Search results for: constriction factor based particle swarm optimization (CPSO)

Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Nur Azilina Abdul Aziz, Tuti Katrina Abdullah, Ahmad Azmin Mohamad

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Lithium-transition metals and some of their oxides, such as LiCoO2, LiMn2O2, LiFePO4, and LiNiO2 have been used as cathode materials in high performance lithium-ion rechargeable batteries. Among the cathode materials, LiCoO2 has potential to been widely used as a lithium-ion battery because of its layered crystalline structure, good capacity, high cell voltage, high specific energy density, high power rate, low self-discharge, and excellent cycle life. This cathode material has been widely used in commercial lithium-ion batteries due to its low irreversible capacity loss and good cycling performance. However, there are several problems that interfere with the production of material that has good electrochemical properties, including the crystallinity, the average particle size and particle size distribution. In recent years, synthesis of nanoparticles has been intensively investigated. Powders prepared by the traditional solid-state reaction have a large particle size and broad size distribution. On the other hand, solution method can reduce the particle size to nanometer range and control the particle size distribution. In this study, LiCoO2 was synthesized using the sol–gel preparation method, which Lithium acetate and Cobalt acetate were used as reactants. The stoichiometric amounts of the reactants were dissolved in deionized water. The solutions were stirred for 30 hours using magnetic stirrer, followed by heating at 80°C under vigorous stirring until a viscous gel was formed. The as-formed gel was calcined at 700°C for 7 h under a room atmosphere. The structural and morphological analysis of LiCoO2 was characterized using X-ray diffraction and Scanning electron microscopy. The diffraction pattern of material can be indexed based on the α-NaFeO2 structure. The clear splitting of the hexagonal doublet of (006)/(102) and (108)/(110) in this patterns indicates materials are formed in a well-ordered hexagonal structure. No impurity phase can be seen in this range probably due to the homogeneous mixing of the cations in the precursor. Furthermore, SEM micrograph of the LiCoO2 shows the particle size distribution is almost uniform while particle size is between 0.3-0.5 microns. In conclusion, LiCoO2 powder was successfully synthesized using the sol–gel method. LiCoO2 showed a hexagonal crystal structure. The sample has been prepared clearly indicate the pure phase of LiCoO2. Meanwhile, the morphology of the sample showed that the particle size and size distribution of particles is almost uniform.

Keywords: cathode material, LiCoO2, lithium-ion rechargeable batteries, Sol-Gel method

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Authors: Mehran Naghizaderokni, Asscar Janalizadechobbasty

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This paper explores probabilistic method for assessing the liquefaction potential of sandy soils. The current simplified methods for assessing soil liquefaction potential use a deterministic safety factor in order to determine whether liquefaction will occur or not. However, these methods are unable to determine the liquefaction probability related to a safety factor. A solution to this problem can be found by reliability analysis.This paper presents a reliability analysis method based on the popular certain liquefaction analysis method. The proposed probabilistic method is formulated based on the results of reliability analyses of 190 field records and observations of soil performance against liquefaction. The results of the present study show that confidence coefficient greater and smaller than 1 does not mean safety and/or liquefaction in cadence for liquefaction, and for assuring liquefaction probability, reliability based method analysis should be used. This reliability method uses the empirical acceleration attenuation law in the Chalos area to derive the probability density distribution function and the statistics for the earthquake-induced cyclic shear stress ratio (CSR). The CSR and CRR statistics are used in continuity with the first order and second moment method to calculate the relation between the liquefaction probability, the safety factor and the reliability index. Based on the proposed method, the liquefaction probability related to a safety factor can be easily calculated. The influence of some of the soil parameters on the liquefaction probability can be quantitatively evaluated.

