Search results for: optimization of routes

Authors: Jiaqi Huang, Lu Jin

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Pervious concrete features with high water permeability rate. However, due to the lack of fine aggregates, the compressive strength is usually lower than other conventional concrete products. Optimization of pervious concrete mix design has long been recognized as an effective mechanism to achieve high compressive strength while maintaining desired permeability rate. In this paper, a Gibbs Sampling based algorithm is proposed to approximate the optimal mix design to achieve a high compressive strength of pervious concrete. We prove that the proposed algorithm efficiently converges to the set of global optimal solutions. The convergence rate and accuracy depend on a control parameter employed in the proposed algorithm. The simulation results show that, by using the proposed approach, the system converges to the optimal solution quickly and the derived optimal mix design achieves the maximum compressive strength while maintaining the desired permeability rate.

Keywords: convergence, Gibbs Sampling, high compressive strength, optimal mix design, pervious concrete

Procedia PDF Downloads 181

Authors: Jiin-Yuh Jang, Yu-Feng Gan

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In order to improve the comprehensive mechanical properties of the steel, the cooling rate, and the temperature distribution must be controlled in the cooling process. A three-dimensional numerical model for the prediction of the heat transfer coefficient distribution of H-beam in the controlled cooling process was performed in order to obtain the uniform temperature distribution and minimize the maximum stress and the maximum deformation after the controlled cooling. An algorithm developed with a simplified conjugated-gradient method was used as an optimizer to optimize the heat transfer coefficient distribution. The numerical results showed that, for the case of air cooling 5 seconds followed by water cooling 6 seconds with uniform the heat transfer coefficient, the cooling rate is 15.5 (℃/s), the maximum temperature difference is 85℃, the maximum the stress is 125 MPa, and the maximum deformation is 1.280 mm. After optimize the heat transfer coefficient distribution in control cooling process with the same cooling time, the cooling rate is increased to 20.5 (℃/s), the maximum temperature difference is decreased to 52℃, the maximum stress is decreased to 82MPa and the maximum deformation is decreased to 1.167mm.

Keywords: controlled cooling, H-Beam, optimization, thermal stress

Procedia PDF Downloads 371

Authors: B. Guezzen, M.A. Didi, B. Medjahed

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A factorial design of experiments and a response surface methodology were implemented to investigate the liquid-liquid extraction process of zinc (II) from acetate medium using the 1-Butyl-imidazolium di(2-ethylhexyl) phosphate [BIm⁺][D2EHP^-]. The optimization process of extraction parameters such as the initial pH effect (2.5, 4.5, and 6.6), ionic liquid concentration (1, 5.5, and 10 mM) and salt effect (0.01, 5, and 10 mM) was carried out using a three-level full factorial design (3³). The results of the factorial design demonstrate that all these factors are statistically significant, including the square effects of pH and ionic liquid concentration. The results showed that the order of significance: IL concentration > salt effect > initial pH. Analysis of variance (ANOVA) showing high coefficient of determination (R² = 0.91) and low probability values (P < 0.05) signifies the validity of the predicted second-order quadratic model for Zn (II) extraction. The optimum conditions for the extraction of zinc (II) at the constant temperature (20 °C), initial Zn (II) concentration (1mM) and A/O ratio of unity were: initial pH (4.8), extractant concentration (9.9 mM), and NaCl concentration (8.2 mM). At the optimized condition, the metal ion could be quantitatively extracted.

Keywords: ionic liquid, response surface methodology, solvent extraction, zinc acetate

Procedia PDF Downloads 374

Authors: Jose Daniel Hoyos Giraldo, Jesus Hernan Jimenez Giraldo, Juan Pablo Alvarado Perilla

