Showing papers in "Arabian Journal for Science and Engineering in 2014"

Effect of Sodium Silicate to Sodium Hydroxide Ratios on Strength and Microstructure of Fly Ash Geopolymer Binder

Abstract: Geopolymerization can transform a wide range of waste aluminosilicate materials into building and mining materials with excellent chemical and physical properties. The present experimental study investigates the effect of sodium silicate/sodium hydroxide ratios on the feasibility of geopolymer synthesis at 80 °C using fly ash. The sodium silicate/sodium hydroxide (S/N) ratios 0.5, 1.0, 1.5, 2.0 and 2.5 were studied. The result showed that the compressive and flexural strength increases as the curing age increases. Also, the compressive strength increases as the sodium silicate/sodium hydroxide ratio increases from 0.5 to 1.0 and then decreases. Morphology studies, conducted by SEM analysis of the geopolymer samples, indicated that geopolymers gel had the fly ash particles and pores embedded in a continuous matrix. At S/N = 1 a homogeneous and less porous microstructure was observed.

Global Chaos Synchronization of n-Scroll Chua Circuit and Lur’e System using Backstepping Control Design with Recursive Feedback

Abstract: In this paper, global chaos synchronization is investigated for n-scroll Chua (Tang et al. in IEEE Trans Circ Syst I Fundam Theory Appl 48:1369–1372, 2001) and Lur’e (Suykens and Vandewalle in Int J Bifurc Chaos 7:1323–1325, 1997) chaotic systems using backstepping control with recursive feedback. Our theorems on synchronization for n-scroll Chua and Lur’e chaotic systems are established using Lyapunov stability theory. The backstepping scheme is a recursive procedure that links the choice of Lyapunov function with the design of a feedback controller and guarantees global stability performance of strict-feedback chaotic systems. Mainly the backstepping technique gives flexibility in designing a feedback control law. Numerical simulations are also given to illustrate and validate the synchronization results derived in this paper.

Autonomous Particles Groups for Particle Swarm Optimization

Abstract: In this paper, a modified particle swarm optimization (PSO) algorithm called autonomous groups particles swarm optimization (AGPSO) is proposed to further alleviate the two problems of trapping in local minima and slow convergence rate in solving high-dimensional problems. The main idea of AGPSO algorithm is inspired by individuals’ diversity in bird flocking or insect swarming. In natural colonies, individuals are not basically quite similar in terms of intelligence and ability, but they all do their duties as members of a colony. Each individual’s ability can be useful in a particular situation. In this paper, a mathematical model of diverse particles groups called autonomous groups is proposed. In other words different functions with diverse slopes, curvatures, and interception points are employed to tune the social and cognitive parameters of the PSO algorithm to give particles different behaviors as in natural colonies. The results show that PSO with autonomous groups of particles outperforms the conventional and some recent modifications of PSO in terms of escaping local minima and convergence speed. The results also indicate that dividing particles in groups and allowing them to have different individual and social thinking can improve the performance of PSO significantly.

Predicting Urban Land Use Changes Using a CA–Markov Model

Abstract: This study employs a CA–Markov model as one of the planning support tools for analysis of temporal changes and spatial distribution of urban land uses in Anzali, located in Gilan province in the northwest of Iran. In the first step, area changes and spatial distribution of land uses in the town were analyzed and calculated using geographic information systems technology for a time span 1989–2011. In the next step, using the transition matrix, the spatial distribution of urban land uses in 2021 was simulated, the changes were predicted and the possible growth patterns were identified as well. The results showed a declining trend of 10.64 % in forest, 8.52 % in Anzali wetland and 11.54 % in barren land during 1989–2011, and also an increasing trend of 7.1 % in urban areas for a time span 1989–2021. Major expansions in urban areas were witnessed around western and eastern borders of the city, particularly close to the eastern border. Scattered expansions were also predicted in the Anzali wetlands registered in the Ramsar Convention (southern borders). This study provides an opportunity to define and apply better strategies for environmental management of land use to make an optimized balance between urban development and ecological protection of environmental resources.