Keywords: liquefaction, reliability analysis, chalos area, civil and structural engineering

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Roman Marsalek

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In this paper, snow samples containing dust particles from several sampling points around the city of Ostrava were analyzed. The pH values of sampled snow were measured and solid particles analyzed. Particle size, zeta potential and content of selected heavy metals were determined in solid particles. The pH values of most samples lay in the slightly acid region. Mean values of particle size ranged from 290.5 to 620.5 nm. Zeta potential values varied between -5 and -26.5 mV. The following heavy metal concentration ranges were found: copper 0.08-0.75 mg/g, lead 0.05-0.9 mg/g, manganese 0.45-5.9 mg/g and iron 25.7-280.46 mg/g. The highest values of copper and lead were found in the vicinity of busy crossroads, and on the contrary, the highest levels of manganese and iron were detected close to a large steelworks. The proportion between pH values, zeta potentials, particle sizes and heavy metal contents was established. Zeta potential decreased with rising pH values and, simultaneously, heavy metal content in solid particles increased. At the same time, higher metal content corresponded to lower particle size.

Keywords: dust, snow, zeta potential, particles size distribution, heavy metals

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Authors: Yu-Hsuan Liu, Ying-Fang Wang

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The objectives of the present study are to characterize nanoparticles generated from the laser metal deposition (LMD) process and to estimate particle concentrations deposited in the head (H), that the tracheobronchial (TB) and alveolar (A) regions, respectively. The studied LMD chamber (3.6m × 3.8m × 2.9m) is installed with a robot laser metal deposition machine. Direct-reading instrument of a scanning mobility particle sizer (SMPS, Model 3082, TSI Inc., St. Paul, MN, USA) was used to conduct static sampling inside the chamber for nanoparticle number concentration and particle size distribution measurements. The SMPS obtained particle number concentration at every 3 minutes, the diameter of the SMPS ranged from 11~372 nm when the aerosol and sheath flow rates were set at 0.6 and 6 L / min, respectively. The resultant size distributions were used to predict depositions of nanoparticles at the H, TB, and A regions of the respiratory tract using the UK National Radiological Protection Board’s (NRPB’s) LUDEP Software. Result that the number concentrations of nanoparticles in indoor background and LMD chamber were 4.8×10³ and 4.3×10⁵ # / cm³, respectively. However, the nanoparticles emitted from the LMD process was in the form of the uni-modal with number median diameter (NMD) and geometric standard deviation (GSD) as 142nm and 1.86, respectively. The fractions of the nanoparticles deposited on the alveolar region (A: 69.8%) were higher than the other two regions of the head region (H: 10.9%), tracheobronchial region (TB: 19.3%). This study conducted static sampling to measure the nanoparticles in the LMD process, and the results show that the fraction of particles deposited on the A region was higher than the other two regions. Therefore, applying the characteristics of nanoparticles emitted from LMD process could be provided valuable scientific-based evidence for exposure assessments in the future.

Keywords: exposure assessment, laser metal deposition process, nanoparticle, respiratory region

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Authors: Heming Yao

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Graphene-based percolative strain gauges could find applications in many places such as touch panels, artificial skins or human motion detection because of its advantages over conventional strain gauges such as flexibility and transparency. These strain gauges rely on a novel sensing mechanism that depends on strain-induced morphology changes. Once a compression or tension strain is applied to Graphene-based percolative strain gauges, the overlap area between neighboring flakes becomes smaller or larger, which is reflected by the considerable change of resistance. Tiny strain change on graphene-based percolative strain sensor can act as an important leverage to tremendously increase resistance of strain sensor, which equipped graphene-based percolative strain gauges with higher gauge factor. Despite ongoing research in the underlying sensing mechanism and the limits of sensitivity, neither suitable understanding has been obtained of what intrinsic factors play the key role in adjust gauge factor, nor explanation on how the strain gauge sensitivity can be enhanced, which is undoubtedly considerably meaningful and provides guideline to design novel and easy-produced strain sensor with high gauge factor. We here simulated the strain process by modeling graphene flakes and its percolative networks. We constructed the 3D resistance network by simulating overlapping process of graphene flakes and interconnecting tremendous number of resistance elements which were obtained by fractionizing each piece of graphene. With strain increasing, the overlapping graphenes was dislocated on new stretched simulation graphene flake simulation film and a new simulation resistance network was formed with smaller flake number density. By solving the resistance network, we can get the resistance of simulation film under different strain. Furthermore, by simulation on possible variable parameters, such as out-of-plane resistance, in-plane resistance, flake size, we obtained the changing tendency of gauge factor with all these variable parameters. Compared with the experimental data, we verified the feasibility of our model and analysis. The increase of out-of-plane resistance of graphene flake and the initial resistance of sensor, based on flake network, both improved gauge factor of sensor, while the smaller graphene flake size gave greater gauge factor. This work can not only serve as a guideline to improve the sensitivity and applicability of graphene-based strain sensors in the future, but also provides method to find the limitation of gauge factor for strain sensor based on graphene flake. Besides, our method can be easily transferred to predict gauge factor of strain sensor based on other nano-structured transparent optical conductors, such as nanowire and carbon nanotube, or of their hybrid with graphene flakes.

