Search results for: optimal reaction network

Authors: Zouhour Neji Ben Salem

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Microscopic evacuation focuses on the evacuee behavior and way of search of safety place in an egress situation. In recent years, several models handled microscopic evacuation problem. Among them, we have proposed Artificial Neural Network (ANN) as an alternative to mathematical models that can deal with such problem. In this paper, we present two ANN models: SOM map and Hopfield Network used to predict the evacuee behavior in a disaster situation. These models are tested in a real case, the second floor of Tunisian children hospital evacuation in case of fire. The two models are studied and compared in order to evaluate their performance.

Keywords: artificial neural networks, self-organization map, hopfield network, microscopic evacuation, fire building evacuation

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

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Authors: R. Dariani, S. Schmidt, R. Kasper

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Based on a non-linear single track model which describes the dynamics of vehicle, an optimal path planning strategy is developed. Real time optimization is used to generate reference control values to allow leading the vehicle alongside a calculated lane which is optimal for different objectives such as energy consumption, run time, safety or comfort characteristics. Strict mathematic formulation of the autonomous driving allows taking decision on undefined situation such as lane change or obstacle avoidance. Based on position of the vehicle, lane situation and obstacle position, the optimization problem is reformulated in real-time to avoid the obstacle and any car crash.

Keywords: autonomous driving, obstacle avoidance, optimal control, path planning

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Authors: David Hasurungan

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This paper objective is to analyze the impact from photovoltaic system integration to power distribution network. The case study in Badak Liquefied Natural Gas (LNG) plant is presented in this paper. Badak LNG electricity network is operated in islanded mode. The total power generation in Badak LNG plant is significantly affected to feed gas supply. Meanwhile, to support the Government regulation, Badak LNG continuously implemented the grid-connected photovoltaic system in existing power distribution network. The impact between train operational mode change in Badak LNG plant and the growth of photovoltaic system is also encompassed in analysis. The analysis and calculation are performed using software Power Factory 15.1.

Keywords: power quality, distribution network, grid-connected photovoltaic system, power management system

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Authors: Thomas E. Portegys

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An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.

Keywords: artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation

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Authors: Dina M. Abd El-Aty, D. Aman, E. G. Zaki, Heba M. Salem

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A friendly environmental source of energy as hydrogen was produced by photo-hydrolysis of hydrogen storage material as sodium borohydride (NaBH4), which is non-toxic and stores a high percentage of hydrogen. The photoreaction was produced under visible light and nano-alumina as a catalyst. In this study, we use more economical and friendly environmental oil as a template to produce a nano-catalyst. The prepared catalyst was characterized by X-Ray diffraction, N2-adsorption-desorption, Fourier Transforms Infrared, Scanning Electron microscope and X-Ray Photoelectron Spectroscopy. Different parameters such as catalyst weight, NaBH4 weight and time of irradiation were studied to obtain a highly efficient photo-hydrolysis reaction. The reaction is pseudo-first order and the hydrogen production rate was determined as 1500 ml min-1 g-1 at the optimum conditions.

Keywords: photo-reaction, nano-alumina, hydrogen production, sodium borohydride, visible light

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Authors: Balamurali Gunji, B. B. V. L. Deepak, B. B. Biswal, Amrutha Rout, Golak Bihari Mohanta

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Robots play an important role in the operations like pick and place, assembly, spot welding and much more in manufacturing industries. Out of those, assembly is a very important process in manufacturing, where 20% of manufacturing cost is wholly occupied by the assembly process. To do the assembly task effectively, Assembly Sequences Planning (ASP) is required. ASP is one of the multi-objective non-deterministic optimization problems, achieving the optimal assembly sequence involves huge search space and highly complex in nature. Many researchers have followed different algorithms to solve ASP problem, which they have several limitations like the local optimal solution, huge search space, and execution time is more, complexity in applying the algorithm, etc. By keeping the above limitations in mind, in this paper, a new automated optimal robotic assembly sequence planning using Artificial Bee Colony (ABC) Algorithm is proposed. In this algorithm, automatic extraction of assembly predicates is done using Computer Aided Design (CAD) interface instead of extracting the assembly predicates manually. Due to this, the time of extraction of assembly predicates to obtain the feasible assembly sequence is reduced. The fitness evaluation of the obtained feasible sequence is carried out using ABC algorithm to generate the optimal assembly sequence. The proposed methodology is applied to different industrial products and compared the results with past literature.

