Search results for: weight optimization

Authors: Guozhen Li, Philip Hall, Nick Miles, Tao Wu

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This paper presents a numerical investigation of two horizontally mounted four-lobed swirl pipes in terms of swirl induction effectiveness into flows passing through them. The swirl flows induced by the two swirl pipes have the potential to improve the efficiency of Clean-In-Place procedures in a closed processing system by local intensification of hydrodynamic impact on the internal pipe surface. Pressure losses, swirl development within the two swirl pipe, swirl induction effectiveness, swirl decay and wall shear stress variation downstream of two swirl pipes are analyzed and compared. It was found that a shorter length of swirl inducing pipe used in joint with transition pipes is more effective in swirl induction than when a longer one is used, in that it has a less constraint to the induced swirl and results in slightly higher swirl intensity just downstream of it with the expense of a smaller pressure loss. The wall shear stress downstream of the shorter swirl pipe is also slightly larger than that downstream of the longer swirl pipe due to the slightly higher swirl intensity induced by the shorter swirl pipe. The advantage of the shorter swirl pipe in terms of swirl induction is more significant in flows with a larger Reynolds Number.

Keywords: swirl pipe, swirl effectiveness, CFD, wall shear stress, swirl intensity

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Authors: Herbert W. Corley

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A scalar equilibrium (SE) is an alternative type of equilibrium in pure strategies for an n-person normal-form game G. It is defined using optimization techniques to obtain a pure strategy for each player of G by maximizing an appropriate utility function over the acceptable joint actions. The players’ actions are determined by the choice of the utility function. Such a utility function could be agreed upon by the players or chosen by an arbitrator. An SE is an equilibrium since no players of G can increase the value of this utility function by changing their strategies. SEs are formally defined, and examples are given. In a greedy SE, the goal is to assign actions to the players giving them the largest individual payoffs jointly possible. In a weighted SE, each player is assigned weights modeling the degree to which he helps every player, including himself, achieve as large a payoff as jointly possible. In a compromise SE, each player wants a fair payoff for a reasonable interpretation of fairness. In a parity SE, the players want their payoffs to be as nearly equal as jointly possible. Finally, a satisficing SE achieves a personal target payoff value for each player. The vector payoffs associated with each of these SEs are shown to be Pareto optimal among all such acceptable vectors, as well as computationally tractable.

Keywords: compromise equilibrium, greedy equilibrium, normal-form game, parity equilibrium, pure strategies, satisficing equilibrium, scalar equilibria, utility function, weighted equilibrium

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Authors: Wen Ma, Yong Peng, Zhixiang Li

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Security journey is a vital focus on the field of Rail Transportation. Accidents caused by the damage of the high-speed train windshield have occurred many times and have given rise to terrible consequences. Train windshield consists of tempered glass and polyvinyl butyral (PVB) film. In this work, the quasi-static tests and the split Hopkinson pressure bar (SHPB) tests were carried out first to obtain the mechanical properties and constitutive model for the tempered glass and PVB film. These tests results revealed that stress and Young’s modulus of tempered glass were wake-sensitive to strain rate, but stress and Young’s modulus of PVB film were strong-sensitive to strain rate. Then impact experiment of the windshield was carried out to investigate dynamic response and failure characteristics of train windshield. In addition, a finite element model based on the combined finite element method was proposed to investigate fracture and fragmentation responses of train windshield under different-velocity impact. The results can be used for further design and optimization of the windshield for high-speed train application.

Keywords: constitutive model, impact response, mechanism properties, PVB film, tempered glass

Procedia PDF Downloads 136

Authors: Guebli Abdelkader, Reguieg Madani, Belkadi Adel, Sbaa Bouabdellah

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The identification of mechanical variables in the motor performance trajectory has a prominent role in improving skill performance, error-exceeding, it contributes seriously to solving some problems of learning and training. The study aims to highlight the indicators of motor performance for Paralympic athletes during the practicing sports between modelling and between excellence in motor performance, this by taking into account the distinction of athlete practicing with special behavioral skills for the Paralympic athletes. In the study, we relied on the analysis of some previous research of biomechanical performance indicators during some of the events sports (shooting activities in the Paralympic athletics, shooting skill in the wheelchair basketball). The results of the study highlight the distinction of disabled practitioners of sporting events identified in motor performance during practice, by overcoming some physics indicators in human movement, as a lower center of body weight, increase in offset distance, such resistance which requires them to redouble their efforts. However, the results of the study highlighted the strength of the correlation between biomechanical variables of motor performance and the digital level achievement similar to the other practitioners normal.

