Search results for: bi-level optimization model

Authors: Zeshan Ahmad, Matteo Zoppi, Rezia Molfino

Abstract:

The geometric errors in the manufacturing process can be reduced by optimal positioning of the fixture elements in the fixture to make the workpiece stiff. We propose a new fixture layout optimization method N-3-2-1 for large metal sheets in this paper that combines the genetic algorithm and finite element analysis. The objective function in this method is to minimize the sum of the nodal deflection normal to the surface of the workpiece. Two different kinds of case studies are presented, and optimal position of the fixturing element is obtained for different cases.

Keywords: Fixture layout, optimization, fixturing element, genetic algorithm.

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Authors: Susmi Routray, A. M. Sherry, B. V. R. Reddy

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Asynchronous Transfer Mode (ATM) is widely used in telecommunications systems to send data, video and voice at a very high speed. In ATM network optimizing the bandwidth through dynamic routing is an important consideration. Previous research work shows that traditional optimization heuristics result in suboptimal solution. In this paper we have explored non-traditional optimization technique. We propose comparison of two such algorithms - Genetic Algorithm (GA) and Tabu search (TS), based on non-traditional Optimization approach, for solving the dynamic routing problem in ATM networks which in return will optimize the bandwidth. The optimized bandwidth could mean that some attractive business applications would become feasible such as high speed LAN interconnection, teleconferencing etc. We have also performed a comparative study of the selection mechanisms in GA and listed the best selection mechanism and a new initialization technique which improves the efficiency of the GA.

Keywords: Asynchronous Transfer Mode(ATM), GeneticAlgorithm(GA), Tabu Search(TS).

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Authors: Aspasia Koutsokosta, Stefanos Katsavounis

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This paper reviews the model-based qualitative and quantitative Operations Management research in the context of Construction Supply Chain Management (CSCM). Construction industry has been traditionally blamed for low productivity, cost and time overruns, waste, high fragmentation and adversarial relationships. The construction industry has been slower than other industries to employ the Supply Chain Management (SCM) concept and develop models that support the decision-making and planning. However the last decade there is a distinct shift from a project-based to a supply-based approach of construction management. CSCM comes up as a new promising management tool of construction operations and improves the performance of construction projects in terms of cost, time and quality. Modeling the Construction Supply Chain (CSC) offers the means to reap the benefits of SCM, make informed decisions and gain competitive advantage. Different modeling approaches and methodologies have been applied in the multi-disciplinary and heterogeneous research field of CSCM. The literature review reveals that a considerable percentage of the CSC modeling research accommodates conceptual or process models which present general management frameworks and do not relate to acknowledged soft Operations Research methods. We particularly focus on the model-based quantitative research and categorize the CSCM models depending on their scope, objectives, modeling approach, solution methods and software used. Although over the last few years there has been clearly an increase of research papers on quantitative CSC models, we identify that the relevant literature is very fragmented with limited applications of simulation, mathematical programming and simulation-based optimization. Most applications are project-specific or study only parts of the supply system. Thus, some complex interdependencies within construction are neglected and the implementation of the integrated supply chain management is hindered. We conclude this paper by giving future research directions and emphasizing the need to develop optimization models for integrated CSCM. We stress that CSC modeling needs a multi-dimensional, system-wide and long-term perspective. Finally, prior applications of SCM to other industries have to be taken into account in order to model CSCs, but not without translating the generic concepts to the context of construction industry.

Keywords: Construction supply chain management, modeling, operations research, optimization and simulation.

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Authors: YuriyTasinkevych, Ihor Trots, AndrzejNowicki, Marcin Lewandowski

Abstract:

The paper presents the optimization problem for the multi-element synthetic transmit aperture method (MSTA) in ultrasound imaging applications. The optimal choice of the transmit aperture size is performed as a trade-off between the lateral resolution, penetration depth and the frame rate. Results of the analysis obtained by a developed optimization algorithm are presented. Maximum penetration depth and the best lateral resolution at given depths are chosen as the optimization criteria. The optimization algorithm was tested using synthetic aperture data of point reflectors simulated by Filed II program for Matlab® for the case of 5MHz 128-element linear transducer array with 0.48 mm pitch are presented. The visualization of experimentally obtained synthetic aperture data of a tissue mimicking phantom and in vitro measurements of the beef liver are also shown. The data were obtained using the SonixTOUCH Research systemequipped with a linear 4MHz 128 element transducerwith 0.3 mm element pitch, 0.28 mm element width and 70% fractional bandwidth was excited by one sine cycle pulse burst of transducer's center frequency.