Keywords: assembly sequence planning, CAD, artificial Bee colony algorithm, assembly predicates

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: Oya Umit Yemisci, Nur Saracgil Cosar, Tubanur Ozturk Sisman, Selin Ozen

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Chronic autoimmune thyroiditis (AIT) may result in a wide spectrum of reversible abnormalities in the neuromuscular function. Usually, proximal muscle-related symptoms and neuropathic findings such as mild axonal peripheral neuropathy have been reported. Sympathetic skin responses are useful in evaluating sudomotor activity of the unmyelinated sympathetic fibers of the autonomic nervous system. Neurocognitive impairment may also be a prominent feature of hypothyroidism, particularly in elderly patients. Electromyographic reaction times as a highly sensitive parameter provides. Objective data concerning cognitive and motor functions. The aim of this study was to evaluate peripheral nerve functions, sympathetic skin response and electroneuromyographic (ENMG) reaction times in euthyroid and subclinically hypothyroid patients with a diagnosis of AIT and compare to those of a control group. Thirty-five euthyroid, 19 patients with subclinical hypothyroidism and 35 age and sex-matched healthy subjects were included in the study. Motor and sensory nerve conduction studies, sympathetic skin responses recorded from hand and foot by stimulating contralateral median nerve and simple reaction times by stimulating tibial nerve and recording from extensor indicis proprius muscle were performed to all patients and control group. Only median nerve sensory conduction velocities of the forearm were slower in patients with AIT compared to the control group (p=0.019). Otherwise, nerve conduction studies and sympathetic skin responses showed no significant difference between the patients and the control group. However, reaction times were shorter in the healthy subjects compared to AIT patients. Prolongation in the reaction times may be considered as a parameter reflecting the alterations in the cognitive functions related to the primary disease process in AIT. Combining sympathetic skin responses with more quantitative tests such as cardiovascular tests and sudomotor axon reflex testing may allow us to determine higher rates of involvement of the autonomic nervous system in AIT.

Keywords: sympathetic skin response, simple reaction time, chronic autoimmune thyroiditis

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Authors: M. J. Park, S. H. Lee, C. H. Lee, O. M. Kwon

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This paper studies a secondary voltage control framework of the microgrids based on the consensus for a communication network of multiagent. The proposed control is designed by the communication network with one-way links. The communication network is modeled by a directed graph. At this time, the concept of sampling is considered as the communication constraint among each distributed generator in the microgrids. To analyze the sampling effects on the secondary voltage control of the microgrids, by using Lyapunov theory and some mathematical techniques, the sufficient condition for such problem will be established regarding linear matrix inequality (LMI). Finally, some simulation results are given to illustrate the necessity of the consideration of the sampling effects on the secondary voltage control of the microgrids.

Keywords: microgrids, secondary control, multiagent, sampling, LMI

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Authors: Thirupathi Thippani, Kothandaraman Ramanujam

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Current research on energy storage has been paid to metal-air batteries, because of attractive alternate energy source for the future. Metal – air batteries have the probability to significantly increase the power density, decrease the cost of energy storage and also used for a long time due to its high energy density, low-level pollution, light weight. The performance of these batteries mostly restricted by the slow kinetics of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) on cathode during battery discharge and charge. The ORR and OER are conventionally carried out with precious metals (such as Pt) and metal oxides (such as RuO₂ and IrO₂) as catalysts separately. However, these metal-based catalysts are regularly undergoing some difficulties, including high cost, low selectivity, poor stability and unfavorable to environmental effects. So, in order to develop the active, stable, corrosion resistance and inexpensive bi-functional catalyst material is mandatory for the commercialization of zinc-air rechargeable battery technology. We have attempted and synthesized non-precious metal (NPM) catalysts comprising cobalt and N-doped multiwalled carbon nanotubes (N-MWCNTs-Co) were synthesized by the solid-state pyrolysis (SSP) of melamine with Co₃O₄. N-MWCNTs-Co acts as an excellent electrocatalyst for both the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER), and hence can be used in secondary metal-air batteries and in unitized regenerative fuel cells. It is important to study the OER and ORR at high concentrations of KOH as most of the metal-air batteries employ KOH concentrations > 4M. In the first 16 cycles of the zinc-air battery while using N-MWCNTs-Co, 20 wt.% Pt/C or 20 wt.% IrO₂/C as air electrodes. In the ORR regime (the discharge profile of the zinc-air battery), the cell voltage exhibited by N-MWCNTs-Co was 44 and 83 mV higher (based on 5th cycle) in comparison to of 20 wt.% Pt/C and 20 wt.% IrO₂/C respectively. To demonstrate this promise, a zinc-air battery was assembled and tested at a current density of 0.5 Ag⁻¹ for charge-discharge 100 cycles.