Keywords: sports, the disabled, motor performance, Paralympic

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Authors: Jorge R. Santos, Pedro Sebastiao

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In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.

Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js

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Authors: Selena Gutiérrez, Araceli Martínez

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One of the most important challenges worldwide, scientific and technological, is to have a sustainable energy source; friendly to the environment and widely available. Currently, the 85% of the energy used comes from the fossil sources. Another important environmental problem is that several rubber products (tires, gloves, hoses, among others) are discarded practically without any treatment. In nature, the degradation of such products will take at least 500 years. In 2009, the worldwide rubber production was about 23.6 million tons. In order to solve this problems, our research focus in an alternative synthesis of biofuels in a two-step approach: The metathesis degradation of industrial rubber (models of rubber waste), and the oligomers transesterification. Thus, cis-1,4-polybutadiene (Mn= 9.1x105, Mw/Mn= 2.2) and styrene-butadiene block copolymers with 30% (Mn= 1.61x105; Mw/Mn= 1.3) and 21% wt styrene (Mn= 1.92x105; Mw/Mn= 1.4) were degraded via metathesis with soybean oil as chain transfer agent (CTA) and green solvent; using [(PCy3)2Cl2Ru=CHPh] and [(1,3-diphenyl-4,5-dihydroimidazol-2-ylidene)(PCy3)Ru=CHPh] catalysts. Afterwards, the products were transesterified by basic homogeneous catalysis. Before transesterification, the polystyrene microblocks (Mn= 16,761; Mw/Mn= 1.2) were isolated. Finally, the biofuels obtained (BO) were purified, characterized and showed similar properties to standards biodiesel (SB) (Norms: EN 14214-03 and ASTM D6751-02), i.e. (SB / BO): molecular weight [Daltons] (570 / 543-596), density [g/cm3] (0.86-0.90 / 0.88), kinematic viscosity [mm2/s] (1.90-6.0 / 3.5-4.5), iodine (97 / 97-98) and cetane number (Min.47 / 56-58).

Keywords: biofuels, industrial rubber, metathesis, vegetable oils

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Authors: A. Sacko, H. Nyoni, T. A. M. Msagati, B. Ntsendwana

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Carbon based materials to target environmental pollutants have become increasingly recognized in science. Electrochemical methods using carbon based materials are notable methods for high sensitive detection of organic pollutants in air. It is therefore in this light that exfoliated graphite electrode was fabricated for electrochemical analysis of PAHs in urban atmospheric air. The electrochemical properties of the graphite electrode were studied using CV and EIS in the presence of acetate buffer supporting electrolyte with 2 Mm ferricyanide as a redox probe. The graphite electrode showed enhanced current response which confirms facile kinetics and enhanced sensitivity. However, the peak to peak (DE) separation increased as a function of scan rate. The EIS showed a high charger transfer resistance. The detection phenanthrene on the exfoliated graphite was studied in the presence of acetate buffer solution at PH 3.5 using DPV. The oxidation peak of phenanthrene was observed at 0.4 V. Under optimized conditions (supporting electrolyte, pH, deposition time, etc.). The detection limit observed was at 5x 10⁻⁸ M. Thus the results demonstrate with further optimization and modification lower concentration detection can be achieved.

Keywords: electrochemical detection, exfoliated graphite, PAHs (polycyclic aromatic hydrocarbons), urban air

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Authors: Nor Mazlin Zahari, Lian Gan, Xuerui Mao

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The power production in wind farms and the mechanical loads on the turbines are strongly impacted by the wake of the wind turbine. Thus, there is a need for understanding and modelling the turbine wake dynamic in the wind farm and the layout optimization. Having a good wake model is important in predicting plant performance and understanding fatigue loads. In this paper, the Dynamic Mode Decomposition (DMD) was applied to the simulation data generated by a Direct Numerical Simulation (DNS) of flow around a turbine, perturbed by upstream inflow noise. This technique is useful in analyzing the wake flow, to predict its future states and to reflect flow dynamics associated with the coherent structures behind wind turbine wake flow. DMD was employed to describe the dynamic of the flow around turbine from the DNS data. Since the DNS data comes with the unstructured meshes and non-uniform grid, the interpolation of each occurring within each element in the data to obtain an evenly spaced mesh was performed before the DMD was applied. DMD analyses were able to tell us characteristics of the travelling waves behind the turbine, e.g. the dominant helical flow structures and the corresponding frequencies. As the result, the dominant frequency will be detected, and the associated spatial structure will be identified. The dynamic mode which represented the coherent structure will be presented.