Image Encryption Using Random Bit Sequence Based on Chaotic Maps

Abstract: The paper proposes an algorithm for image encryption using the random bit sequence generator and based on chaotic maps. Chaotic Logistic and Tent maps are used to generate required random bit sequences. Pixels of the plain image are permuted using these chaotic functions, and then the image is partitioned into eight bit map planes. In each plane, bits are permuted and substituted according to random bit and random number matrices; these matrices are the products of those functions. The pixels and bit maps permutation stage are based on a chaotic random Ergodic matrix. This chaotic encryption method produces encrypted image whose performance is evaluated using chi-square test, correlation coefficient, number of pixel of change rate (NPCR), unified average changing intensity (UACI), and key space. The histogram of encrypted image is approximated by a uniform distribution with low chi-square factor. Horizontal, vertical, and diagonal correlation coefficients of two adjacent pixels of encrypted image are calculated. These factors are improved compared to other proposed methods. The NPCR and UACI values of encrypted image are also calculated. The result shows that a swift change in the original image will cause a significant change in the ciphered image. Total key space for the proposed method is (2^2, 160), which is large enough to protect the proposed encryption image against any brute-force attack.

A PSO-Based Fuzzy Long-Term Multi-Objective Optimization Approach for Placement and Parameter Setting of UPFC

Abstract: This paper presents a Particle Swarm Optimization (PSO)-based fuzzy multi-objective methodology for optimal locating and parameter setting of Unified Power Flow Controller (UPFC) in a power system for a long-term period. One of the profits obtained by UPFC placement in a transmission network is the reduction in total generation cost due to its ability to change the power flow pattern in the network. Considering this ability, UPFC can be also used to remove or at least mitigate the congestion in transmission networks. The other issue in a power system is voltage violation which could even render the optimal power flow problem infeasible to be solved. Voltage violation could be also mitigated by proper application of UPFC in a transmission system. These objectives are considered simultaneously in a unified objective function for the proposed optimization algorithm. At first, these objectives are fuzzified and designed to be comparable against each other and then they are integrated and introduced to a PSO Algorithm to find the solution which maximizes the value of integrated objective function in a 3-year planning horizon, considering the load growth. A power injection model is adopted for UPFC. Unlike the most previous works in this field the parameters of UPFC are set for each load level to avoid inconvenient rejection of more optimal solutions. IEEE Reliability Test System is used as an illustrative example to show the effectiveness of the proposed method.

Influence of TiO2 Nanoparticles on the Morphological, Thermal and Solution Properties of PVA/TiO2 Nanocomposite Membranes

Abstract: Polyvinylalcohol/titanium dioxide (PVA/TiO2) nanocomposite membranes were prepared by dispersing hydrophilic fumed TiO2 nanoparticles into the polymer matrix. The influence of TiO2 nanoparticles on the morphological, thermal and solution properties of PVA/TiO2 nanocomposite membranes was investigated using FESEM, XRD, DSC, TGA, rheometer, zeta sizer and contact angle meter. FESEM analysis shows that TiO2 nanoparticles up to 30wt% are dispersed homogeneously in the membranes without aggregation and covered by PVA polymeric chains. Above 30wt% TiO2 content, the level of aggregation increases, and at 50wt%, it was significant. The incorporation of TiO2 nanoparticles into the PVA matrix lowers the primary crystallinity of PVA and by inducing new crystalline regions due to TiO2; the overall crystallinity of the nanocomposite membranes is modified. Thermal stability of the composite membrane is improved by the addition of TiO2 nanoparticles. The increase of TiO2 concentration in PVA/TiO2 suspension has shown a transition in the regime of suspension from Newtonian to shear thinning starting at 10wt% TiO2. At low concentration of nanoparticles, the shear thinning behavior at lower shear rate is less. The shear thinning behavior increases as the concentration of TiO2 is increased. The conductivity of PVA/TiO2 dispersions is lower than PVA which indicates the formation of clusters, leading to decrease in number of charge carriers and their mobility. The zeta potential increases with increasing TiO2 content, which shows that PVA/TiO2 suspension is stable at higher content of TiO2. The hydrophilicity of PVA/TiO2 nanocomposite membranes increases as the loading of TiO2 is increased in the membrane.