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Range and endurance are the main limitations of electric aircraft due to the nature of its source of power. The improvement of efficiency on this kind of systems is extremely meaningful to encourage the aircraft operation with less environmental impact. The propeller efficiency highly affects the overall efficiency of the propulsion system; hence its optimization can have an outstanding effect on the aircraft performance. An optimization method is applied to an aircraft propeller in order to maximize its range and endurance by estimating the best combination of geometrical parameters such as diameter and airfoil, chord and pitch distribution for a specific aircraft design at a certain cruise speed, then the rotational speed at which the propeller operates at minimum current consumption is estimated. The optimization is based on the Blade Element Momentum (BEM) method, additionally corrected to account for tip and hub losses, Mach number and rotational effects; furthermore an airfoil lift and drag coefficients approximation is implemented from Computational Fluid Dynamics (CFD) simulations supported by preliminary studies of grid independence and suitability of different turbulence models, to feed the BEM method, with the aim of achieve more reliable results. Additionally, Vortex Theory is employed to find the optimum pitch and chord distribution to achieve a minimum induced loss propeller design. Moreover, the optimization takes into account the well-known brushless motor model, thrust constraints for take-off runway limitations, maximum allowable propeller diameter due to aircraft height and maximum motor power. The BEM-CFD method is validated by comparing its predictions for a known APC propeller with both available experimental tests and APC reported performance curves which are based on Vortex Theory fed with the NASA Transonic Airfoil code, showing a adequate fitting with experimental data even more than reported APC data. Optimal propeller predictions are validated by wind tunnel tests, CFD propeller simulations and a study of how the propeller will perform if it replaces the one of on known aircraft. Some tendency charts relating a wide range of parameters such as diameter, voltage, pitch, rotational speed, current, propeller and electric efficiencies are obtained and discussed. The implementation of CFD tools shows an improvement in the accuracy of BEM predictions. Results also showed how a propeller has higher efficiency peaks when it operates at high rotational speed due to the higher Reynolds at which airfoils present lower drag. On the other hand, the behavior of the current consumption related to the propulsive efficiency shows counterintuitive results, the best range and endurance is not necessary achieved in an efficiency peak.

Keywords: BEM, blade design, CFD, electric aircraft, endurance, optimization, range

Procedia PDF Downloads 108

Authors: M. A. Badr, M. N. El Kordy, A. N. Mohib, M. M. Ibrahim

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The basic objective of the research is to study the effect of hybrid wind energy on the cost of generated electricity considering the cost of reduction CO₂ emissions. The system consists of small wind turbine(s), storage battery bank and a diesel generator (W/D/B). Using an optimization software package, different system configurations are investigated to reach optimum configuration based on the net present cost (NPC) and cost of energy (COE) as economic optimization criteria. The cost of avoided CO₂ is taken into consideration. The system is intended to supply the electrical load of a small community (gathering six families) in a remote Egyptian area. The investigated system is not connected to the electricity grid and may replace an existing conventional diesel powered electric supply system to reduce fuel consumption and CO₂ emissions. The simulation results showed that W/D energy system is more economic than diesel alone. The estimated COE is 0.308$/kWh and extracting the cost of avoided CO₂, the COE reached 0.226 $/kWh which is an external benefit of wind turbine, as there are no pollutant emissions through operational phase.

Keywords: hybrid wind turbine systems, remote areas electrification, simulation of hybrid energy systems, techno-economic study

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Authors: Shadab Shishegar, Sophie Duchesne, Genevieve Pelletier

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Sustainability as one of the key elements of Smart cities, can be realized by employing Real Time Control Strategies for city’s infrastructures. Nowadays Stormwater management systems play an important role in mitigating the impacts of urbanization on natural hydrological cycle. These systems can be managed in such a way that they meet the smart cities standards. In fact, there is a huge potential for sustainable management of urban stormwater and also its adaptability to global challenges like climate change. Hence, a dynamically managed system that can adapt itself to instability of the environmental conditions is desirable. A Global Predictive Real Time Control approach is proposed in this paper to optimize the performance of stormwater management basins in terms of flooding prevention. To do so, a mathematical optimization model is developed then solved using Genetic Algorithm (GA). Results show an improved performance at system-level for the stormwater basins in comparison to static strategy.

Keywords: environmental sustainability, optimization, real time control, storm water management

Procedia PDF Downloads 177

Authors: Vallisree Sivathanu, Kumaraswamidhas Lakshmi Annamalai, Trupti Ranjan Lenka

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We report a tandem solar cell model with Silicon as the bottom cell absorber material and Cu₂BaSn(S, Se)₄(CBTSSe) as absorber material for the top cell. As a first step, the top and bottom cells were modelled and validated by comparison with the experiment. Once the individual cells are validated, then the tandem structure is modelled with Indium Tin Oxide(ITO) as conducting layer between the top and bottom cells. The tandem structure yielded better open circuit voltage and fill factor; however, the efficiency obtained is 7.01%. The top cell and the bottom cells are investigated with the help of electron-hole current density, photogeneration rate, and external quantum efficiency profiles. In order to minimize the various loss mechanisms in the tandem solar cell, the material parameters are optimized within experimentally achievable limits. Initially, the top cell optimization was carried out; then, the bottom cell is optimized for maximizing the light absorption, and upon minimizing the current and photon losses in the tandem structure, the maximum achievable efficiency is predicted to be 19.52%.