Keywords: graphene, gauge factor, percolative transport, strain sensor

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Authors: Christina Hagen, Pragathi Kamale Gurmurthy, Thorsten M. Buzug

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CFD simulations are performed in the upper airway of a patient suffering from obstructive sleep apnea (OSA) that is a sleep related breathing disorder characterized by repetitive partial or complete closures of the upper airways. The simulations are aimed at getting a better understanding of the pathophysiological flow patterns in an OSA patient. The simulation is compared to medical data of a sleep endoscopic examination under sedation. A digital model consisting of surface triangles of the upper airway is extracted from the MR images by a region growing segmentation process and is followed by a careful manual refinement. The computational domain includes the nasal cavity with the nostrils as the inlet areas and the pharyngeal volume with an outlet underneath the larynx. At the nostrils a flat inflow velocity profile is prescribed by choosing the velocity such that a volume flow rate of 150 ml/s is reached. Behind the larynx at the outlet a pressure of -10 Pa is prescribed. The stationary incompressible Navier-Stokes equations are numerically solved using finite elements. A grid convergence study has been performed. The results show an amplification of the maximal velocity of about 2.5 times the inlet velocity at a constriction of the pharyngeal volume in the area of the tongue. It is the same region that also shows the highest pressure drop from about 5 Pa. This is in agreement with the sleep endoscopic examinations of the same patient under sedation showing complete contractions in the area of the tongue. CFD simulations can become a useful tool in the diagnosis and therapy of obstructive sleep apnea by giving insight into the patient’s individual fluid dynamical situation in the upper airways giving a better understanding of the disease where experimental measurements are not feasible. Within this study, it could been shown on one hand that constriction areas within the upper airway lead to a significant pressure drop and on the other hand a good agreement of the area of pressure drop and the area of contraction could be shown.

Keywords: biomedical engineering, obstructive sleep apnea, pharynx, upper airways

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Authors: S. Gokul Prassad, S. Aakash, K. Malar Mohan

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In the process to cope with the challenges faced by the automobile industry in providing ride comfort, the electronics and control systems play a vital role. The control systems in an automobile monitor various parameters, controls the performances of the systems, thereby providing better handling characteristics. The automobile suspension system is one of the main systems that ensure the safety, stability and comfort of the passengers. The system is solely responsible for the isolation of the entire automobile from harmful road vibrations. Thus, integration of the control systems in the automobile suspension system would enhance its performance. The diverse road conditions of India demand the need of an efficient suspension system which can provide optimum ride comfort in all road conditions. For any passenger vehicle, the design of the suspension system plays a very important role in assuring the ride comfort and handling characteristics. In recent years, the air suspension system is preferred over the conventional suspension systems to ensure ride comfort. In this article, the ride comfort of the adaptive suspension system is compared with that of the passive suspension system. The schema is created in MATLAB/Simulink environment. The system is controlled by a proportional integral differential controller. Tuning of the controller was done with the Particle Swarm Optimization (PSO) algorithm, since it suited the problem best. Ziegler-Nichols and Modified Ziegler-Nichols tuning methods were also tried and compared. Both the static responses and dynamic responses of the systems were calculated. Various random road profiles as per ISO 8608 standard are modelled in the MATLAB environment and their responses plotted. Open-loop and closed loop responses of the random roads, various bumps and pot holes are also plotted. The simulation results of the proposed design are compared with the available passive suspension system. The obtained results show that the proposed adaptive suspension system is efficient in controlling the maximum over shoot and the settling time of the system is reduced enormously.

