation, Kalman filter

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Authors: Amitesh Goswami, Vishal Gulati, Annu Yadav

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In this paper, WEDM has been used to investigate the machining characteristics of Inconel-625 alloy. The machining characteristics namely material removal rate (MRR) and surface roughness (SR) have been investigated along with surface microstructure analysis using SEM and EDS of the machined surface. Taguchi’s L27 Orthogonal array design has been used by considering six varying input parameters viz. Pulse-on time (Ton), Pulse-off time (Toff), Spark Gap Set Voltage (SV), Peak Current (IP), Wire Feed (WF) and Wire Tension (WT) for the responses of interest. It has been found out that Pulse-on time (Ton) and Spark Gap Set Voltage (SV) are the most significant parameters affecting material removal rate (MRR) and surface roughness (SR) are. Microstructure analysis of workpiece was also done using Scanning Electron Microscope (SEM). It was observed that, variations in pulse-on time and pulse-off time causes varying discharge energy and as a result of which deep craters / micro cracks and large/ small number of debris were formed. These results were helpful in studying the effects of pulse-on time and pulse-off time on MRR and SR. Energy Dispersive Spectrometry (EDS) was also done to check the compositional analysis of the material and it was observed that Copper and Zinc which were initially not present in the Inconel 625, later migrated on the material surface from the brass wire electrode during machining

Keywords: MRR, SEM, SR, taguchi, Wire Electric Discharge Machining

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Authors: Tahsin Shameem, Chowdhury Sadman Jahan, Mohammad Alam

Abstract:

Interest about Net Zero Energy Buildings have gained traction in recent years following the need to sustain energy consumption with generations on site and to reduce dependence on grid supplied energy from large plants using fossil fuel. With this end in view, building integrated photovoltaics are being studied attempting to utilize all exterior facades of a building to generate power. In this paper, we have looked at the physical parameters defining a dye sensitized solar cell (DSSC) and discussed their impact on energy harvest. Following our discussion and experimental data obtained from literature, we have attempted to optimize these physical parameters accordingly so as to allow maximum light absorption for a given active layer thickness. We then modified a planer DSSC design with our optimized properties to allow adequate light transmission which demonstrated a high fill factor and an External Quantum Efficiency (EQE) of greater than 9% by computer aided design and simulation. In conclusion, a DSSC based solar window with such high output values even after such high light transmission through it definitely flags a promising future for this technology and our work elicits the need for further study and practical experimentation.

Keywords: net zero energy building, integrated photovoltaics, dye sensitized solar cell, fill factor, External Quantum Efficiency

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Authors: Barzin Rajabloo, Martin Desilets

Abstract:

First, an electrochemical model for the reduction of CO₂ into CH₃OH is developed in which mass and charge transfer, reactions at the surface of the electrodes and fluid flow of the electrolyte are considered. This mathematical model is developed in COMSOL Multiphysics® where both secondary and tertiary current distribution interfaces are coupled to consider concentrations and potentials inside different parts of the cell. Constant reaction rates are assumed as the fitted parameters to minimize the error between experimental data and modeling results. The model is validated through a comparison with experimental data in terms of faradaic efficiency for production of CH₃OH, the current density in different applied cathode potentials as well as current density in different electrolyte flow rates. The comparison between model outputs and experimental measurements shows a good agreement. The model indicates the higher hydrogen evolution in comparison with CH₃OH production as well as mass transfer limitation caused by CO₂ concentration, which are consistent with findings in the literature. After validating the model, in the second part of the study, some design parameters of the cell, such as cathode geometry and catholyte/anolyte channel widths, are modified to reach better performance and higher faradaic efficiency of methanol production.