Keywords: coherent structure, Direct Numerical Simulation (DNS), dominant frequency, Dynamic Mode Decomposition (DMD)

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Authors: M. Dabirinezhad, M. Bayat Pour, A. Dabirinejad

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In 2020, the technology was introduced to solve some of the deficiencies of direct digital radiology. SDDR is an invention that is capable of capturing dental images without human intervention, and it was invented by the authors of this paper. Adjusting the radiology wave dose is a part of the dentists, radiologists, and dental nurses’ tasks during the radiology photography process. In this paper, an improvement will be added to enable SDDR to set the suitable radiology wave dose according to the density and age of the patients automatically. The separate sensors will be included in the sensors’ package to use the ultrasonic wave to detect the density of the teeth and change the wave dose. It facilitates the process of dental photography in terms of time and enhances the accuracy of choosing the correct wave dose for each patient separately. Since the radiology waves are well known to trigger off other diseases such as cancer, choosing the most suitable wave dose can be helpful to decrease the side effect of that for human health. In other words, it decreases the exposure time for the patients. On the other hand, due to saving time, less energy will be consumed, and saving energy can be beneficial to decrease the environmental impact as well.

Keywords: dental direct digital imaging, environmental impacts, SDDR technology, wave dose

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: M. Mohemmed Sha Mohamed Kunju, Abdalla A. Al-Ameen Abdurahman, T. Manesh Thankappan, A. Mohamed Mustaq Ahmed Hameed

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Web service composition is an effective approach to complete the web based tasks with desired quality. A single web service with limited functionality is inadequate to execute a specific task with series of action. So, it is very much required to combine multiple web services with different functionalities to reach the target. Also, it will become more and more challenging, when these services are from different providers with identical functionalities and varying QoS, so while composing the web services, the overall QoS is considered to be the major factor. Also, it is not true that the expected QoS is always attained when the task is completed. A single web service in the composed chain may affect the overall performance of the task. So care should be taken in different aspects such as functionality of the service, while composition. Dynamic and automatic service composition is one of the main option available. But to achieve the actual functionality of the task, quality of the individual web services are also important. Normally the QoS of the individual service can be evaluated by using the non-functional parameters such as response time, throughput, reliability, availability, etc. At the same time, the QoS is not needed to be at the same level for all the composed services. So this paper proposes a framework that allows composing the services in terms of QoS by setting the appropriate weight to the non-functional parameters of each individual web service involved in the task. Experimental results show that the importance given to the non-functional parameter while composition will definitely improve the performance of the web services.

Keywords: composition, non-functional parameters, quality of service, web service

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Halil Ibrahim Demir, Caner Erden, Abdullah Hulusi Kokcam, Mumtaz Ipek

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Process planning, scheduling, and due date assignment are three important manufacturing functions which are studied independently in literature. There are hundreds of works on IPPS and SWDDA problems but a few works on IPPSDDA problem. Integrating these three functions is very crucial due to the high relationship between them. Since the scheduling problem is in the NP-Hard problem class without any integration, an integrated problem is even harder to solve. This study focuses on the integration of these functions. Sum of weighted tardiness, earliness, and due date related costs are used as a penalty function. Random search and hybrid metaheuristics are used to solve the integrated problem. Marginal improvement in random search is very high in the early iterations and reduces enormously in later iterations. At that point directed search contribute to marginal improvement more than random search. In this study, random and genetic search methods are combined to find better solutions. Results show that overall performance becomes better as the integration level increases.