Keywords: oxygen reduction reaction (ORR), oxygen evolution reaction(OER), non-platinum, zinc air battery

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Authors: Horng-Wen Wu, Yi Chao, Rong-Fang Horng

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This study is to develop a methanol steam reformer with a heat recovery zone, which recovers heat from exhaust gas of a diesel engine, and to investigate waste heat recovery ratio at the required reaction temperature. The operation conditions of the reformer are reaction temperature (200 °C, 250 °C, and 300 °C), steam to carbonate (S/C) ratio (0.9, 1.1, and 1.3), and N2 volume flow rate (40 cm3/min, 70 cm3/min, and 100 cm3/min). Finally, the hydrogen concentration, the CO, CO2, and N2 concentrations are measured and recorded to calculate methanol conversion efficiency, hydrogen flow rate, and assisting combustion gas and impeding combustion gas ratio. The heat source of this reformer comes from electric heater and waste heat of exhaust gas from diesel engines. The objective is to recover waste heat from the engine and to make more uniform temperature distribution within the reformer. It is beneficial for the reformer to enhance the methanol conversion efficiency and hydrogen-rich gas production. Experimental results show that the highest hydrogen flow rate exists at N2 of the volume rate 40 cm3/min and reforming reaction temperature of 300 °C and the value is 19.6 l/min. With the electric heater and heat recovery from exhaust gas, the maximum heat recovery ratio is 13.18 % occurring at water-methanol (S/C) ratio of 1.3 and the reforming reaction temperature of 300 °C.

Keywords: heat recovery, hydrogen-rich production, methanol steam reformer, methanol conversion efficiency

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: Ehsan Khademi, Mahdi Jalili

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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Jose L. Oliver, Taras Agryzkov, Leandro Tortosa, Jose F. Vicent, Javier Santacruz

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This paper aims to demonstrate how a topological study of an urban street network can be used as a tool to be applied to some heritage conservation areas in a city. In the last decades, we find different kinds of approaches in the discipline of Architecture and Urbanism based in the so-called Sciences of Complexity. In this context, this paper uses mathematics from the Network Theory. Hence, it proposes a methodology based in obtaining information from a graph, which is created from a network of urban streets. Then, it is used an algorithm that establishes a ranking of importance of the nodes of that network, from its topological point of view. The results are applied to a heritage area in a particular city, confronting the data obtained from the mathematical model, with the ones from the field work in the case study. As a result of this process, we may conclude the necessity of implementing some actions in the area, and where those actions would be more effective for the whole heritage site.

Keywords: graphs, heritage cities, spatial analysis, urban networks

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Authors: Xu Jie

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As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decisionmaking problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security). By modeling the multi-job collaborative cryptographic service scheduling problem as a multiobjective optimized job flow scheduling problem, and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing, and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: multi-agent reinforcement learning, non-stationary dynamics, multi-agent systems, cooperative and competitive agents

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Authors: Peter A. L’vov, Roman S. Konovalov, Alexey A. L’vov

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The paper presents the advanced digital modification of the conventional current loop circuit for pressure piezoelectric transducers. The optimal DSP algorithms of current loop responses by the maximum likelihood method are applied for diminishing of measurement errors. The loop circuit has some additional advantages such as the possibility to operate with any type of resistance or reactance sensors, and a considerable increase in accuracy and quality of measurements to be compared with AC bridges. The results obtained are dedicated to replace high-accuracy and expensive measuring bridges with current loop circuits.