Keywords: automobile suspension, MATLAB, control system, PID, PSO

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Authors: Jing Hu, Xiaoping Ouyang

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We develop an optical readout gas chamber based on avalanche-induced scintillation for energetic charged particles track. The gas chamber is equipped with a Single Anode Wires (SAW) structure to produce intensive electric field when the measured particles are of low yield or even single. In the presence of an intensive electric field around the single anode, primary electrons, resulting from the incident charged particles when depositing the energy along the track, accelerate to the anode effectively and rapidly. For scintillation gasses, this avalanche of electrons induces multiplying photons comparing with the primary scintillation excited directly from particle energy loss. The electric field distribution for different shape of the SAW structure is analyzed, and finally, an optimal one is used to study the optical readout performance. Using CF4 gas and its mixture with the noble gas, the results indicate that the optical readout characteristics of the chamber are attractive for imaging. Moreover, images of particles track including single particle track from 5.485MeV alpha particles are successfully acquired. The track resolution is quite well for the reason that the electrons undergo less diffusion in the intensive electric field. With the simple and ingenious design, the optical readout gas chamber has a high sensitivity. Since neutrons can be converted to charged particles when scattering, this optical readout gas chamber can be applied to neutron measurement for dark matter, fusion research, and others.

Keywords: optical readout, gas chamber, charged particle track, avalanche-induced scintillation, neutron measurement

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Authors: Mehrdad Rezaei, Reza Haghmaram, Nima Amjadi

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This paper proposes an operating and cost optimization model for micro-grid (MG). This model takes into account emission costs of NOx, SO2, and CO2, together with the operation and maintenance costs. Wind turbines (WT), photovoltaic (PV) arrays, micro turbines (MT), fuel cells (FC), diesel engine generators (DEG) with different capacities are considered in this model. The aim of the optimization is minimizing operation cost according to constraints, supply demand and safety of the system. The proposed genetic algorithm (GA), with the ability to fine-tune its own settings, is used to optimize the micro-grid operation.

Keywords: micro-grid, optimization, genetic algorithm, MG

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Authors: M. I. Abdelhamid, A. O. Ghallab, R. S. Ettouney, M. A. El-Rifai

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An optimization scheme based on COM server is suggested for communication between Genetic Algorithm (GA) toolbox of MATLAB and Aspen HYSYS. The structure and details of the proposed framework are discussed. The power of the developed scheme is illustrated by its application to the optimization of a recently developed natural gas liquefaction process in which Aspen HYSYS was used for minimization of the power consumption by optimizing the values of five operating variables. In this work, optimization by coupling between the GA in MATLAB and Aspen HYSYS model of the same process using the same five decision variables enabled improvements in power consumption by 3.3%, when 77% of the natural gas feed is liquefied. Also on inclusion of the flow rates of both nitrogen and carbon dioxide refrigerants as two additional decision variables, the power consumption decreased by 6.5% for a 78% liquefaction of the natural gas feed.

Keywords: stranded gas liquefaction, genetic algorithm, COM server, single nitrogen expansion, carbon dioxide pre-cooling

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Authors: Abdulbaset Saad, Adel Younis, Zuomin Dong

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For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge.

Keywords: differential evolution, engineering design, expensive computations, meta-modeling, radial basis function, optimization

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Authors: A. Peniak, J. Makarovič, P. Rafajdus, P. Dúbravka

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The purpose of this work is to optimize a Switched Reluctance Motor (SRM) for an automotive application, specifically for a fully electric car. A new optimization approach is proposed. This unique approach transforms automotive customer requirements into an optimization problem, based on sound knowledge of a SRM theory. The approach combines an analytical and a finite element analysis of the motor to quantify static nonlinear and dynamic performance parameters, as phase currents and motor torque maps, an output power and power losses in order to find the optimal motor as close to the reality as possible, within reasonable time. The new approach yields the optimal motor which is competitive with other types of already proposed motors for automotive applications. This distinctive approach can also be used to optimize other types of electrical motors, when parts specifically related to the SRM are adjusted accordingly.