Keywords: CBTSSe, silicon, tandem, solar cell, device modeling, current losses, photon losses

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Authors: Nina Philipova

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The influence of the geometric parameters of trapezoidal labyrinth channel on the pressure losses along the labyrinth length is investigated in this work. The impact of the dentate height is studied at fixed values of the dentate angle and the dentate spacing. The objective of the work presented in this paper is to derive a mathematical model of the pressure losses along the labyrinth length depending on the dentate height. The numerical simulations of the water flow movement are performed by using Commercial codes ANSYS GAMBIT and FLUENT. Dripper inlet pressure is set up to be 1 bar. As a result, the mathematical model of the pressure losses is determined as a second-order polynomial by means Commercial code STATISTIKA. Bi-objective optimization is performed by using the mean algebraic function of utility. The optimum value of the dentate height is defined at fixed values of the dentate angle and the dentate spacing. The derived model of the pressure losses and the optimum value of the dentate height are used as a basis for a more successful emitter design.

Keywords: drip irrigation, labyrinth channel hydrodynamics, numerical simulations, Reynolds stress model

Procedia PDF Downloads 154

Authors: Aleksander Ejsmont, Stefan Wuttke, Joanna Goscianska

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Gaining control over particle shape, size and crystallinity is an ongoing challenge for many materials. Especially metalorganic frameworks (MOFs) are recently widely studied. Besides their remarkable porosity and interesting topologies, morphology has proven to be a significant feature. It can affect the further material application. Thus seeking new approaches that enable MOF morphology modulation is important. MOFs are reticular structures, where building blocks are made up of organic linkers and metallic nodes. The most common strategy of ensuring metal source is using salts, which usually exhibit high solubility and hinder morphology control. However, there has been a growing interest in using metal oxides as structure-directing agents towards MOFs due to their very low solubility and shape preservation. Metal oxides can be treated as a metal reservoir during MOF synthesis. Up to now, reports in which receiving MOFs from metal oxides mostly present ZnO conversion to ZIF-8. However, there are other oxides, for instance, Co₃O₄, which often is overlooked due to their structural stability and insolubility in aqueous solutions. Cobalt-based materials are famed for catalytic activity. Therefore the development of their efficient synthesis is worth attention. In the presented work, an optimized Co₃O₄transition to Co-MOFviaa solvothermal approach was proposed. The starting point of the research was the synthesis of Co₃O₄ flower petals and needles under hydrothermal conditions using different cobalt salts (e.g., cobalt(II) chloride and cobalt(II) nitrate), in the presence of urea, and hexadecyltrimethylammonium bromide (CTAB) surfactant as a capping agent. After receiving cobalt hydroxide, the calcination process was performed at various temperatures (300–500 °C). Then cobalt oxides as a source of cobalt cations were subjected to reaction with trimesic acid in solvothermal environment and temperature of 120 °C leading to Co-MOF fabrication. The solution maintained in the system was a mixture of water, dimethylformamide, and ethanol, with the addition of strong acids (HF and HNO₃). To establish how solvents affect metal oxide conversion, several different solvent ratios were also applied. The materials received were characterized with analytical techniques, including X-ray powder diffraction, energy dispersive spectroscopy,low-temperature nitrogen adsorption/desorption, scanning, and transmission electron microscopy. It was confirmed that the synthetic routes have led to the formation of Co₃O₄ and Co-based MOF varied in shape and size of particles. The diffractograms showed receiving crystalline phase for Co₃O₄, and also for Co-MOF. The Co₃O₄ obtained from nitrates and with using low-temperature calcination resulted in smaller particles. The study indicated that cobalt oxide particles of different size influence the efficiency of conversion and morphology of Co-MOF. The highest conversion was achieved using metal oxides with small crystallites.

Keywords: Co-MOF, solvothermal synthesis, morphology control, core-shell

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Authors: Nader A. Al Theeb, Zaid Abu Manneh, Ibrahim Al-Qadi

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Traveling salesman problem (TSP) is a combinatorial integer optimization problem that asks "What is the optimal route for a vehicle to traverse in order to deliver requests to a given set of customers?”. It is widely used by the package carrier companies’ distribution centers. The main goal of applying the TSP in courier organizations is to minimize the time that it takes for the courier in each trip to deliver or pick up the shipments during a day. In this article, an optimization model is constructed to create a new TSP variant to optimize the routing in a courier organization with a consideration of congestion in Amman, the capital of Jordan. Real data were collected by different methods and analyzed. Then, concert technology - CPLEX was used to solve the proposed model for some random generated data instances and for the real collected data. At the end, results have shown a great improvement in time compared with the current trip times, and an economic study was conducted afterwards to figure out the impact of using such models.