Effect of Current in the EDM Machining of Aluminum 6061 T6 and its Effect on the Surface Morphology

Abstract: Electric discharge machining (EDM) produces a recast/white layer on the surface of the machined workpiece. Machining with EDM generally produces a higher surface roughness as compared to conventional machining processes. The operating parameters in EDM, i.e., “current”, “voltage”, “on time” and “off time” are directly related with the white layer formation, its thickness, morphology and roughness. Machining of Al 6061 T6 cylinders is performed on die-sinking EDM machine with varying electric current values to determine its effect on surface morphology in the case of aluminum alloy. Material removal rate versus current is determined for different current values. An attempt is made to relate the globule formation on the machined surface, with the machining current. Scanning electron microscopy, optical microscopy and material composition study through energy dispersive spectrography are performed. The white layer thickness, globule diameter and inter-globule distance are found to increase with the increase in electric current.

A Fuzzy PROMETHEE Approach for Mining Customer Reviews in Chinese

Abstract: Online customer reviews of products have a great impact on potential customers’ purchase decisions and provide valuable customer opinions to businesses. However, it is difficult for a customer to go through the huge number of customer reviews of a product to make an informed decision. The opinion comparison, one of the important tasks in opinion mining, uses main product features that have been commented upon by consumers to compare competing products. Because the task of comparing customer opinions can be expressed as the ranking of alternative products using key product features, it can be modeled as a multi-criteria decision making (MCDM) problem. The goal of this paper is to propose fuzzy PROMETHEE, an MCDM method, to rank alternative products based on online customer reviews of products. An experiment is designed to test the proposed method using a sample of Chinese reviews of mobile phones. The results demonstrate that this approach can not only generate a reliable and realistic ranking of products, but also identify key product features that are considered by consumers as the most important aspects of a product.

Effect of Non-Traditional Additives on Engineering and Microstructural Characteristics of Laterite Soil

Abstract: The stabilization of soils with additives is a chemical method that can be used to improve soils with lowengineering properties. The stabilizing mechanisms of TX-85 and SH-85 additives are not fully understood, and their proprietary chemical composition makes it very difficult to evaluate the stabilizing mechanisms and predict their performance. The objective of this study was to investigate the macro- and microstructural properties related to tropical laterite soil mixed with the specified non-traditional soil additives. The tests carried out, i.e., compaction and unconfined compression strength, were used to assess the engineering and shear properties of the stabilized laterite soil, and the physicochemical changes were monitored via field-emission scanning electron microscopy (FESEM) and thermal gravity analysis. Based on the results, it was found that both additives can decrease the dry density and increase the laterite soil strength approximately fourfold in comparison with the natural soil. FESEM results showed that the porosity of untreated soil was filled by the new cementitious products. Also, it was found that the treatment of laterite had a marginal impact on the thermal characteristics of the soil.

Prediction of Effect of Reinforcement Size and Volume Fraction on the Abrasive Wear Behavior of AA2014/B4Cp MMCs Using Artificial Neural Network

Abstract: In this study, an artificial neural network (ANN) approach was used to predict the abrasive wear behavior of AA2014 aluminum alloy matrix composites reinforced with B4C particles. The abrasive wear properties of varying volume fraction of particles up to 12 % B4C particle reinforced AA2014MMCS produced by stir casting method were investigated using a block-on-disc wear tester. Wear tests were performed under 92 N against the abrasive suspension mixture with a novel three body abrasive. For wear behavior, the volume loss, specific wear rate and surface roughness of the composites were measured. The effect of sliding time and content of B4C particles on the abrasive wear behavior were analyzed in detail. As a result of this study, the ANN was found to be successful for predicting the volume loss, specific wear rate and surface roughness of AA2014/B4C composites. The mean absolute percentage error (MAPE) for the predicted values did not exceed 4.1 %. The results have shown that ANN is an effective technique in the prediction of the properties of MMCs, and quite useful instead of time-consuming experimental processes.