Keywords: current loop, maximum likelihood method, optimal digital signal processing, precise pressure measurement

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Authors: Abasali Amini, Alireza Mirbagheri, Amir Homayoun Jafari

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In this paper, a new structure for teleoperation systems with time varying delay has been modeled and proposed. A random time varying the delay of up to 150 msec is simulated in teleoperation channel of both masters to slave and vice versa. The system stability and transparency have been investigated, comparing the result of a PID controller and an optimal controller on each master and slave sub-systems separately. The controllers have been designed in slave subsystem for reducing position errors between master and slave, and another controller has been designed in the master subsystem to establish stability, transparency and force tracking. Results have been compared together. The results showed PID controller is appropriate in position tracking, but force response oscillates in contact with the environment. We showed the optimal control established position tracking properly. Also, force tracking is achieved in this controller appropriately.

Keywords: optimal control, time varying delay, teleoperation systems, stability and transparency

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Authors: Mohd. Akram, A. A. M. Saeed

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Surfactants are widely used in our daily life either directly in household and personal care products or indirectly in the industrial processes. The kinetics of the interaction of glycyl-DL-aspartic acid (Gly-DL-Asp) with ninhydrin has been investigated spectrophotometrically in aqueous and organic-solvent media in the absence and presence of cationic surfactant of tetradecyltrimethylammonium bromide (TTAB). The study was carried out under different experimental conditions. The first and fractional order-rate were observed for [Gly-DL-Asp] and [ninhydrin], respectively. The reaction was enhanced about four-fold by TTAB micelles. The effect of organic solvents was studied at a constant concentration of TTAB and showed an increase in the absorbance as well as the rate constant for the formation of product (Ruhemann's purple). The results obtained in micellar media are treated quantitatively in terms of pseudo-phase and Piszkiewicz cooperativity models. The Arrhenius and Eyring equations are valid for the reaction over the range of temperatures used and different activation parameters (Ea, ∆H#, ∆S#, and ∆G#) have been evaluated.

Keywords: glycyl-DL-aspartic acid, ninhydrin, organic solvents, TTAB

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Authors: U. P. L. Wijayarathne, K. C. Wasalathilake

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This work presents the modelling and simulation of saponification of ethyl acetate in the presence of sodium hydroxide in a plug flow reactor using Aspen Plus simulation software. Plug flow reactors are widely used in the industry due to the non-mixing property. The use of plug flow reactors becomes significant when there is a need for continuous large scale reaction or fast reaction. Plug flow reactors have a high volumetric unit conversion as the occurrence for side reactions is minimum. In this research Aspen Plus V8.0 has been successfully used to simulate the plug flow reactor. In order to simulate the process as accurately as possible HYSYS Peng-Robinson EOS package was used as the property method. The results obtained from the simulation were verified by the experiment carried out in the EDIBON plug flow reactor module. The correlation coefficient (r2) was 0.98 and it proved that simulation results satisfactorily fit for the experimental model. The developed model can be used as a guide for understanding the reaction kinetics of a plug flow reactor.

Keywords: aspen plus, modelling, plug flow reactor, simulation

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Authors: Bhairi Lakshminarayana, Surajit Sarker, Ch. Subrahmanyam

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The present study reports the development of noble metal free nano catalysts for low-temperature CO oxidation and water gas shift reaction. Mn-substituted CeO2 solid solution catalysts were synthesized by co-precipitation, combustion and hydrothermal methods. The formation of solid solution was confirmed by XRD with Rietveld refinement and the percentage of carbon and nitrogen doping was ensured by CHNS analyzer. Raman spectroscopic confirmed the oxygen vacancies. The surface area, pore volume and pore size distribution confirmed by N2 physisorption analysis, whereas, UV-visible diffuse reflectance spectroscopy and XPS data confirmed the oxidation state of the Mn ion. The particle size and morphology (spherical shape) of the material was confirmed using FESEM and HRTEM analysis. Ce0.8Mn0.2O2-δ was calcined at 400 °C, 600 °C and 800 °C. Raman spectroscopy confirmed that the catalyst calcined at 400 °C has the best redox properties. The activity of the designed catalysts for CO oxidation (0.2 vol%), carried out with GHSV of 21,000 h-1 and it has been observed that co-precipitation favored the best active catalyst towards CO oxidation and water gas shift reaction, due to the high surface area, improved reducibility, oxygen mobility and highest quantity of surface oxygen species. The activation energy of low temperature CO oxidation on Ce0.8Mn0.2O2- δ (combustion) was 5.5 kcal.K-1.mole-1. The designed catalysts were tested for water gas shift reaction. The present study demonstrates that Mn ion substituted ceria at 400 °C calcination temperature prepared by co-precipitation method promise to revive a green sustainable energy production approach.