Keywords: automotive, drive system, electric car, finite element method, hybrid car, optimization, switched reluctance motor

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Authors: Aweda Francis Olatunbosun, Falaiye Oluwasesan Adeniran

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The suspended harmattan dust was collected at seven different stations in Nigeria: Iwo (7º 63'N, 4º 19'E), Oyo (8º 12'N, 3º 42'E), Ilorin (8º36'N, 4º 35'E), Minna (9º36'N, 06º35'E), Abuja (09º 09'N, 07º 11'E), Lafia (08º 49'N, 07º50'E), and Jos (9º55'N, 8º55'E), which were analyzed to determine elements and minerals present in the sample using X-Ray Fluorescence (XRF), and Particle Induced X-Ray Emission (PIXE). The collected sample results show the elemental concentration of the sample in various forms across each station. Cr, Ce, Mo, Zr, Sr, V, Ti, K, As, Ni, Mn, Ca, Pb, Fe, Zn, and Cu were found in the sample using an XRF machine. The minerals discovered in the sample include, but are not limited to, Corundum [Al₂O₃], Periclase [MgO], Rutile [TiO₂], and Quartz [SiO₂] in various proportions. Furthermore, the results revealed the enrichment factor for Iwo (1.3998 μg/m³), Oyo (1.3998 μg/m³), Ilorin (1.79765 μg/m³), Minna (1.737325 μg/m³), Abuja (1.635425 μg/m³), Lafia (1.409695 μg/m³), and Jos (1.787075 μg/m³). The study concluded that the sample contains sixteen (16) elements and minerals in varying percentages and concentrations. It is therefore recommended that appropriate safety procedures be put in place to raise community awareness of the presence of elements in harmattan dust.

Keywords: elements, minerals, harmattan dust, XRF, PIXE

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Authors: Ahmad Rouhani, Masood Jabbari, Sima Honarmand

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This paper introduces a method to optimal design of a hybrid Wind/Photovoltaic/Fuel cell generation system for a typical domestic load that is not located near the electricity grid. In this configuration the combination of a battery, an electrolyser, and a hydrogen storage tank are used as the energy storage system. The aim of this design is minimization of overall cost of generation scheme over 20 years of operation. The Matlab/Simulink is applied for choosing the appropriate structure and the optimization of system sizing. A teaching learning based optimization is used to optimize the cost function. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. The results have been analyzed in terms of technics and economics. The simulation results indicate that the proposed hybrid system would be a feasible solution for stand-alone applications at remote locations.

Keywords: hybrid energy system, optimum sizing, power management, TLBO

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Authors: Shamik Hazra, Sushanta Mitra, Ashis Sen

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Despite growing interest of microparticle manipulation in non-Newtonian fluids, combined effect of viscoelasticity and shear thinning on particle lateral position is not well understood. We performed experiments with rigid microparticles of 15 µm diamater in popular Shear thinning viscoelastic (STVE) liquid poyethylene oxide (PEO) of different molecular weights (MW) and concentrations (c), for Reynolds number (Re) < 1. Microparticles in an STVE liquid revealed four different migration regimes: original streamline (OS), bimodal (BM), centre migration (CM) and defocusing (DF), depending upon the Re and c and interplay of different forces is also elucidated. Our investigation will be helpful to select proper polymer concentration to achieve desired particle focusing inside microchannel.