Biosorption of Cadmium and Lead from Aqueous Solutions by Chlorella vulgaris Biomass: Equilibrium and Kinetic Study

Abstract: Biosorption of cadmium and lead from aqueous solution was investigated using green algae Chlorella vul- garis at room temperature and at pH adjusted to 7.0. Vari- ous sorption parameters such as contact time, initial metal ion concentration and biomass quantity were studied. The equilibrium experimental data are well represented by Lang- muir model among two-parameter models. It was noted that the maximum adsorption capacity for cadmium and lead was 149.9 and 178.5 mg (metal) g −1 biomass, respectively. The kinetic data were fitted by models including pseudo- first order and pseudo-second order. It was observed that, the pseudo-first-order kinetic model describes the biosorption of cadmium and lead ions onto C. vulgaris biomass.

Computational Analysis of Three-Dimensional Unsteady Natural Convection and Entropy Generation in a Cubical Enclosure Filled with Water-Al2O3 Nanofluid

Abstract: Effects of Rayleigh number, solid volume fraction and entropy generation on the natural convection heat transfer and fluid flow inside a three-dimensional cubical enclosure filled with water-Al2O3 nanofluid have been investigated numerically using the control volume finite difference method. The enclosure left sidewall is maintained at isothermal hot temperature, while the right one is maintained at isothermal cold temperature. The other enclosure walls are considered adiabatic. The second law of thermodynamics is applied to predict the nature of irreversibility in terms of entropy generation rate. Numerical computations are carried out for Rayleigh numbers from $${(10^{3} \leq {\rm Ra} \leq 10^{6})}$$ , solid volume fraction from $${(0\% \leq \varphi\, \leq 20\%)}$$ , while the Prandtl number of water is considered constant as (Pr = 6.2). Streamlines, isothermal lines, counters of local and total entropy generation and the variation of Bejan number, local and average Nusselt numbers are presented and discussed in detail. The results explain that the average Nusselt number increases when the solid volume fraction of nanoparticles and the Rayleigh number increase. Also, the Bejan and average Nusselt numbers have a reverse behavior to each other for the same range of Rayleigh number and solid volume fraction. In addition, the results show that the entropy generation rate due to heat transfer, friction and the total entropy generation increase as the solid volume fraction increases, while it increases highly when the Rayleigh number increases especially near the hot left sidewall.

Advance Particle Swarm Optimization-Based Navigational Controller For Mobile Robot

Abstract: While the robot is in motion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimization-based path planner of an autonomous mobile robot. In this approach, a fitness function has been introduced for converting the mobile robot navigation problem into multi objective optimization problem. The fitness of the swarm mainly depends on two parameters: (1) distance between each particle of the swarm and target, (2) distance between each particle of the swarm and the nearest obstacle. From the obtained fitness values of the swarm, the global best position of the particle is selected in each cycle. Thereby, the robot reaches the global best position in sequence. The effectiveness of the developed algorithm in various environments has been verified by simulation modes.

Investigation of Nanofluid Flow and Heat Transfer in Presence of Magnetic Field Using KKL Model

Abstract: In this paper, flow and heat transfer of a nanofluid in a square cavity with curve boundaries in presence of magnetic field is investigated numerically using lattice Boltzmann method. The base fluid in the enclosure is water containing Al2O3. The effective thermal conductivity and viscosity of nanofluid are calculated by KKL (Koo–Kleinstreuer–Li) correlation. In this model effect of Brownian motion on the effective thermal conductivity is considered. This investigation when compared with other numerical methods was found to be in excellent agreement. The influence of the nanoparticle volume fraction, Rayleigh number and Hartmann number on flow and heat transfer is investigated. The results show that enhancement in heat transfer increases with increase of Hartmann number except for Ra = 104 in which Ha = 40 roles as a critical Hartmann number.