Keywords: Ce0.8Mn0.2O2-ð, CO oxidation, physicochemical characterization, water gas shift reaction (WGS)

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Authors: Yuanhao Gao, Ping Lin, Kees Weijer

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An optimal control system model is proposed for the cell flow in the process of chick embryo gastrulation in this paper. The target is to determine the cellular interaction forces which are hard to measure. This paper will take an approach to investigate the forces with the idea of the inverse problem. By choosing the forces as the control variable and regarding the cell flow as Stokes fluid, an objective functional will be established to match the numerical result of cell velocity with the experimental data. So that the forces could be determined by minimizing the objective functional. The Lagrange multiplier method is utilized to derive the state and adjoint equations consisting the optimal control system, which specifies the first-order necessary conditions. Finite element method is used to discretize and approximate equations. A conjugate gradient algorithm is given for solving the minimum solution of the system and determine the forces.

Keywords: optimal control model, Stokes equation, conjugate gradient method, finite element method, chick embryo gastrulation

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Authors: Bita Bayatsarmadi, Shi-Zhang Qiao

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The development of ordered mesoporous carbon materials with controllable structures and improved physicochemical properties by doping heteroatoms such as nitrogen into the carbon framework has attracted a lot of attention, especially in relation to energy storage and conversion. Herein, a series of Nitrogen-doped mesoporous carbon spheres (NMC) was synthesized via a facile dual soft-templating procedure by tuning the nitrogen content and carbonization temperature. Various physical and (electro) chemical properties of the NMCs have been comprehensively investigated to pave the way for feasible design of nitrogen-containing porous carbon materials. The optimized sample showed a favorable electrocatalytic activity as evidenced by high kinetic current and positive onset potential for oxygen reduction reaction (ORR) due to its large surface area, high pore volume, good conductivity and high nitrogen content, which make it as a highly efficient ORR metal-free catalyst in alkaline solutions.

Keywords: porous carbon, N-doping, oxygen reduction reaction, soft-template

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Authors: Mouhamadou Diop, Mohamed I. Hassan

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Molten metallic flow in metallurgical plant is highly turbulent and presents a complex coupling with heat transfer, phase transfer, chemical reaction, momentum transport, etc. Molten silicon flow has significant effect in directional solidification of multicrystalline silicon by affecting the temperature field and the emerging crystallization interface as well as the transport of species and impurities during casting process. Owing to the complexity and limits of reliable measuring techniques, computational models of fluid flow are useful tools to study and quantify these problems. The overall objective of this study is to investigate the potential of a traveling magnetic field for an efficient operating control of the molten metal flow. A multidimensional numerical model will be developed for the calculations of Lorentz force, molten metal flow, and the related phenomenon. The numerical model is implemented in a laboratory-scale silicon crystallization furnace. This study presents the potential of traveling magnetic field approach for an efficient operating control of the molten flow. A numerical model will be used to study the effects of magnetic force applied on the molten flow, and their interdependencies. In this paper, coupled and decoupled, steady and unsteady models of molten flow and crystallization interface will be compared. This study will allow us to retrieve the optimal traveling magnetic field parameter range for crystallization furnaces and the optimal numerical simulations strategy for industrial application.

Keywords: multidimensional, numerical simulation, solidification, multicrystalline, traveling magnetic field

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Authors: Zhaofeng Li, Jun Kang Chow, Yu-Hsing Wang

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This paper attempts to bridge the soil properties and the mechanical response of soil in the discrete element method (DEM) simulation. The artificial neural network (ANN) was therefore adopted, aiming to reproduce the stress-strain-volumetric response when soil properties are given. 31 biaxial shearing tests with varying soil parameters (e.g., initial void ratio and interparticle friction coefficient) were generated using the DEM simulations. Based on these 45 sets of training data, a three-layer neural network was established which can output the entire stress-strain-volumetric curve during the shearing process from the input soil parameters. Beyond the training data, 2 additional sets of data were generated to examine the validity of the network, and the stress-strain-volumetric curves for both cases were well reproduced using this network. Overall, the ANN was found promising in predicting the soil behavior and reducing repetitive simulation work.