Keywords: lateral migration, microparticle, polyethylene oxide, shear thinning, viscoelasticity

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Authors: Md. Z. Alam, Devi R. Asih, Md. N. Salleh

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Immobilization of lipase enzyme produced from palm oil mill effluent (POME) by the activated carbon (AC) among the low cost support materials was optimized. The results indicated that immobilization of 94% was achieved by AC as the most suitable support material. A sequential optimization strategy based on a statistical experimental design, including one-factor-at-a-time (OFAT) method was used to determine the equilibrium time. Three components influencing lipase immobilization were optimized by the response surface methodology (RSM) based on the face-centered central composite design (FCCCD). On the statistical analysis of the results, the optimum enzyme concentration loading, agitation rate and carbon active dosage were found to be 30 U/ml, 300 rpm and 8 g/L respectively, with a maximum immobilization activity of 3732.9 U/g-AC after 2 hrs of immobilization. Analysis of variance (ANOVA) showed a high regression coefficient (R2) of 0.999, which indicated a satisfactory fit of the model with the experimental data. The parameters were statistically significant at p<0.05.

Keywords: activated carbon, POME based lipase, immobilization, adsorption

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Authors: Rahul Paul, Kedar Nath Das

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The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models.

Keywords: machine learning, reinforcement learning, loss function, evolutionary optimization techniques

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Authors: Saeed Kamarian, Mahmoud Shakeri

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Sandwich structures are used in a variety of engineering applications including aircraft, construction and transportation where strong, stiff and light structures are required. In this paper, frequency maximization of Functionally Graded Sandwich (FGS) beams resting on Pasternak foundations is investigated. A generalized power-law distribution with four parameters is considered for material distribution through the thicknesses of face layers. Since the search space is large, the optimization processes becomes so complicated and too much time consuming. Thus a novel meta–heuristic called Imperialist Competitive Algorithm (ICA) which is a socio-politically motivated global search strategy is implemented to improve the speed of optimization process. Results show the success of applying ICA for engineering problems especially for design optimization of FGM sandwich beams.

Keywords: sandwich beam, functionally graded materials, optimization, imperialist competitive algorithm

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Authors: Tomasz Robert Kuczerski

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The high complexity of the algorithm of the autonomous tandem detonator system creates an optimization problem due to the parallel operation of several machine states of the system. Many years of experience and classic analyses have led to a partially optimized model. Limitations on the energy resources of this class of autonomous systems make it necessary to search for more effective methods of optimisation. The use of the Quantum Approximate Optimization Algorithm (QAOA) in these studies shows the most promising results. With the help of multiple evaluations of several qubit quantum circuits, proper results of variable parameter optimization were obtained. In addition, it was observed that the increase in the number of assessments does not result in further efficient growth due to the increasing complexity of optimising variables. The tests confirmed the effectiveness of the QAOA optimization method.

Keywords: algorithm analysis, autonomous system, quantum optimization, tandem detonator

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Authors: M. Farhaoui, M. Derraz

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In the drinking water treatment processes, the optimization of the treatment is an issue of particular concern. In general, the process consists of many units as settling, coagulation, flocculation, sedimentation, filtration and disinfection. The optimization of the process consists of some measures to decrease the managing and monitoring expenses and improve the quality of the produced water. The objective of this study is to provide water treatment operators with methods and practices that enable to attain the most effective use of the facility and, in consequence, optimize the of the cubic meter price of the treated water. This paper proposes a review on optimization of drinking water treatment process by analyzing all of the water treatment units and gives some solutions in order to maximize the water treatment performances without compromising the water quality standards. Some solutions and methods are performed in the water treatment plant located in the middle of Morocco (Meknes).

Keywords: coagulation process, optimization, turbidity removal, water treatment

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Authors: N. A. Zainuddin, I. Norhuda, I. S. Adeib, A. N. Mustapa, S. H. Sarijo

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Recently, RESS (Rapid Expansion Supercritical Solution) method has been used by researchers to produce fine particles for pharmaceutical drug substances. Since RESS technology acknowledges a lot of benefits compare to conventional method of ginger extraction, it is suggested to use this method to explore particle formation of bioactive compound from powder ginger. The objective of this research is to produce direct solid oil particles formation from ginger rhizome which contains valuable compounds by using RESS-CO₂ process. RESS experiments were carried using extraction pressure of 3000, 4000, 5000, 6000 and 7000psi and at different extraction temperature of 40, 45, 50, 55, 60, 65 and 70°C for 40 minutes extraction time and contant flowrate (24ml/min). From the studies conducted, it was found that at extraction pressure 5000psi and temperature 40°C, the smallest particle size obtained was 2.22μm on 99 % reduction from the original size of 370μm.