Swarm Stability Analysis of Nonlinear Dynamical Multi-Agent Systems via Relative Lyapunov Function

Abstract: Conditions for swarm stability of nonlinear high-order multi-agent systems are analyzed based on the idea of space transformation. Swarm stability can be assured by sufficient connectivity of graph topology and dissipative property regulated by relative Lyapunov function, with two independent variables. The problems addressed are general, since the models concerned can be time-varying or heterogeneous.

Design of Linear and Elliptical Antenna Arrays Using Biogeography Based Optimization

Abstract: In this paper, the problem of designing linear and elliptical antenna arrays for specific radiation properties is dealt with. The biogeography-based optimization (BBO) method, which represents a new evolutionary algorithm, is used in the optimization process. BBO is used to minimize the maximum side lobe level (SLL) and null control for isotropic linear antenna arrays by optimizing different array parameters (position, amplitude, and phase). Similarly, for elliptical antenna array, four optimization techniques (BBO, genetic algorithm, self-adaptive differential evolution, and sequential quadratic programming) are used to determine an optimum set of weights that provide a radiation pattern with maximum SLL reduction with the constraint of a fixed major lobe beam width. The obtained results show the effectiveness of BBO compared to other well-known optimization methods.

Analysis of Stroop Color Word Test-Based Human Stress Detection using Electrocardiography and Heart Rate Variability Signals

Abstract: A stress assessment based on the electrocardiography (ECG) and heart rate variability (HRV) signals is described in this paper. The Stroop color word test (stressor) was used to induce stress, and the ECG signal was acquired throughout the experiment to identify the variations that are induced by this stressor. A total of 10 female subjects (aged 20–25 years) participated in this study. A time and frequency domain analysis of the HRV and ECG signals was done to extract the stress-related features. A total of five frequency bands and ratios of the HRV signal were used to analyze the new and existing statistical features. The results indicate that significant changes between the normal and stressed states are more evident with a classification accuracy of 79.17 %. Alternatively, the low frequency range (0.04–0.5 Hz) of the ECG signal (0–100 Hz) was used to identify the effect of stress instead of the usual frequency domain analysis of the HRV signal (0.04–0.5 Hz). To extract the stress-related features of the ECG signal, a discrete wavelet transform based feature extraction was performed using the “db4” and “coif5” wavelet functions. A set of eight statistical features was extracted from the two different frequency bands and the three frequency band ratios. All of the extracted features were classified into two states (stress and normal) using the simple non-linear K-nearest neighbor classifier. The experimental results gave the maximum average accuracy of 94.58 and 94.22 % with the “db4” and “coif5” wavelet functions, respectively. Remarkably, the classification results obtained with the features of the ECG and HRV signals were completely independent of the post-task questionnaire. The outcome of this work was helpful to develop the multiple physiological signal based stress system using optimal features in these two signals.

Large Consumer Electricity Acquisition Considering Time-of-Use Rates Demand Response Programs

Abstract: The consumers try to obtain their electricity demand at minimum cost from different resources in restructured electricity markets Hence more attention have been made on demand response programs (DRP) which aims to electricity price reduction, transmission lines congestion solution, security intensification and improvement of market liquidity and customer load benefit This paper develops a stochastic energy procurement model for large consumer with multiple options including distributed generations, bilateral contracts and pool market purchase considering DRP Pool market price uncertainty is modeled based on scenario distribution curve approach such as normal distribution curve The curve is divided into several areas, each area identified as a scenario, and the problem is solved using stochastic programming Also, this paper is focused to study the effect of DRP on total expected procurement cost has been discussed in all scenarios Actually a new stochastic framework using demand response program is presented for large consumer expected procurement cost reduction

A Model for Detecting Customer Level Intentions to Purchase in B2C Websites Using TOPSIS and Fuzzy Logic Rule-Based System