Keywords: process planning, genetic algorithm, hybrid search, random search, weighted due-date assignment, weighted scheduling

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Authors: Mukesh Kumar, Mahendra Pal Sharma

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Depleting fossil fuel resources coupled with serious environmental degradation has led to the search for alternative resources for biodiesel production as a substitute of Petro-diesel. Currently, edible, non-edible oils and microalgal plant species are cultivated for biodiesel production. Looking at the demerits of edible and non-edible oil resources, the focus is being given to grow microalgal species having high oil productivities, less maturity time and less land requirement. Out of various microalgal species, Chlorella protothecoides is considered as the most promising species for biodiesel production owing to high oil content (58 %), faster growth rate (24–48 h) and high biomass productivity (1214 mg/l/day). The present paper reports the results of optimization of reaction parameters of transesterification process as well as the kinetics of transesterification with 97% yield of biodiesel. The measurement of fuel quality of microalgal biodiesel shows that the biodiesel exhibit very good oxidation stability (O.S) of 7 hrs, more than ASTM D6751 (3 hrs) and EN 14112 (6 hrs) specifications. The CP and PP of 0 and -3 °C are finding as per ASTM D 2500-11 and ASTM D 97-12 standards. These results show that the microalgal biodiesel does not need any enhancement in O.S & CFP and hence can be recommended to be directly used as MB100 or its blends into diesel engine operation. Further, scope is available for the production of binary blends using poor quality biodiesel for engine operation.

Keywords: fuel quality, methyl ester yield, microalgae, transesterification

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Authors: Mohamed M. Emara, Heba Ashraf

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This study is to investigation the effect of silicon carbide (SiC) particle size and thermo-mechanical processing on dimensional stability of aluminum alloy 2124. Three combinations of SiC weight fractions are investigated, 2.5, 5, and 10 wt. % with different SiC particle sizes (25 μm, 5 μm, and 100nm) were produced using mechanical ball mill. The standard testing samples were fabricated using powder metallurgy technique. Both samples, prior and after extrusion, were heated from room temperature up to 400ºC in a dilatometer at different heating rates, that is, 10, 20, and 40ºC/min. The analysis showed that for all materials, there was an increase in length change as temperature increased and the temperature sensitivity of aluminum alloy decreased in the presence of both micro and nano-sized silicon carbide. For all conditions, nanocomposites showed better dimensional stability compared to conventional Al 2124/SiC composites. The after extrusion samples showed better thermal stability and less temperature sensitivity for the aluminum alloy for both micro and nano-sized silicon carbide.

Keywords: aluminum 2124 metal matrix composite, SiC nano-sized reinforcements, powder metallurgy, extrusion mechanical ball mill, dimensional stability

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Authors: Kwame B. O. Amoah

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This paper focuses on retrofitting an old existing office building to a net-zero energy building (NZEB). An existing small office building in Melbourne, Florida, was chosen as a case study to integrate state-of-the-art design strategies and energy-efficient building systems to improve building performance and reduce energy consumption. The study aimed to explore possible ways to maximize energy savings and renewable energy generation sources to cover the building's remaining energy needs necessary to achieve net-zero energy goals. A series of retrofit options were reviewed and adopted with some significant additional decision considerations. Detailed processes and considerations leading to zero energy are well documented in this study, with lessons learned adequately outlined. Based on building energy simulations, multiple design considerations were investigated, such as emerging state-of-the-art technologies, material selection, improvements to the building envelope, optimization of the HVAC, lighting systems, and occupancy loads analysis, as well as the application of renewable energy sources. The comparative analysis of simulation results was used to determine how specific techniques led to energy saving and cost reductions. The research results indicate this small office building can meet net-zero energy use after appropriate design manipulations and renewable energy sources.

Keywords: energy consumption, building energy analysis, energy retrofits, energy-efficiency

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Authors: A. T. Ramachandra Naik, H. Shivananda Murthy, H. n. Anjanayappa

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The freshwater prawn, Macrobrachium rosenbergii is a more popular crustacean cultured widely in monoculture system in India. It has got high nutritional value in the human diet. Hence, understanding its enzymatic and body composition is important in order to judge its flesh quality. Fish oil specially derived from Indian oil sardine is a good source of highly unsaturated fatty acid and lipid source in fish/prawn diet. A 35% crude protein diet with graded levels of Sardine oil as a source of fat was incorporated at four levels viz, 2.07, 4.07, 6.07 and 8.07% maintaining a total lipid level of feed at 8.11, 10.24, 12.28 and 14.33% respectively. Diet without sardine oil (6.05% total lipid) was served as basal treatment. The giant freshwater prawn, Macrobrachium rosenbergii was used as test animal and the experiment was lost for 112 days. Significantly, higher gain in weight of prawn was recorded in the treatment with 6.07% sardine oil incorporation followed by higher specific growth rate, food conversion rate and protein efficiency ratio. The 8.07% sardine oil diet produced the highest RNA: DNA ratio in the prawn muscle. Digestive enzyme analyses in the digestive tract and mid-gut gland showed the greatest activity in prawns fed the 8.07% diet.