Keywords: synthetic aperture method, ultrasound imaging, beamforming.

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Authors: Roshan Sharma, Bjørn Glemmestad

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In gas lifted oil fields, the lift gas should be distributed optimally among the wells which share gas from a common source to maximize total oil production. One of the objectives of the paper is to show that a linear MPC consisting of a control objective and an economic objective can be used both as an optimizer and a controller for gas lifted systems. The MPC is based on linearized model of the oil field developed from first principles modeling. Simulation results show that the total oil production is increased by 3.4%. Difficulties in accurately measuring the bottom hole pressure using sensors in harsh operating conditions can be resolved by using an Unscented Kalman Filter (UKF) for estimation. In oil fields where input disturbance (total supply of gas) is not measured, UKF can also be used for disturbance estimation. Increased total oil production due to optimization leads to increased profit.

Keywords: gas lift, MPC, oil production, optimization, Unscented Kalman filter.

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Authors: Suman Kumar Saha, Rajib Kar, Durbadal Mandal, S. P. Ghoshal

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This paper demonstrates the application of craziness based particle swarm optimization (CRPSO) technique for designing the 8th order low pass Infinite Impulse Response (IIR) filter. CRPSO, the much improved version of PSO, is a population based global heuristic search algorithm which finds near optimal solution in terms of a set of filter coefficients. Effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, real coded genetic algorithm (RGA) and particle swarm optimization (PSO). Simulation results affirm that the proposed algorithm CRPSO, outperforms over its counterparts not only in terms of quality output i.e. sharpness at cut-off, pass band ripple, stop band ripple, and stop band attenuation but also in convergence speed with assured stability.

Keywords: IIR Filter, RGA, PSO, CRPSO, Evolutionary Optimization Techniques, Low Pass (LP) Filter, Magnitude Response, Pole-Zero Plot, Stability.

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Authors: Bahareh Amirjabbari, Nadia Bhuiyan

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In recent decades, the lean methodology, and the development of its principles and concepts have widely been applied in supply chain management. One of the most important strategies of being lean is having efficient inventory within the chain. On the other hand, managing inventory efficiently requires appropriate management of safety stock in order to protect against increasing stretch in the breaking points of the supply chain, which in turn can result in possible reduction of inventory. This paper applies a safety stock cost minimization model in a manufacturing company. The model results in optimum levels and locations of safety stock within the company-s supply chain in order to minimize total logistics costs.

Keywords: Cost, efficient inventory, optimization, safety stock, supply chain

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Authors: C. Rajan, K. Geetha, C. Rasi Priya, R. Sasikala

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Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.

Keywords: Ant colony optimization algorithm, Genetic algorithm, naturally inspired algorithms and particle swarm optimization algorithm.

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Authors: Christian Endisch, Christoph Hackl, Dierk Schröder

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This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach.

Keywords: System identification, dynamic neural network, recurrentneural network, GDNN, optimization, Levenberg Marquardt, realtime recurrent learning, network pruning, quasi-online learning.