Abstract: The perception of people and attitude towards e-commerce and B2C websites significantly affect their intention to make purchase online. The technique for order preference by similarity to ideal solution (TOPSIS), one of the multi-criteria decision making methods, was developed to solve real-world decision problems that has continued to work satisfactorily across different application areas. This paper presents a model using TOPSIS and fuzzy logic for detecting the level of customer intentions to purchase against factors affecting the intention to purchase in business-to-customer (B2C) websites. The paper extends the work of Schaupp and Belanger (J Electron Commer Res 6(2), 95–111, 2005) which suggested technology, shopping, and product characteristics as important factors influencing customer satisfaction and purchase intention. The three main factors and a comprehensive list of attributes and features of B2C websites are investigated by synthesizing prior literatures. Furthermore, TOPSIS method is used for ranking the most important features of attributes and then fuzzy logic for building model to show real levels of the influencing factors, attributes and features on customer intentions to purchase in B2C websites based on customer’s perception of B2C websites. Two questionnaires were used in this paper, the first questionnaire used the TOPSIS method and the second questionnaire used data gathered from B2C websites. Respondents in this study are 450 online customers with experience in online shopping from B2C websites. Our model assists as an instrument to vendors and customers in determining the real-level effects of these factors on purchase intention in B2C websites.

Rock Pore Structure Damage Due to Freeze During Liquid Nitrogen Fracturing

Abstract: Liquid nitrogen has been successfully used as a fracturing fluid in petroleum engineering. When liquid nitrogen is pumped into a well, the physical conditions of the reservoir rocks will be altered. In these experiments, pore structure damage in a sandstone and a coal due to freezing with liquid nitrogen was measured by nuclear magnetic resonance. The changes both in amplitude of the T 2 distribution curve and in maximum T 2 value were analysed. The results showed that the action of freezing with liquid nitrogen produced damage to the pore structure and caused the development of micro-pores or micro-fissures. The change in amplitude of T 2 distribution curves showed that the new pores or fissures in coal specimens were larger than those in sandstone. This indicated that coal specimens were subjected to more serious damage to their pore structure because of their inherent natural fractures and weaker grain cementation. For sandstone, the change in amplitude of T 2 distribution curve peaks revealed that the damage increased with increasing water saturation. By contrasting the pore structure damage characteristics of coal and sandstone, a tentative conclusion that liquid nitrogen fracturing was more suitable for use in coal bed methane extraction than in sandstone reservoirs was drawn.

Damage Categorization of Glass/Epoxy Composite Material Under Mode II Delamination Using Acoustic Emission Data: A Clustering Approach to Elucidate Wavelet Transformation Analysis

Abstract: Delamination is the most common failure mode in composite materials, since it will result in the reduction of stiffness and can grow throughout other layers. Delamination consists of two main stages: initiation and propagation. Understanding the behavior of the material in these zones is imperative, hence the identification of this mode of failure is of great importance. There are several methods to identify damage in materials, one of which is using acoustic emission (AE) signals. Most of the pervious works have used statistical methods based on the energy of AE signals, but in this study, the normalized form of shock wave is used. The aim of this study is to extract a general pattern for specific damage from AE signals including all of the other damage signals. The method consists of a discrete wavelet packet decomposition of AE signals accompanied with a clustering algorithm, which gives the distribution of the normalized AE signal energy on the frequency band. Test set-up involved End Notched Flexure (ENF) test to detect mode II delamination on glass/epoxy composite material. The data obtained from ENF test specimens is used for the wavelet packet decomposition, and the energy of different levels of decomposition for each shock wave is clustered using different clustering algorithms including K-means and Fuzzy C-mean. Scanning Electron Microscope was used to validate the results.