Keywords: travel salesman problem, congestions, pick-up, integer programming, package carriers, service engineering

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Authors: Yingjie Zhang, Ming Li, Ying Zhang, Jing Zhang, Zuolei Hu

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In the actual start-up process of DC motors, the DC drive system often faces a conflict between energy consumption and acceleration performance. To resolve the conflict, this paper proposes a comprehensive performance index that energy consumption index is added on the basis of classical control performance index in the DC motor starting process. Taking the comprehensive performance index as the cost function, particle swarm optimization algorithm is designed to optimize the comprehensive performance. Then it conducts simulations on the optimization of the comprehensive performance of the DC motor on condition that the weight coefficient of the energy consumption index should be properly designed. The simulation results show that as the weight of energy consumption increased, the energy efficiency was significantly improved at the expense of a slight sacrifice of fastness indicators with the comprehensive performance index method. The energy efficiency was increased from 63.18% to 68.48% and the response time reduced from 0.2875s to 0.1736s simultaneously compared with traditional proportion integrals differential controller in energy saving.

Keywords: comprehensive performance index, energy consumption, acceleration performance, particle swarm optimal control

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Authors: Sutapa Mondal Roy, Suban K. Sahoo

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The primary aim of this work is to synthesis silver nanoparticles (AgNPs) through environmentally benign routes to avoid any chemical toxicity related undesired side effects. The nanoparticles were stabilized with drug ciprofloxacin (Cp) and were studied for their effectiveness as drug delivery agent. Targeted drug delivery improves the therapeutic potential of drugs at the diseased site as well as lowers the overall dose and undesired side effects. The small size of nanoparticles greatly facilitates the transport of active agents (drugs) across biological membranes and allows them to pass through the smallest capillaries in the body that are 5-6 μm in diameter, and can minimize possible undesired side effects. AgNPs are non-toxic, inert, stable, and has a high binding capacity and thus can be considered as biomaterials. AgNPs were synthesized from the nutrient broth supernatant after the culture of environment-friendly bacteria Bacillus subtilis. The AgNPs were found to show the surface plasmon resonance (SPR) band at 425 nm. The Cp capped Ag nanoparticles formation was complete within 30 minutes, which was confirmed from absorbance spectroscopy. Physico-chemical nature of the AgNPs-Cp system was confirmed by Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM) etc. The AgNPs-Cp system size was found to be in the range of 30-40 nm. To monitor the kinetics of drug release from the surface of nanoparticles, the release of Cp was carried out by careful dialysis keeping AgNPs-Cp system inside the dialysis bag at pH 7.4 over time. The drug release was almost complete after 30 hrs. During the drug delivery process, to understand the AgNPs-Cp system in a better way, the sincere theoretical investigation is been performed employing Density Functional Theory. Electronic charge transfer, electron density, binding energy as well as thermodynamic properties like enthalpy, entropy, Gibbs free energy etc. has been predicted. The electronic and thermodynamic properties, governed by the AgNPs-Cp interactions, indicate that the formation of AgNPs-Cp system is exothermic i.e. thermodynamically favorable process. The binding energy and charge transfer analysis implies the optimum stability of the AgNPs-Cp system. Thus, the synthesized Cp-Ag nanoparticles can be effectively used for biological purposes due to its environmentally benign routes of synthesis procedures, which is clean, biocompatible, non-toxic, safe, cost-effective, sustainable and eco-friendly. The Cp-AgNPs as biomaterials can be successfully used for drug delivery procedures due to slow release of drug from nanoparticles over a considerable period of time. The kinetics of the drug release show that this drug-nanoparticle assembly can be effectively used as potential tools for therapeutic applications. The ease of synthetic procedure, lack of possible chemical toxicity and their biological activity along with excellent application as drug delivery agent will open up vista of using nanoparticles as effective and successful drug delivery agent to be used in modern days.

Keywords: silver nanoparticles, ciprofloxacin, density functional theory, drug delivery

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Authors: Emre Ipekci Cetin, Ebru Tarcan Icigen

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Personnel selection process is considered as one of the most important and most difficult issues in human resources management. At the stage of personnel selection, the applicants are handled according to certain criteria, the candidates are dealt with, and efforts are made to select the most appropriate candidate. However, this process can be more complicated in terms of the managers who will carry out the staff selection process. Candidates should be evaluated according to different criteria such as work experience, education, foreign language level etc. It is crucial that a rational selection process is carried out by considering all the criteria in an integrated structure. In this study, the problem of choosing the front office manager of a 5 star accommodation enterprise operating in Antalya is addressed by using multi-criteria decision-making methods. In this context, SWARA (Step-wise weight assessment ratio analysis) and MOORA (Multi-Objective Optimization on the basis of ratio analysis) methods, which have relatively few applications when compared with other methods, have been used together. Firstly SWARA method was used to calculate the weights of the criteria and subcriteria that were determined by the business. After the weights of the criteria were obtained, the MOORA method was used to rank the candidates using the ratio system and the reference point approach. Recruitment processes differ from sector to sector, from operation to operation. There are a number of criteria that must be taken into consideration by businesses in accordance with the structure of each sector. It is of utmost importance that all candidates are evaluated objectively in the framework of these criteria, after these criteria have been carefully selected in the selection of suitable candidates for employment. In the study, staff selection process was handled by using SWARA and MOORA methods together.