Keywords: carbon dioxide, electrochemical reduction, methanol, modeling

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Authors: Maritz Lourens Van Rensburg, Chantel Niebuhr

Abstract:

Utilizing all available renewable energy sources is an ever-growing necessity, this includes a newfound interest into hydrokinetic energy systems, which open the door to installations where conventional hydropower shows no potential. Optimization and obtaining high efficiencies are key in these installations. In this study a vertical axis Darrieus hydrokinetic turbine is designed and constructed to address certain drawbacks experience by axial flow horizontal axis turbines in an irrigation channel. Many horizontal axis turbines have been well developed and optimized to have high efficiencies but depending on the conditions experienced in an open channel, the performance of these turbines may be adversely affected. The study analyses how the designed vertical axis turbine addresses the problems experienced by a horizontal axis turbine while still achieving a satisfactory efficiency. To be able to optimize the vertical axis turbine, a computational fluid dynamics model was validated to the experimental results obtained from the power generated from a test turbine installation operating at various rotational speeds. It was found that an accurate validated model can be obtained through validation of generated power output.

Keywords: hydrokinetic, Darrieus, computational fluid dynamics, vertical axis turbine

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Authors: Mawloud Mosbah, Bachir Boucheham

Abstract:

We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-Nearest Neighbours Algorithm (KNN) while the rest of the methods is related purely to the information retrieval field and they fall under the purview of the following three major axes: Shifting query, Feature Weighting and the optimization of the parameters of similarity metric. As a contribution, and in addition to the comparative purpose, we propose a new version of the KNN algorithm referred to as an incremental KNN which is distinct from the original version in the sense that besides the influence of the seeds, the rate of the actual target image is influenced also by the images already rated. The results presented here have been obtained after experiments conducted on the Wang database for one iteration and utilizing colour moments on the RGB space. This compact descriptor, Colour Moments, is adequate for the efficiency purposes needed in the case of interactive systems. The results obtained allow us to claim that the proposed algorithm proves good results; it even outperforms a wide range of techniques available in the literature.

Keywords: CBIR, category search, relevance feedback, query point movement, standard Rocchio’s formula, adaptive shifting query, feature weighting, original KNN, incremental KNN

Procedia PDF Downloads 257

Authors: Mohammad Besharatloo

Abstract:

Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

Procedia PDF Downloads 62

Authors: Rabaa Tlili Yaakoubi, Hind Nakouri, Olivier Blanpain

Abstract:

The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of the CARDIO project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 40 to 100%. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 60% of total volume rejected to the natural environment and of 80 % in the number of discharges.

Keywords: RTC, paradigm, optimization, automation

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Authors: Ramzi Suleiman

Abstract:

Recently, the author proposed an observationally-based relativity theory termed information relativity theory (IRT). The theory is simple and is based only on basic principles, with no prior axioms and no free parameters. For the case of a body of mass in uniform rectilinear motion relative to an observer, the theory transformations uncovered a matter-dark matter duality, which prescribes that the sum of the densities of the body's baryonic matter and dark matter, as measured by the observer, is equal to the body's matter density at rest. It was shown that the theory transformations were successful in predicting several important phenomena in small particle physics, quantum physics, and cosmology. This paper extends the theory transformations to the cases of rotating disks and spheres. The resulting transformations for a rotating disk are utilized to derive predictions of the radial distributions of matter and dark matter densities in rotationally supported galaxies based solely on their observed rotation curves. It is also shown that for galaxies with flattening curves, good approximations of the radial distributions of matter and dark matter and of the dark matter fraction could be obtained from one measurable scale radius. Test of the model on five galaxies, chosen randomly from the SPARC database, yielded impressive predictions. The rotation curves of all the investigated galaxies emerged as accurate traces of the predicted radial density distributions of their dark matter. This striking result raises an intriguing physical explanation of gravity in galaxies, according to which it is the proximal drag of the stars and gas in the galaxy by its rotating dark matter web. We conclude by alluding briefly to the application of the proposed model to stellar systems and black holes. This study also hints at the potential of the discovered matter-dark matter duality in fixing the standard model of elementary particles in a natural manner without the need for hypothesizing about supersymmetric particles.

Keywords: dark matter, galaxies rotation curves, SPARC, rotating disk

Procedia PDF Downloads 51