Keywords: particle size, RESS, solid oil particle, supercritical carbon dioxide,

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Authors: Miroslav Mrlik, Marketa Ilcikova, Martin Cvek, Josef Osicka, Michal Sedlacik, Vladimir Pavlinek, Jaroslav Mosnacek

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Magnetorheological elastomers (MREs) are a unique type of materials consisting of two components, magnetic filler, and elastomeric matrix. Their properties can be tailored upon application of an external magnetic field strength. In this case, the change of the viscoelastic properties (viscoelastic moduli, complex viscosity) are influenced by two crucial factors. The first one is magnetic performance of the particles and the second one is off-state stiffness of the elastomeric matrix. The former factor strongly depends on the intended applications; however general rule is that higher magnetic performance of the particles provides higher MR performance of the MRE. Since magnetic particles possess low stability properties against temperature and acidic environment, several methods how to improve these drawbacks have been developed. In the most cases, the preparation of the core-shell structures was employed as a suitable method for preservation of the magnetic particles against thermal and chemical oxidations. However, if the shell material is not single-layer substance, but polymer material, the magnetic performance is significantly suppressed, due to the in situ polymerization technique, when it is very difficult to control the polymerization rate and the polymer shell is too thick. The second factor is the off-state stiffness of the elastomeric matrix. Since the MR effectivity is calculated as the relative value of the elastic modulus upon magnetic field application divided by elastic modulus in the absence of the external field, also the tuneability of the cross-linking reaction is highly desired. Therefore, this study is focused on the controllable modification of magnetic particles using a novel monomeric system based on 2-(1H-pyrrol-1-yl)ethyl methacrylate. In this case, the short polymer chains of different chain lengths and low polydispersity index will be prepared, and thus tailorable stability properties can be achieved. Since the relatively thin polymer chains will be grafted on the surface of magnetic particles, their magnetic performance will be affected only slightly. Furthermore, also the cross-linking density will be affected, due to the presence of the short polymer chains. From the application point of view, such MREs can be utilized for, magneto-resistors, piezoresistors or pressure sensors especially, when the conducting shell on the magnetic particles will be created. Therefore, the selection of the pyrrole-based monomer is very crucial and controllably thin layer of conducting polymer can be prepared. Finally, such composite particle consisting of magnetic core and conducting shell dispersed in elastomeric matrix can find also the utilization in shielding application of electromagnetic waves.

Keywords: atom transfer radical polymerization, core-shell, particle modification, electromagnetic waves shielding

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Authors: Aadiv Shah, Hari Nair, Vedant Mittal, Alice Cheeran

Abstract:

Present-day agricultural practices such as blanket spraying not only lead to excessive usage of pesticides but also harm the overall crop yield. This paper introduces an algorithm to optimize the traversal of an unmanned aerial vehicle (UAV) in constrained environments. The proposed system focuses on the agricultural application of targeted spraying for locust elimination. Given a satellite image of a farm, target zones that are prone to locust swarm formation are detected through the calculation of the normalized difference vegetation index (NDVI). This is followed by determining the optimal path for traversal of a UAV through these target zones using the proposed algorithm in order to perform pesticide spraying in the most efficient manner possible. Unlike the classic travelling salesman problem involving point-to-point optimization, the proposed algorithm determines an optimal path for multiple regions, independent of its geometry. Finally, the paper explores the idea of implementing reinforcement learning to model complex environmental behaviour and make the path planning mechanism for UAVs agnostic to external environment changes. This system not only presents a solution to the enormous losses incurred due to locust attacks but also an efficient way to automate agricultural practices across the globe in order to improve farmer ergonomics.