Effects of Casimir Force and Thermal Stresses on the Buckling of Electrostatic Nanobridges Based on Couple Stress Theory

Abstract: In this paper, the thermal and size effects on the buckling behavior of a nanobeam symmetrically located between two electrodes, subjected to the influence of the nonlinear external forces including electrostatic and Casimir attractions, have been investigated. Based on the modified couple stress theory and by using Hamilton’s principle, the governing equation is derived from Euler–Bernoulli beam model and the non-dimensional critical buckling load is presented. In order to achieve linearization the equations, instead of nonlinear forces, their corresponding Maclaurin series expansions are used. Also, the differential quadrature method is utilized to solve the buckling equations numerically. Finally, the material length scale parameters (the size effects), the electrostatic and Casimir nonlinear forces, the temperature change, as well as the effect of the initial gap between beam and fixed electrodes on the buckling load are studied. The results indicated that by increasing the forces dependent on the displacement such as Casimir and electrostatic, the buckling load decreases. However, increasing the material length scale parameters leads to an increase in the buckling load value. Meanwhile, the initial gap does not significantly affect the buckling load. Furthermore, the non-dimensional critical buckling load becomes higher with the temperature change increasing.

Sb2O3 Nanowires as Anode Material for Sodium-Ion Battery

Abstract: The anodic properties of antimony trioxide (Sb2O3) nanowires were investigated as electrode material for sodium-ion battery. Sb2O3 nanowires were prepared via a mild-condition, solvothermal route based on the hydrolysis of antimony trichloride (SbCl3) in alcohol aqueous solution. The uniform morphology and crystal phases of Sb2O3 nanowires are confirmed by scanning electronic microscopy, transmission electronic microscopy, and X-ray diffraction. The electrochemical performance of Sb2O3 nanowire anodes was studied and the material exhibits a high reversible capacity of 230 mAh/g which is attributed to the reversible complex conversion–alloying reactions between antimony trioxide and sodium.

Optimization of wavelet-ANFIS and wavelet-ANN hybrid models by taguchi method for groundwater level forecasting

Abstract: In the recent years, artificial intelligence techniques have been widely developed for modeling hydrologic processes. Determining the best structures of these models such as Wavelet-ANN and Wavelet-ANFIS still remains a difficult task. In fact, there are several factors in the structure of these models that should be optimized. Selecting the best model structure by testing all of the possible combinations of factors is very time consuming and labor intensive. Using the optimization Taguchi method, this study assessed different factors affecting the performance of Wavelet-ANN and Wavelet-ANFIS hybrid models each of which has several levels. A L18 orthogonal array was selected according to the selected factors and levels and experimental tests were performed accordingly. Analysis of the signal-to-noise (S/N) ratio was used to evaluate the models performance. The optimum structures for both models were determined. For Wavelet-ANN, a model having 14 neurons in the hidden layer and trained with 1,000 epochs using Tangent Sigmoid (TanSig) transfer function in both hidden and output layers, and trained with Levenberg–Marquardt (LM) algorithm, whose input data were decomposed using Reverse Bior 1.5 (rbio1.5) wavelet in level 2, is the optimal Wavelet-ANN model. For Wavelet-ANFIS, a model with 700 iterations, using bell-shaped membership function and 5 membership functions, whose input data were decomposed using Daubechies 4 (db4) wavelet in level 2, is the optimal Wavelet-ANFIS model. Confirmation tests were then conducted using the optimum structures. It is also concluded that the best Wavelet-ANFIS model outperforms the best Wavelet-ANN model.

Application of Response Surface Methodology and Firefly Algorithm for Optimizing Multiple Responses in Turning AISI 1045 Steel

Abstract: Optimization of machining parameters considering multiple responses flank wear, surface roughness, and material removal rate (MRR) simultaneously are performed using response surface methodology (RSM). The workpiece material chosen for turning is AISI 1045, medium carbon steel, and uncoated carbide tool inserts. Twenty experiments are designed based on face-centered center composite design for three numerical parameters such as cutting speed, feed rate, and depth of cut. In this work, wear at the flank face of the cutting tool insert and surface roughness at the machined surface are to be minimized, whereas the MRR has to be maximized. With the obtained optimum condition, a confirmation experiment is performed and the experimental results obtained are flank wear of 0.118 mm, surface roughness of 3.27 μm, and MRR of 187.35 gm/min, which shows that prediction using RSM is within the acceptable range. Along with the combined optimization of these responses, a quadratic empirical model is generated for each response. An evolutionary optimization algorithm, firefly algorithm, is applied to determine the optimum machining parameters for the chosen objective of lowering flank wear and increasing MRR within a specific surface roughness value.