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Authors: S. Moshaf, Z. Hamidi-Esfahani, M. H. Azizi

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Xanthan gum is one of the major commercial biopolymers. Due to its excellent rheological properties xanthan gum is used in many applications, mainly in food industry. Commercial production of xanthan gum uses glucose as the carbon substrate; consequently the price of xanthan production is high. One of the ways to decrease xanthan price, is using cheaper substrate like agricultural wastes. Iran is one of the biggest date producer countries. However approximately 50% of date production is wasted annually. The goal of this study is to produce xanthan gum from waste date using Xanthomonas campestris PTCC1473 by submerged fermentation. In this study the effect of three variables including phosphor and nitrogen amount and agitation rate in three levels using response surface methodology (RSM) has been studied. Results achieved from statistical analysis Design Expert 7.0.0 software showed that xanthan increased with increasing level of phosphor. Low level of nitrogen leaded to higher xanthan production. Xanthan amount, increasing agitation had positive influence. The statistical model identified the optimum conditions nitrogen amount=3.15g/l, phosphor amount=5.03 g/l and agitation=394.8 rpm for xanthan. To model validation, experiments in optimum conditions for xanthan gum were carried out. The mean of result for xanthan was 6.72±0.26. The result was closed to the predicted value by using RSM.

Keywords: Optimization, RSM, Waste date, Xanthan gum, Xanthomonas Campestris

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Authors: Angelica I. Aviles, Pilar Sobrevilla, Alicia Casals

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Compensating physiological motion in the context of minimally invasive cardiac surgery has become an attractive issue since it outperforms traditional cardiac procedures offering remarkable benefits. Owing to space restrictions, computer vision techniques have proven to be the most practical and suitable solution. However, the lack of robustness and efficiency of existing methods make physiological motion compensation an open and challenging problem. This work focusses on increasing robustness and efficiency via exploration of the classes of 1−and 2−regularized optimization, emphasizing the use of explicit regularization. Both approaches are based on natural features of the heart using intensity information. Results pointed out the 1−regularized optimization class as the best since it offered the shortest computational cost, the smallest average error and it proved to work even under complex deformations.

Keywords: Motion Compensation, Optimization, Regularization, Beating Heart Surgery, Ill-posed problem.

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Authors: Leila Jafari, Viliam Makis

Abstract:

In this paper, the joint optimization of the economic manufacturing quantity (EMQ), safety stock level, and condition-based maintenance (CBM) is presented for a partially observable, deteriorating system subject to random failure. The demand is stochastic and it is described by a Poisson process. The stochastic model is developed and the optimization problem is formulated in the semi-Markov decision process framework. A modification of the policy iteration algorithm is developed to find the optimal policy. A numerical example is presented to compare the optimal policy with the policy considering zero safety stock.

Keywords: Condition-based maintenance, economic manufacturing quantity, safety stock, stochastic demand.

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Authors: M.Rajasimman, S.Subathra

Abstract:

In this work, statistical experimental design was applied for the optimization of medium constituents for Gentamicin production by Micromsonospora echinospora subs pallida (MTCC 708) in a batch reactor and the results are compared with the ANN predicted values. By central composite design, 50 experiments are carried out for five test variables: Starch, Soya bean meal, K2HPO4, CaCO3 and FeSO4. The optimum condition was found to be: Starch (8.9,g/L), Soya bean meal (3.3 g/L), K2HPO4 (0.8 g/L), CaCO3 (4 g/L) and FeSO4 (0.03 g/L). At these optimized conditions, the yield of gentamicin was found to be 1020 mg/L. The R2 values were found to be 1 for ANN training set, 0.9953 for ANN test set, and 0.9286 for RSM.

Keywords: Gentamicin, optimization, Micromonospora echinospora, ANN, RSM

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Authors: Sidhartha Panda, C. Ardil

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Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, particle swarm optimization (PSO) technique is applied to coordinately design multiple power system stabilizers (PSS) in a multi-machine power system. The design problem of the proposed controllers is formulated as an optimization problem and PSO is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented for various severe disturbances and small disturbance at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations.

Keywords: Low frequency oscillations, Particle swarm optimization, power system stability, power system stabilizer, multimachine power system.

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Authors: Ali Nadi, Ali Edrissi

Abstract:

Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: Disaster management, real-time demand, reinforcement learning, relief demand.