Keywords: locust, NDVI, optimization, path planning, reinforcement learning, UAV

Procedia PDF Downloads 231

Authors: Hong Yu, Ion Matei

Abstract:

Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

Procedia PDF Downloads 179

Authors: Subzar Ahmad Bhat, Saraswati Setia, V. K.Sehgal

Abstract:

During the past earthquakes, open ground storey buildings have performed poorly due to the soft storey defect. Indian Standard IS 1893:2002 allows analysis of open ground storey buildings without considering infill stiffness but with a multiplication factor 2.5 in compensation for the stiffness discontinuity. Therefore, the aim of this paper is to check the applicability of the multiplication factor of 2.5 and study behaviour of the structure after the application of the multiplication factor. For this purpose, study is performed on models considering infill stiffness using SAP 2000 (Version 14) by linear static analysis and response spectrum analysis. Total seven models are analysed and designed for the range of multiplication factor ranging from 1.25 to 2.5. The value of multiplication factor equal to 2.5 has been found on the higher side, resulting in increased dimension and percentage of reinforcement without significant enhancement beyond a certain multiplication factor. When the building with OGS is designed for values of MF higher than 1.25 considering infill stiffness soft storey effect shifts from ground storey to first storey. For the analysis of the OGS structure best way to analysis the structure is to analyse it as the frame with stiffness and strength of the infill taken into account. The provision of infill walls in the upper storeys enhances the performance of the structure in terms of displacement and storey drift controls.

Keywords: open ground storey, multiplication factor, IS 1893:2002 provisions, static analysis, response spectrum analysis, infill stiffness, equivalent strut

Procedia PDF Downloads 373

Authors: K. Y. Tseng, C. Y. Wu

Abstract:

This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.

Keywords: differential evolution, Truss structure optimization, optimal chiller loading, modified binary differential evolution

Procedia PDF Downloads 150

Authors: Aman Sharma, Sonali Sengupta

Abstract:

The study explores the utility of Bayesian optimization in tuning the physical and chemical parameters of reactions in an offline experimental setup. A comparative analysis of the influence of the acquisition function on the optimization performance is also studied. For proxy first and second-order reactions, the results are indifferent to the acquisition function used, whereas, while studying the parameters for oxidative desulphurization of thiophene in an offline setup, upper confidence bound (UCB) provides faster convergence along with a marginal trade-off in the maximum conversion achieved. The work also demarcates the critical number of independent parameters and input observations required for both sequential and offline reaction setups to yield tangible results.

Keywords: acquisition function, Bayesian optimization, desulfurization, kinetics, thiophene

Procedia PDF Downloads 165

Authors: Luristya Nur Mahfut, Nada Mawarda Rilek, Ameiga Cautsarina Putri, Mujaroh Khotimah

Abstract:

The use of bioetanol from lignocellulosic material has begone to be developed. In Indonesia the most abundant lignocellulosic material is stem of palm which contain 32.22% of cellulose. Indonesia produces approximatelly 300.375.000 tons of stem of palm each year. To produce bioetanol from lignocellulosic material, the first process is pretreatment. But, until now the method of lignocellulosic pretretament is uneffective. This is related to the particle size and the method of pretreatment of less than optimal so that led to an overhaul of the lignin insufficient, consequently increased levels of cellulose was not significant resulting in low yield of bioetanol. To solve the problem, this research was implemented by using the process of pretreatment method ultasonifikasi in order to produce higher pulp with nano-sized particles that will obtain higher of yield ethanol from stem of palm. Research methods used in this research is the RAK that is composed of one factor which is the frequency ultrasonic waves with three varians, they are 30 kHz, 40 kHz, 50 kHz, and use constant variable is concentration of NaOH. The analysis conducted in this research is the influence of the frequency of the wave to increase levels of cellulose and change size on the scale of nanometers on pretreatment process by using the PSA methods (Particle Size Analyzer), and a Cheason. For the analysis of the results, data, and best treatment using ANOVA and test BNT with confidence interval 5%. The best treatment was obtained by combination X3 (frequency of sonication 50 kHz) and lignin (19,6%) cellulose (59,49%) and hemicellulose (11,8%) with particle size 385,2nm (18,8%).

Keywords: bioethanol, pretreatment, stem of palm, cellulosa

Procedia PDF Downloads 314