Keywords: artificial neural network, discrete element method, soil properties, stress-strain-volumetric response

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Authors: Nathalie Elia, Samer Aouad, Jane Estephane, Christophe Poupin, Bilal Nsouli, Edmond Abi Aad

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Carbon dioxide is one of the main contributors to greenhouse effect and hence to climate change. As a result, the methanation reaction CO2(g) + 4H2(g) →CH4(g) + 2H2O (ΔH°298 = -165 kJ/mol), also known as Sabatier reaction, has received great interest as a process for the valorization of the greenhouse gas CO2 into methane which is a hydrogen-carrier gas. The methanation of CO2 is an exothermic reaction favored at low temperature and high pressure. However, this reaction requires a high energy input to activate the very stable CO2 molecule, and exhibits serious kinetic limitations. Consequently, the development of active and stable catalysts is essential to overcome these difficulties. Catalytic methanation of CO2 has been studied using catalysts containing Rh, Pd, Ru, Co and Ni on various supports. Among them, the Ni-based catalysts have been extensively investigated under various conditions for their comparable methanation activity with highly improved cost-efficiency. The addition of promoters are common strategies to increase the performance and stability of Ni catalysts. In this work, a small amount of Ru was used as a promoter for Ni catalysts supported on ceria and tested in the CO2 methanation reaction. The nickel loading was 5 wt. % and ruthenium loading is 0.5wt. %. The catalysts were prepared by successive impregnation method using Ni(NO3)2.6H2O and Ru(NO)(NO3)3 as precursors. The calcined support was impregnated with Ni(NO3)2.6H2O, dried, calcined at 600°C for 4h, and afterward, was impregnated with Ru(NO)(NO3)3. The resulting solid was dried and calcined at 600°C for 4 h. Supported monometallic catalysts were prepared likewise. The prepared solids Ru(0.5%)/CeO2, Ni(5%)/CeO2 and Ru(0.5%)-Ni(5%)/CeO2 were then reduced prior to the catalytic test under a flow of 50% H2/Ar (50 ml/min) for 4h at 500°C. Finally, their catalytic performances were evaluated in the CO2 methanation reaction, in the temperature range of 100–350°C by using a gaseous mixture of CO2 (10%) and H2 (40%) in Ar balanced at a total flow rate of 100 mL/min. The effect of pressure on the CO2 methanation was studied by varying the pressure between 1 and 10 bar. The various catalysts showed negligible CO2 conversion at temperatures lower than 250°C. The conversion of CO2 increases with increasing reaction temperature. The addition of Ru as promoter to Ni/CeO2 improved the CO2 methanation. It was shown that the CO2 conversion increases from 15 to 70% at 350°C and 1 bar. The effect of pressure on CO2 conversion was also studied. Increasing the pressure from 1 to 5 bar increases the CO2 conversion from 70% to 87%, while increasing the pressure from 5 to 10 bar increases the CO2 conversion from 87% to 91%. Ru–Ni catalysts showed excellent catalytic performance in the methanation of carbon dioxide with respect to Ni catalysts. Therefore the addition of Ru onto Ni catalysts improved remarkably the catalytic activity of Ni catalysts. It was also found that the pressure plays an important role in improving the CO2 methanation.

Keywords: CO2, methanation, nickel, ruthenium

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Authors: Vrince Vimal, Madhav J. Nigam

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Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.

Keywords: Wireless Sensor network (WSN), Random Deployment, Clustering, Isolated Nodes, Networks Lifetime

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Authors: M. M. Othman, Walid El-Khattam, Y. G. Hegazy, A. Y. Abdelaziz

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This paper presents a method for finding the optimal location and capacity of dispatchable DGs connected to the distribution feeders for optimal planning for a specified power loss without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37-nodes feeder. The results that are validated by comparing it with results obtained from other competing methods show the effectiveness, accuracy and speed of the proposed method.

Keywords: distributed generators, firefly technique, optimization, power loss

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

Abstract:

This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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