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Authors: Ε. Giovanis

Abstract:

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

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Authors: Giovanni Incerti

Abstract:

The motion planning procedure described in this paper has been developed in order to eliminate or reduce the residual vibrations of electromechanical positioning systems, without augmenting the motion time (usually imposed by production requirements), nor introducing overtime for vibration damping. The proposed technique is based on a suitable choice of the motion law assigned to the servomotor that drives the mechanism. The reference profile is defined by a Bezier curve, whose shape can be easily changed by modifying some numerical parameters. By means of an optimization technique these parameters can be modified without altering the continuity conditions imposed on the displacement and on its time derivatives at the initial and final time instants.

Keywords: Servomechanism, residual vibrations, motion optimization.

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Authors: Khaled S. Abdallah, Abdel-Aziz M. Mohamed

Abstract:

Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.

Keywords: Modeling, truck rental, supply chains management, simulation.

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Authors: C. Temaneh Nyah

Abstract:

The analysis of electromagnetic environment using deterministic mathematical models is characterized by the impossibility of analyzing a large number of interacting network stations with a priori unknown parameters, and this is characteristic, for example, of mobile wireless communication networks. One of the tasks of the tools used in designing, planning and optimization of mobile wireless network is to carry out simulation of electromagnetic environment based on mathematical modelling methods, including computer experiment, and to estimate its effect on radio communication devices. This paper proposes the development of a statistical model of electromagnetic environment of a mobile wireless communication network by describing the parameters and factors affecting it including the propagation channel and their statistical models.

Keywords: Electromagnetic Environment, Statistical model, Wireless communication network.

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Authors: Y. Abdelrazig, R. Moses

Abstract:

Roadway planning and design is a very complex process involving five key phases before a project is completed; planning, project development, final design, right-of-way, and construction. The planning phase for a new roadway transportation project is a very critical phase as it greatly affects all latter phases of the project. A location study is usually performed during the preliminary planning phase in a new roadway project. The objective of the location study is to develop alignment alternatives that are cost efficient considering land acquisition and construction costs. This paper describes a methodology to develop optimal preliminary roadway alignments utilizing spatial-data. Four optimization criteria are taken into consideration; roadway length, land cost, land slope, and environmental impacts. The basic concept of the methodology is to convert the proposed project area into a grid, which represents the search space for an optimal alignment. The aforementioned optimization criteria are represented in each of the grid’s cells. A spatial-data optimization technique is utilized to find the optimal alignment in the search space based on the four optimization criteria. Two case studies for new roadway projects in Duval County in the State of Florida are presented to illustrate the methodology. The optimization output alignments are compared to the proposed Florida Department of Transportation (FDOT) alignments. The comparison is based on right-of-way costs for the alignments. For both case studies, the right-of-way costs for the developed optimal alignments were found to be significantly lower than the FDOT alignments.

Keywords: Optimization, planning, roadway alignment, FDOT.

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Authors: Saria Abed, Tahar Khir, Ammar Ben Brahim

Abstract:

A thermo-exergy optimization of a gas turbine cycle with two different regenerator designs is established. A comparison was made between the performance of the two regenerators and their roles in improving the cycle efficiencies. The effect of operational parameters (the pressure ratio of the compressor, the ambient temperature, excess of air, geometric parameters of the regenerators, etc.) on thermal efficiencies, the exergy efficiencies, and irreversibilities were studied using thermal balances and quantitative exegetic equilibrium for each component and for the whole system. The results are given graphically by using the EES software, and an appropriate discussion and conclusion was made.

Keywords: Exergy efficiency, gas turbine, heat transfer, irreversibility, optimization, regenerator, thermal efficiency.

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Authors: Mohamed Ghorab

Abstract:

Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO₂ optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO₂ emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO₂ emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.  

Keywords: Distributed energy resources, network energy system, optimization, microgeneration system.

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Authors: C. Rajan, K. Geetha, C. Rasi Priya, S. Geetha

Abstract:

Nature is the immense gifted source for solving complex problems. It always helps to find the optimal solution to solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide research area of networks which has set of independent nodes. The characteristics involved in MANET’s are Dynamic, does not depend on any fixed infrastructure or centralized networks, High mobility. The Bio-Inspired algorithms are mimics the nature for solving optimization problems opening a new era in MANET. The typical Swarm Intelligence (SI) algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf Search Algorithm (WSA) and so on. This work mainly concentrated on nature of MANET and behavior of nodes. Also it analyses various performance metrics such as throughput, QoS and End-to-End delay etc.