Keywords: accommodation establishments, human resource management, multi-objective optimization on the basis of ratio analysis, multi-criteria decision making, step-wise weight assessment ratio analysis

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Authors: Emile Niringiyimana, LI Ji Qing, Giovanni Dushimimana, Virginie Umwere

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The development and utilization of renewable energy (RE) can not only effectively reduce carbon dioxide (CO2) emissions, but also became a solution to electricity shortage mitigation in rural areas. Hybrid RE systems are promising ways to provide consistent and continuous power for isolated areas. This work investigated the prospect and cost effectiveness of hybrid system complementarity between a 100kW solar PV system and a small-scale 200kW hydropower station in the South of Rwanda. In order to establish the optimal size of a RE system with adequate sizing of system components, electricity demand, solar radiation, hydrology, climate data are utilized as system input. The average daily solar radiation in Rukarara is 5.6 kWh/m2 and average wind speed is 3.5 m/s. The ideal integrated RE system, according to Homer optimization, consists of 91.21kW PV, 146kW hydropower, 12 x 24V li-ion batteries with a 20kW converter. The method of enhancing such hybrid systems control, sizing and choice of components is to reduce the Net present cost (NPC) of the system, unmet load, the cost of energy and reduction of CO2. The power consumption varies according to dominant source of energy in the system by controlling the energy compensation depending on the generation capacity of each power source. The initial investment of the RE system is $977,689.25, and its operation and maintenance expenses is $142,769.39 over a 25-year period. Although the investment is very high, the targeted profits in future are huge, taking into consideration of high investment in rural electrification structure implementations, tied with an increase of electricity cost and the 5 years payback period. The study outcomes suggest that the standalone hybrid PV-Hydropower system is feasible with zero pollution in Rukara community.

Keywords: HOMER optimization, hybrid power system, renewable energy, NPC and solar pv systems

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Moheb R. Girgis, Tarek M. Mahmoud, Bahgat A. Abdullatif, Ahmed M. Rabie

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Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. We usually need multiple gateways, which takes time and costs a lot of money set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. In this paper, we concentrate on this challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

Keywords: wireless mesh networks, genetic algorithms, simulated annealing, topology design

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

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Authors: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

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Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

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Authors: Changshan Zhang, Zhijin Yu, Shixing Fan

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A comprehensive hazard evaluation for coal self-heating in composite long-wall gobs is heavily dependent on computational simulation. In this study, the spatial distributions of cracks which caused significant air leakage were simulated by universal distinct element code (UDEC) simulation. Based on the main routes of air leakage and characteristics of coal self-heating, a computational fluid dynamics (CFD) modeling was conducted to model the coal spontaneous combustion dangerous area in composite long-wall gobs. The results included the oxygen concentration distributions and temperature profiles showed that the numerical approach is validated by comparison with the test data. Furthermore, under the conditions of specific engineering, the major locations where some techniques for extinguishing and preventing long-wall gob fires need to be put into practice were also examined.

Keywords: computational simulation, UDEC simulation, coal self-heating, CFD modeling, long-wall gobs

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Authors: Choonoh Lee, Seyeon Park, Dongyun Kang, Jaehyeong Choi, Soojee Kim, Younggeun Kim

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Market Kurly is the first South Korean online grocery retailer that guarantees same-day, overnight shipping. More than 1.6 million customers place an average of 4.7 million orders and add 3 to 14 products into a cart per month. The company has sold almost 30,000 kinds of various products in the past 6 months, including food items, cosmetics, kitchenware, toys for kids/pets, and even flowers. The company is operating and expanding multiple dry, cold, and frozen fulfillment centers in order to store and ship these products. Due to the scale and complexity of the fulfillment, pick-pack-ship processes are planned and operated in batches, and thus, the planning that decides the batch of the customers’ orders is a critical factor in overall productivity. This paper introduces a metaheuristic optimization method that reduces the complexity of batch processing in a fulfillment center. The method is an iterative genetic algorithm with heuristic creation and evolution strategies; it aims to group similar orders into pick-pack-ship batches to minimize the total number of distinct products. With a well-designed approach to create initial genes, the method produces streamlined plans, up to 13.5% less complex than the actual plans carried out in the company’s fulfillment centers in the previous months. Furthermore, our digital-twin simulations show that the optimized plans can reduce 3% of operation time for packing, which is the most complex and time-consuming task in the process. The optimization method implements a multithreading design on the Spring framework to support the company’s warehouse management systems in near real-time, finding a solution for 4,000 orders within 5 to 7 seconds on an AWS c5.2xlarge instance.