A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems

Abstract: Solar photovoltaic (PV) energy has witnessed growth in the past decade. Nowadays, PV energy systems have proved to be effective methods for renewable energy resources with minimum environmental impacts. Due to these environmental and economic benefits, PV systems are being widely deployed as distributed energy resources in distribution generation systems or microgrids. Maximum power point tracking (MPPT) algorithms have an important role to play due to optimization performance in these systems. In this paper, PV array output voltage has been optimized by increasing the MPPT algorithm performance. A new hybrid fuzzy-neural MPPT controller is proposed. Training data in neural network are optimized by genetic algorithm. The proposed controller is simulated and studied using MATLAB software. The obtained results show superior capability of the suggested method in MPP tracking under rapid fluctuation of atmospheric conditions and converter load.

GIS Based Assessment of Groundwater Vulnerability Using Drastic Model

Abstract: Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The DRASTIC model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. In this study the DRASTIC model is applied for a part of Kancheepuram district, Tamil Nadu, India, to generate a small-scale map of groundwater vulnerability to contamination. The whole area is classified on a scale of very low, low, moderate and high susceptibility to pollution. The model is considered in relation to groundwater quality data and results have shown a strong relationship between DRASTIC specific vulnerability index and nitrate-as-nitrogen concentrations. A groundwater vulnerability map is developed by using the DRASTIC model in a computer based Geographic Information System. The results show that the central part of the study area is classified as a high vulnerable zone and the south and northeastern parts show medium vulnerable zones, and record higher nitrate values.

Multi-objective Optimization of Process Parameters in Wire Electric Discharge Machining of Ti-6-2-4-2 Alloy

Abstract: The present paper identifies the different process parameters that effect the cutting speed and surface roughness in wire electrical discharge machining of Titanium-6-2-4-2 (HSTR aerospace alloy), which is so far not reported in the literature. It also identifies optimal process parameter combination for simultaneous optimization of cutting speed and surface roughness to be presented as a guideline for machining of Titanium-6-2-4-2. Box–Behnken design and response surface methodology are used to plan and analyse the experiments. Six control factors viz. Pulse on time, pulse off time, peak current, spark gap set voltage, wire feed, and wire tension are chosen as process parameters to study the performance of the process in terms of cutting speed and surface roughness. The recommended optimal parameter combinations have been verified by conducting confirmation experiments.

Numerical Simulation of Heat Flow in Friction Stud Welding of Dissimilar Metals

Abstract: Friction stud welding is a solid-state joining technique used for welding similar and dissimilar materials with high integrity. In friction stud welding, heat is generated by the conversion of mechanical energy into thermal energy at the interface of the work pieces during rotation under pressure. This complicated metallurgical process is accompanied by frictional heat generation, plastic deformation, cooling of high-temperature metal, and solid-state phase variation. Since the thermal cycle of friction stud welding is very short, simulation becomes a vital role to study the behaviour of materials. The simulations make it possible to observe the temperature distribution and heat transfer fields that take place during the process. In the present work, a three-dimensional nonlinear Finite Element-based heat flow model is developed for friction stud welding of aluminium and mild steel combination. The numerical model is validated with a temperature history at the weld interface measured using a non-contact-type infrared thermometer. During friction stud welding, temperature, temperature distribution, temperature gradient, heat transfer rate and their variations, govern welding parameters of a welding machine. Knowledge of them helps to determine optimum parameters and ways to improve the design and manufacture of welding machines. Hence, the developed numerical model could be used as a tool to study the thermal cycles during the process and it will be very much useful for the subsequent analysis of residual stress and distortion of welded joints.