Keywords: Ant Colony Algorithm, Artificial Bee Colony algorithm, Bio-Inspired algorithm, Modified Termite Algorithm.

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Authors: Yunyong Guo, Sudhakar Ganti, Bryan Guo

Abstract:

The rapid growth of telehealth Internet of Things (IoT) devices has raised concerns about energy consumption and efficient data processing. This paper presents an energy-efficient model that integrates telehealth IoT devices with a fog and cloud computing-based platform, offering a sustainable and robust solution to overcome these challenges. Our model employs fog computing as a localized data processing layer while leveraging cloud computing for resource-intensive tasks, significantly reducing energy consumption. We incorporate adaptive energy-saving strategies. Simulation analysis validates our approach's effectiveness in enhancing energy efficiency for telehealth IoT systems integrated with localized fog nodes and both private and public cloud infrastructures. Future research will focus on further optimization of the energy-saving model, exploring additional functional enhancements, and assessing its broader applicability in other healthcare and industry sectors.

Keywords: Energy-efficient, fog computing, IoT, telehealth.

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Authors: M. R. AlRashidi, K. M. El-Naggar, M. F. AlHajri

Abstract:

A new technique based on Pattern search optimization is proposed for estimating different solar cell parameters in this paper. The estimated parameters are the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor. The proposed approach is tested and validated using double diode model to show its potential. Performance of the developed approach is quite interesting which signifies its potential as a promising estimation tool.

Keywords: Solar Cell, Parameter Estimation, Pattern Search.

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Authors: K. C. Meher, R. K. Swain, C. K. Chanda

Abstract:

This paper gives an intuition regarding glowworm swarm optimization (GSO) technique to solve dynamic economic dispatch (DED) problems of thermal generating units. The objective of the problem is to schedule optimal power generation of dedicated thermal units over a specific time band. In this study, Glowworm swarm optimization technique enables a swarm of agents to split into subgroup, exhibit simultaneous taxis towards each other and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. The feasibility of the GSO method has been tested on ten-unit-test systems where the power balance constraints, operating limits, valve point effects, and ramp rate limits are taken into account. The results obtained by the proposed technique are compared with other heuristic techniques. The results show that GSO technique is capable of producing better results.

Keywords: Dynamic economic dispatch, Glowworm swarm optimization, Luciferin, Valve–point loading effect, Ramp rate limits.

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Authors: Magdi M. Nabi, Ding-Li Yu

Abstract:

This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.

Keywords: Chylla-Haase reactor, RBF neural network modelling, model predictive control.

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Authors: Rashid S. S., Alam M. Z.

Abstract:

A statistical optimization of the saccharification process of EFB was studied. The statistical analysis was done by applying faced centered central composite design (FCCCD) under response surface methodology (RSM). In this investigation, EFB dose, enzyme dose and saccharification period was examined, and the maximum 53.45% (w/w) yield of reducing sugar was found with 4% (w/v) of EFB, 10% (v/v) of enzyme after 120 hours of incubation. It can be calculated that the conversion rate of cellulose content of the substrate is more than 75% (w/w) which can be considered as a remarkable achievement. All the variables, linear, quadratic and interaction coefficient, were found to be highly significant, other than two coefficients, one quadratic and another interaction coefficient. The coefficient of determination (R2) is 0.9898 that confirms a satisfactory data and indicated that approximately 98.98% of the variability in the dependent variable, saccharification of EFB, could be explained by this model.

Keywords: Face centered central composite design (FCCCD), Liquid state bioconversion (LSB), Palm oil mill effluent, Trichoderma reesei RUT C-30.

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Authors: L. Mirtskhulava, M. Khunjgurua, N. Lomineishvili, K. Bakuria

Abstract:

Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: Exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability.

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

Abstract:

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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