Keywords: fulfillment planning, genetic algorithm, online grocery retail, optimization

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Authors: Kamna Solanki, Yudhvir Singh, Sandeep Dalal

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Software testing is a time and cost intensive process. A scrutiny of the code and rigorous testing is required to identify and rectify the putative bugs. The process of bug identification and its consequent correction is continuous in nature and often some of the bugs are removed after the software has been launched in the market. This process of code validation of the altered software during the maintenance phase is termed as Regression testing. Regression testing ubiquitously considers resource constraints; therefore, the deduction of an appropriate set of test cases, from the ensemble of the entire gamut of test cases, is a critical issue for regression test planning. This paper presents a novel method for designing a suitable prioritization process to optimize fault detection rate and performance of regression test on predefined constraints. The proposed method for test case prioritization m-ACO alters the food source selection criteria of natural ants and is basically a modified version of Ant Colony Optimization (ACO). The proposed m-ACO approach has been coded in 'Perl' language and results are validated using three examples by computation of Average Percentage of Faults Detected (APFD) metric.

Keywords: regression testing, software testing, test case prioritization, test suite optimization

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Authors: Tamas Hartvanyi, Zoltan Andras Nagy, Miklos Szabo

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The dynamic and static capacity are two opposing aspect of warehouse design. Static capacity optimization aims to maximize the space-usage for goods storing, while dynamic capacity needs more free place to handling them. They are opposing by the building structure and the area utilization. According to Pareto principle: the 80% of the goods are the 20% of the variety. From the origin of this statement, it worth to store the big amount of same products by fulfill the space with minimal corridors, meanwhile the rest 20% of goods have the 80% variety of the whole range, so there is more important to be fast-reachable instead of the space utilizing, what makes the space fulfillment numbers worse. The warehouse design decisions made in present practice by intuitive and empiric impressions, the planning method is formed to one selected technology, making this way the structure of the warehouse homogeny. Of course the result can’t be optimal for the inhomogeneous demands. A new innovative model based on our research will be introduced in this paper to describe the technic capacities, what makes possible to define optimal cluster of technology. It is able to optimize the space fulfillment and the dynamic operation together with this cluster application.

Keywords: warehouse, warehouse capacity, warehouse design method, warehouse optimization

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Authors: Ndana Mohammed, Abdullahi Musa Sabo

Abstract:

The oils used in this work were extracted from seeds of Ricinuscommunis, Heaveabrasiliensis, Gossypiumhirsutum, Azadirachtaindica, Glycin max and Jatrophacurcasby solvent extraction method using n-hexane, and gave the yield of 48.00±0.00%, 44.30±0.52%, 45.50±0.64%, 47.60±0.51%, 41.50±0.32% and 46.50±0.71% respectively. However these feed stocks are highly challenging to trans-esterification reaction because they were found to contain high amount of free fatty acids (FFA) (6.37±0.18, 17.20±0.00, 6.14±0.05, 8.60±0.14, 5.35±0.07, 4.24±0.02mgKOH/g) in order of the above. As a result, two-stage trans-esterification reactions process was used to produce biodiesel; Acid esterification was used to reduce high FFA to 1% or less, and the second stage involve the alkaline trans-esterification/optimization of process condition to obtain high yield quality biodiesel. The salient features of this study include; characterization of oils using AOAC, AOCS standard methods to reveal some properties that may determine the viability of sample seeds as potential feed stocks for biodiesel production, such as acid value, saponification value, Peroxide value, Iodine value, Specific gravity, Kinematic viscosity, and free fatty acid profile. The optimization of process parameters in biodiesel production was investigated. Different concentrations of alkaline catalyst (KOH) (0.25, 0.5, 0.75, 1.0 and 1.50w/v, methanol/oil molar ratio (3:1, 6:1, 9:1, 12:1, and 15:1), reaction temperature (500 C, 550 C, 600 C, 650 C, 700 C), and the rate of stirring (150 rpm,225 rpm,300 rpm and 375 rpm) were used for the determination of optimal condition at which maximum yield of biodiesel would be obtained. However, while optimizing one parameter other parameters were kept fixed. The result shows the optimal biodiesel yield at a catalyst concentration of 1%, methanol/oil molar ratio of 6:1, except oil from ricinuscommunis which was obtained at 9:1, the reaction temperature of 650 C was observed for all samples, similarly the stirring rate of 300 rpm was also observed for all samples except oil from ricinuscommunis which was observed at 375 rpm. The properties of biodiesel fuel were evaluated and the result obtained conformed favorably to ASTM and EN standard specifications for fossil diesel and biodiesel. Therefore biodiesel fuel produced can be used as substitute for fossil diesel. The work also reports the result of the study on the evaluation of the effect of the biodiesel storage on its physicochemical properties to ascertain the level of deterioration with time. The values obtained for the entire samples are completely out of standard specification for biodiesel before the end of the twelve months test period, and are clearly degraded. This suggests the biodiesels from oils of Ricinuscommunis, Heaveabrasiliensis, Gossypiumhirsutum, Azadirachtaindica, Glycin max and Jatrophacurcascannot be stored beyond twelve months.