Keywords: digestive enzyme, fish diet, Macrobrachium rosenbergii, sardine oil

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Authors: Hee-Jae Suh, Kyung-Su Kim, Min-Ji Kim, Yeon-Seong Jeong, Ok-Hwan Lee, Jae-Wook Shin, Hyang-Sook Chun, Chan Lee

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Canthaxanthin is keto-carotenoid produced from beta-carotene and it has been approved to be used in many countries as a food coloring agent. Canthaxanthin has been analyzed using High Performance Liquid Chromatography (HPLC) system with various ways of pretreatment methods. Four official methods for verification of canthaxanthin at FSA (UK), AOAC (US), EFSA (EU) and MHLW (Japan) were compared to improve its analytical and the pretreatment method. The Linearity, the limit of detection (LOD), the limit of quantification (LOQ), the accuracy, the precision and the recovery ratio were determined from each method with modification in pretreatment method. All HPLC methods exhibited correlation coefficients of calibration curves for canthaxanthin as 0.9999. The analysis methods from FSA, AOAC, and MLHW showed the LOD of 0.395 ppm, 0.105 ppm, and 0.084 ppm, and the LOQ of 1.196 ppm, 0.318 ppm, 0.254 ppm, respectively. Among tested methods, HPLC method of MHLW with modification in pretreatments was finally selected for the analysis of canthaxanthin in lab, because it exhibited the resolution factor of 4.0 and the selectivity of 1.30. This analysis method showed a correlation coefficients value of 0.9999 and the lowest LOD and LOQ. Furthermore, the precision ratio was lower than 1 and the accuracy was almost 100%. The method presented the recovery ratio of 90-110% with modification in pretreatment method. The cross-validation of coefficient variation was 5 or less among tested three institutions in Korea.

Keywords: analytic method, canthaxanthin, food colorants, pretreatment method

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Authors: A. Bahrani

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In order to study the effect of restricted irrigation systems and different potassium fertilizer on water use efficiency and yield of rapeseed (Brassica napus L.), an experiment was conducted in an arid area in Khuzestan, Iran in 2013. The main plots consisted of three irrigation methods: FI (full irrigation), alternate furrow irrigation (AFI) and fixed furrow irrigation (FFI). Each subplot received three rates of K fertiliser application: 0, 150 or 300 kg ha-1. The results showed that the plots receiving the full irrigation resulted in significantly higher grain yields, 1000-kernel weight and grain number per pod than both alternate treatments. However, the highest WUE were obtained in alternate furrow irrigation and 300 kg K ha-1 and the lowest one was found in the FI treatment and 0 kg K ha-1. Potassium application increased RWC in alternate furrow irrigation and fixed furrow irrigation than FI treatment. Maximum oil content was observed in those treatments where full irrigation was applied while minimum oil content was produced in FFI irrigated treatments. Potassium fertilizer also increased grain oil by 15 % than control. Deficit irrigation reduced oleic acid and erucic acid. However, oleic acid and linoleic acid increased with increasing of potassium.

Keywords: erucic acid, irrigation methods, linoleic acid, oil percent, oleic acid

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Authors: Yiğit Gürler, Berkay Türkcan İmrağ, Taylan Güçkıran, İbrahim Şimşek, Alper Taşdemirci

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Paper honeycombs, which is a sandwich structure, consists of two liner faces and one paper honeycomb core. These materials are widely used in the packaging industry due to their low cost, low weight, good energy absorption capabilities and easy recycling properties. However, to provide maximum protection to the products in cases such as the drop of the packaged products, the mechanical behavior of these materials should be well known at the packaging design stage. In this study, the necessary input parameters for the modeling study were obtained by performing compression tests in the through-thickness and in-plane directions of paper-based honeycomb sandwich structures. With the obtained parameters, homogeneous and micro-mechanical numerical models were developed in the Ls-Dyna environment. The material card used for the homogeneous model is MAT_MODIFIED_HONEYCOMB, and the material card used for the micromechanical model is MAT_PIECEWISE_LINEAR_PLASTICITY. As a result, the effectiveness of homogeneous and micromechanical modeling approaches for paper-based honeycomb sandwich structure was investigated using force-displacement curves. Densification points and peak points on these curves will be compared.

Keywords: environmental packaging, mechanical characterization, Ls-Dyna, sandwich structure

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Marek Dlapa

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The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.

Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value

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Authors: Pratham Baheti, Rohan Sanghi, Aditya Gupta

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In the largely populated city of New Delhi, India, there are a lot of people that travel by two-wheelers. Majority of the people wear helmets while traveling and know how important it is to wear helmets for their safety. Still, the number of deaths because of road accidents involving two-wheelers is significant. We had conducted a survey by traveling within and in the outskirts of Delhi so as to see the variation in data and in the opinion of people towards helmet being a safety device rather than to escape the traffic police. We conducted a survey at traffic junctions and crossings of all the stakeholders and collected feedback on the Helmet scenario in India. According to the survey, the possible reason for these deaths is that the people, being unaware of helmet safety standards (ISI standards for helmets), buy helmets with fake ISI mark from unauthorized helmet sellers for a cheap price. Also, for the people who do not wear a helmet at all or wear a helmet just because it is a law, the reasons that they do not want to wear a helmet is heavyweight, lack of ventilation, inconvenience due to a strap, and hair problems. To address all these problems, we are designing a helmet with reduced weight and also working on the Helmet’s retention system and ventilation. We plan to provide this product at a cheap cost whilst maintaining the ISI standards so that a larger section of the population would be able to afford the helmet.

Keywords: safety, survey, ISI marks, stakeholders, helmet

Procedia PDF Downloads 267

Authors: Malith Premarathne, Shehan Bandara, Kaushalya G. Batawala, Thilini U. Ariyadasa

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The rapid depletion of fossil fuel supplies and the emission of carbon dioxide by their continued combustion have paved the way for increased production of carbon-neutral biodiesel from naturally occurring oil sources. The high biomass growth rate and lipid production of microalgae make it a viable source for biodiesel production compared to conventional feedstock. In Sri Lanka, the production of biodiesel by employing indigenous microalgae species is at its emerging stage. This work was an attempt to compare the various pre-treatment methods before extracting lipids such as autoclaving, microwaving and sonication. A mixed culture of microalgae predominantly consisting of Chlorella sp. was obtained from Beire Lake which is an algae rich, organically polluted water body located in Colombo, Sri Lanka. After each pre-treatment method, a standard solvent extraction using Bligh and Dyer’s method was used to compare the total lipid content in percentage dry weight (% dwt). The fatty acid profiles of the oils extracted with each pretreatment method were analyzed using gas chromatography-mass spectrometry (GC-MS). The properties of the biodiesels were predicted by Biodiesel Analyzer© Version 1.1, in order to compare with ASTM 6751-08 biodiesel standard.

Keywords: biodiesel, lipid extraction, microalgae, pre-treatment

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Authors: Ghasem Sharifi, Hamed Shahmohamadi Ousaloo, Milad Azimi, Mehran Mirshams

Abstract:

The ever-increasing use of satellites demands a search for increasingly accurate and reliable pointing systems. Reaction wheels are rotating devices used commonly for the attitude control of the spacecraft since provide a wide range of torque magnitude and high reliability. The numerical modeling of this device can significantly enhance the accuracy of the satellite control in space. Modeling the wheel rotation in the presence of the various frictions is one of the critical parts of this approach. This paper presents a Dynamic Model Control of a Reaction Wheel (DMCR) in the current control mode. In current-mode, the required current is delivered to the coils in order to achieve the desired torque. During this research, all the friction parameters as viscous and coulomb, motor coefficient, resistance and voltage constant are identified. In order to model identification of a reaction wheel, numerous varying current commands apply on the particular wheel to verify the estimated model. All the parameters of DMCR are identified by classical Levenberg-Marquardt (CLM) optimization method. The experimental results demonstrate that the developed model has an appropriate precise and can be used in the satellite control simulation.

Keywords: experimental modeling, friction parameters, model identification, reaction wheel

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Authors: Mohammed Elsayed Lotfy Elsayed Abouzeid, Tomonobu Senjyu

Abstract:

A novel multi-objective park and ride control approach is presented in this research. Park and ride will encourage the owners of the vehicles to leave their cars in the nearest points (on the edges of the crowded cities) and use public transportation facilities (train, bus, metro, or mon-rail) to reach their work inside the crowded city. The proposed control scheme is used to design electric vehicle charging stations (EVCS) to charge 1000 electric vehicles (EV) during their owners' work time. Cairo, Egypt is used as a case study. Photovoltaic (PV) and battery energy storage system (BESS) are used to meet the EVCS demand. Two multi-objective optimization techniques (MOGA and epsilon-MOGA) are utilized to get the optimal sizes of PV and BESS so as to meet the load demand and minimize the total life cycle cost. Detailed analysis and comparison are held to investigate the performance of the proposed control scheme using MATLAB.