Keywords: biodiesel, characterization, esterification, optimization, transesterification

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Authors: Ghazwan Al-Haji

Abstract:

Most accidents occur in urban areas, and the most related casualties are vulnerable road users (pedestrians and cyclists). The traffic calming measures (TCMs) are widely used and considered to be successful in reducing speed and traffic volume. However, TCMs create unwanted effects include: noise, emissions, energy consumption, vehicle delays and emergency response time (ERT). Different vertical and horizontal TCMs have been already applied nationally (Sweden) and internationally with different impacts. It is a big challenge among traffic engineers, planners, and policy-makers to choose and priorities the best TCMs to be implemented. This study will assess the existing guidelines for TCMs in relation to safety and ERT with focus on data from Norrkoping city in Sweden. The expected results will save lives, time, and money on particularly Swedish Roads. The study will also review newly technologies and how they can improve safety and reduce ERT.

Keywords: traffic calming measures, traffic safety, delay time, vulnerable road users

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Authors: N. Santatriniaina, J. Deseure, T. Q. Nguyen, H. Fontaine, C. Beitia, L. Rakotomanana

Abstract:

Nowadays, with the increasing of the wafer's size and the decreasing of critical size of integrated circuit manufacturing in modern high-tech, microelectronics industry needs a maximum attention to challenge the contamination control. The move to 300 mm is accompanied by the use of Front Opening Unified Pods for wafer and his storage. In these pods an airborne cross contamination may occur between wafers and the pods. A predictive approach using modeling and computational methods is very powerful method to understand and qualify the AMCs cross contamination processes. This work investigates the required numerical tools which are employed in order to study the AMCs cross-contamination transfer phenomena between wafers and FOUPs. Numerical optimization and finite element formulation in transient analysis were established. Analytical solution of one dimensional problem was developed and the calibration process of physical constants was performed. The least square distance between the model (analytical 1D solution) and the experimental data are minimized. The behavior of the AMCs intransient analysis was determined. The model framework preserves the classical forms of the diffusion and convection-diffusion equations and yields to consistent form of the Fick's law. The adsorption process and the surface roughness effect were also traduced as a boundary condition using the switch condition Dirichlet to Neumann and the interface condition. The methodology is applied, first using the optimization methods with analytical solution to define physical constants, and second using finite element method including adsorption kinetic and the switch of Dirichlet to Neumann condition.

Keywords: AMCs, FOUP, cross-contamination, adsorption, diffusion, numerical analysis, wafers, Dirichlet to Neumann, finite elements methods, Fick’s law, optimization

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Authors: Silas A. Ihedioha

Abstract:

In this work; it is considered that an investor’s portfolio is comprised of two assets; a risky stock which price process is driven by the geometric Brownian motion and a risk-free asset with Ornstein-Uhlenbeck Stochastic interest rate of return, where consumption, taxes, transaction costs and dividends are involved. This paper aimed at the optimization of the investor’s expected utility of consumption and terminal return on his investment at the terminal time having power utility preference. Using dynamic optimization procedure of maximum principle, a second order nonlinear partial differential equation (PDE) (the Hamilton-Jacobi-Bellman equation HJB) was obtained from which an ordinary differential equation (ODE) obtained via elimination of variables. The solution to the ODE gave the closed form solution of the investor’s problem. It was found the optimal investment in the risky asset is horizon dependent and a ratio of the total amount available for investment and the relative risk aversion coefficient.

Keywords: optimal, investment, Ornstein-Uhlenbeck, utility maximization, stochastic interest rate, maximum principle

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Authors: Yong Min You

Abstract:

The study on the torque ripple of Permanent Magnet Synchronous Motors (PMSMs) has been rapidly progressed, which effects on the noise and vibration of the electric vehicle. There are several ways to reduce torque ripple, which are the increase in the number of slots and poles, the notch of the rotor and stator teeth, and the skew of the rotor and stator. However, the conventional methods have the disadvantage in terms of material cost and productivity. The demagnetization characteristic of PMSMs must be attained for electric vehicle application. Due to rare earth supply issue, the demand for Dy-free permanent magnet has been increasing, which can be applied to PMSMs for the electric vehicle. Dy-free permanent magnet has lower the coercivity; the demagnetization characteristic has become more significant. To improve the torque ripple as well as the demagnetization characteristics, which are significant parameters for electric vehicle application, an unequal air-gap model is proposed for a PMSM. A shape optimization is performed to optimize the design variables of an unequal air-gap model. Optimal design variables are the shape of an unequal air-gap and the angle between V-shape magnets. An optimization process is performed by Latin Hypercube Sampling (LHS), Kriging Method, and Genetic Algorithm (GA). Finite element analysis (FEA) is also utilized to analyze the torque and demagnetization characteristics. The torque ripple and the demagnetization temperature of the initial model of 45kW PMSM with unequal air-gap are 10 % and 146.8 degrees, respectively, which are reaching a critical level for electric vehicle application. Therefore, the unequal air-gap model is proposed, and then an optimization process is conducted. Compared to the initial model, the torque ripple of the optimized unequal air-gap model was reduced by 7.7 %. In addition, the demagnetization temperature of the optimized model was also increased by 1.8 % while maintaining the efficiency. From these results, a shape optimized unequal air-gap PMSM has shown the usefulness of an improvement in the torque ripple and demagnetization temperature for the electric vehicle.

Keywords: permanent magnet synchronous motor, optimal design, finite element method, torque ripple

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Authors: B. J. Sonani, J. K. Ratnadhariya, Srinivas Palanki

Abstract:

Cryogenic engineering is the fast growing branch of the modern technology. There are various applications of the cryogenic engineering such as liquefaction in gas industries, metal industries, medical science, space technology, and transportation. The low-temperature technology developed superconducting materials which lead to reduce the friction and wear in various components of the systems. The liquid oxygen, hydrogen and helium play vital role in space application. The liquefaction process is produced very low temperature liquid for various application in research and modern application. The air liquefaction system for oxygen plants in gas industries is based on the Claude cycle. The effect of process parameters on the overall system is difficult to be analysed by manual calculations, and this provides the motivation to use process simulators for understanding the steady state and dynamic behaviour of such systems. The parametric study of this system via MATLAB simulations provide useful guidelines for preliminary design of air liquefaction system based on the Claude cycle. Every organization is always trying for reduce the cost and using the optimum performance of the plant for the staying in the competitive market.

Keywords: cryogenic, liquefaction, low -temperature, oxygen, claude cycle, optimization, MATLAB

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Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

Abstract:

The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: Shahram Naghizadeh Raeisi, Ali Alghooneh

Abstract:

The application of whey protein in drink industry to enhance the nutritional value of the products is important. Furthermore, the gelification of protein during thermal treatment and shelf life makes some limitations in its application. So, the main goal of this research is manufacturing of high concentrate whey protein orange drink with appropriate shelf life. In this way, whey protein was 5 to 30% hydrolyzed ( in 5 percent intervals at six stages), then thermal stability of samples with 10% concentration of protein was tested in acidic condition (T= 90 °C, pH=4.2, 5 minutes ) and neutral condition (T=120° C, pH:6.7, 20 minutes.) Furthermore, to study the shelf life of heat treated samples in 4 months at 4 and 24 °C, the time sweep rheological test were done. At neutral conditions, 5 to 20% hydrolyzed sample showed gelling during thermal treatment, whereas at acidic condition, was happened only in 5 to 10 percent hydrolyzed samples. This phenomenon could be related to the difference in hydrodynamic radius and zeta potential of samples with different level of hydrolyzation at acidic and neutral conditions. To study the gelification of heat resistant protein solutions during shelf life, for 4 months with 7 days intervals, the time sweep analysis were performed. Cross over was observed for all heat resistant neutral samples at both storage temperature, while in heat resistant acidic samples with degree of hydrolysis, 25 and 30 percentage at 4 and 20 °C, it was not seen. It could be concluded that the former sample was stable during heat treatment and 4 months storage, which made them a good choice for manufacturing high protein drinks. The Scheffe polynomial model and numerical optimization were employed for modeling and high protein orange drink formula optimization. Scheffe model significantly predicted the overal acceptance index (Pvalue<0.05) of sensorial analysis. The coefficient of determination (R2) of 0.94, the adjusted coefficient of determination (R2Adj) of 0.90, insignificance of the lack-of-fit test and F value of 64.21 showed the accuracy of the model. Moreover, the coefficient of variable (C.V) was 6.8% which suggested the replicability of the experimental data. The desirability function had been achieved to be 0.89, which indicates the high accuracy of optimization. The optimum formulation was found as following: Modified whey protein solution (65.30%), natural orange juice (33.50%), stevia sweetener (0.05%), orange peel oil (0.15%) and citric acid (1 %), respectively. Its worth mentioning that this study made an appropriate model for application of whey protein in drink industry without bitter flavor and gelification during heat treatment and shelf life.

Keywords: croos over, orange beverage, protein modification, optimization

Procedia PDF Downloads 62