Keywords: Battery Energy Storage System, Electric Vehicle, Park and Ride, Photovoltaic, Multi-objective

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Authors: Violeta Damjanovic-Behrendt

Abstract:

This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

Procedia PDF Downloads 344

Authors: Taufik Rizkiandi, Teuku Yuri M. Zagloel, Andri Dwi Setiawan

Abstract:

Distribution and growth in the transportation and warehousing business sector decreased by 15,04%. There was a decrease in Gross Domestic Product (GDP) contribution level from rank 7 of 4,41% in 2019 to 3,81% in rank 8 in 2020. A decline in the transportation and warehousing business sector contributes to GDP, resulting in oil and gas companies implementing an efficient supply chain strategy to ensure the availability of goods, especially lubricants. Fluctuating demand for lubricants and warehouse service time limits are essential things that are taken into account in determining an efficient route. Add depots points as a solution so that demand for lubricants is fulfilled (not stock out). However, adding a depot will increase operating costs and storage costs. Therefore, it is necessary to optimize the addition of depots using the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). This research case study was conducted at an oil and gas company that produces lubricants from 2019 to 2021. The study results obtained the optimal route and the addition of a depot with a minimum additional cost. The total cost remains efficient with the addition of a depot when compared to one depot from Jakarta.

Keywords: CVRPTW, optimal route, depot, tabu search algorithm

Procedia PDF Downloads 127

Authors: R. Pérez Castells, J. M. Carbajo

Abstract:

Sepiolite is used as a processing aid in the manufacture of fibre cement from the start of the replacement of asbestos in the 80s. Sepiolite increases the inter-laminar bond between cement layers and improves homogeneity of the slurries. A new type of sepiolite processed product, Wollatrop TF/C, has been checked as a retention agent for fine particles in the production of fibre cement in a Hatschek machine. The effect of Wollatrop T/FC on filtering and fine particle losses was studied as well as the interaction with anionic polyacrylamide and microsilica. The design of the experiments were factorial and the VDT equipment used for measuring retention and drainage was modified Rapid Köethen laboratory sheet former. Wollatrop TF/C increased the fine particle retention improving the economy of the process and reducing the accumulation of solids in recycled process water. At the same time, drainage time increased sharply at high concentration, however drainage time can be improved by adjusting APAM concentration. Wollatrop TF/C and microsilica are having very small interactions among them. Microsilica does not control fine particle losses while Wollatrop TF/C does efficiently. Further research on APAM type (molecular weight and anionic character) is advisable to improve drainage.

Keywords: drainage, fibre-reinforced cement, fine particle losses, flocculation, microsilica, sepiolite

Procedia PDF Downloads 317

Authors: Gamze Karanfil Celep, Kevser Dincer

Abstract:

The effects of polymer concentration and electrospinning process parameters on the average diameters of electrospun polyacrylonitrile (PAN) nanofibers were experimentally investigated. Besides, mechanical and thermal properties of PAN nanofibers were examined by tensile test and thermogravimetric analysis (TGA), respectively. For this purpose, the polymer concentration, solution feed rate, supply voltage and tip-to-collector distance were determined as the control factors. To succeed these aims, Taguchi’s L16 orthogonal design (4 parameters, 4 level) was employed for the experimental design. Optimal electrospinning conditions were defined using the signal-to-noise (S/N) ratio that was calculated from diameters of the electrospun PAN nanofibers according to "the-smaller-the-better" approachment. In addition, analysis of variance (ANOVA) was evaluated to conclude the statistical significance of the process parameters. The smallest diameter of PAN nanofibers was observed. According to the S/N ratio response results, the most effective parameter on finding out of nanofiber diameter was determined. Finally, the Taguchi design of experiments method has been found to be an effective method to statistically optimize the critical electrospinning parameters used in nanofiber production. After determining the optimum process parameters of nanofiber production, electrical conductivity and fuel cell performance of electrospun PAN nanofibers on the carbon papers will be evaluated.

Keywords: nanofiber, electrospinning, polyacrylonitrile, Taguchi method

Procedia PDF Downloads 194