Sharif Digital Repository

Here a propeller design method based on a vortex lattice algorithm is developed, and two gradient-based and non-gradient-based optimization algorithms are implemented to optimize the shape and efficiency of two propellers. For the analysis of the hydrodynamic performance parameters, a vortex lattice method was used by implementing a computer code. In the first problem, one of the Sequential Unconstraint Minimization Techniques (SUMT) is employed to minimize the torque coefficient as an objective function, while keeping the thrust coefficient constant as a constraint. Also, chord distribution is considered as a design variable, namely 11 design variables. In the second problem, a modified... 

An experimental based ANN model is constructed to describe the performance of vacuum membrane distillation process for desalination in different operating conditions. The vacuum pressure, feed inlet temperature, concentration of the feed salt aqueous solution, and feed flow rate are the input variables of this process, while response is the permeate flux. The neural network approach is capable for modeling this membrane distillation configuration. The application of Genetic Algorithm to optimize the ANN model parameters was also examined. This is an abstract of a paper presented at the CHISA 2012 - 20th International Congress of Chemical and Process Engineering and PRES 2012 - 15th... 

An experimental based ANN model is constructed to describe the performance of vacuum membrane distillation process for desalination in different operating conditions. The vacuum pressure, the feed inlet temperature, the concentration of the feed salt aqueous solution and the feed flow rate are the input variables of this process, whereas the response is the permeate flux. The neural network approach was found to be capable for modeling this membrane distillation configuration. The application of Genetic Algorithm (GA) to optimize the ANN model parameters was also investigated  

We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model  

In this paper, the fuzzy system has been used for fault detection and diagnosis of a yeast fermentation bioreactor based on measurements corrupted by noise. In one case, parameters of membership functions are selected in a conventional manner. In another case, using certainty factors between normal and faulty conditions the optimal values of these parameters have been obtained through the genetic algorithm. These two cases are compared based on their performances in fault diagnosis of a yeast fermentation bioreactor for three different conditions. The simulation results indicate that the fuzzy-genetic system is superior in multiple fault detection for the conditions where the minimum and... 

Service oriented architecture is based on the provision of services. To enhance the performance of the systems by providing a better combination of services, it is necessary to extract more information compared to the one in the service registry. In this regard, the accomplished works have been focusing on the basic concepts of service-oriented architecture. The service composition is based on the information in service registry, provided by the service provider. Further, centralized combination with insufficient information does not meet the system performance requirements. This solution helps to facilitate resource distribution and reduces tasks of the central unit. In this paper, efforts... 

Employing profiles using variable sample size (VSS) may improve its effectiveness and hence is investigated in this paper. Besides, the implementation cost of a profile is an important factor in determining the main parameters. In this paper, the Lorenzen-Vance function is first extended to model implementation cost of VSS linear profiles. Then, the resulted economic model is solved using a genetic algorithm (GA). The average run length criterion when process is in control, ARL0 and the average run length measure when process goes out of control, ARL1 , is used to evaluate statistical properties of the designed profile. Moreover, a sensitivity analysis on the main parameters of the... 

The present paper introduces a novel two-part self-adaptive technique in designing the genetic algorithm for project scheduling problems. One part of the algorithm includes a self-adaptive mechanism for genetic operators like crossover and mutation. The second part contains another self-adaptive mechanism for genetic parameters such as crossover probability. The parts come in turn repeatedly within a loop feeding each other with the information regarding the performance of operators or parameters. The capability of the method is tested and confirmed in comparison to metaheuristic and exact algorithms based on well-known benchmarks  

The functionalized copper oxide–zinc oxide nanocomposite (FCZN) was synthesized and characterized using Fourier transform infrared, scanning electron microscopy, X-ray diffraction, X-ray fluorescence, and BET. Dye removal from aqueous solution was done in a batch system using FCZN as an adsorbent. The effects of adsorbent dosage, initial dye concentration, pH, temperature, and additive salts on dye removal were investigated. Isotherms, kinetics, and thermodynamics of dye adsorption were studied. Equilibrium and kinetic data were fitted by Langmuir isotherm and pseudo-second-order kinetic, respectively. The thermodynamic data showed that dye adsorption was spontaneous, endothermic, and... 

In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at the core of simulated annealing heuristic. Results of proposed approach have been compared with several well-known methods such as Naïve Bayes, Support Vector Machine, Decision Tree, k-NN, and GBML, and show that our method performs the classification task as well as other famous algorithms. ©... 

In this work, a bi-objective multi-facility location-allocation problem is investigated, in which the locations of the customers and their arrivals are stochastic. We first formulate the problem as a continuous location-allocation model with no constraints on the capacity of the facilities. Then, we develop an approximated discrete model in which the facilities with limited capacities can be located on a set of candidate points. The proposed model has two objective functions that are evaluated using discrete event system simulation. The first objective is to minimize the expected total time the customers spend in the system until their services begin. The time that each customer spends in... 

Optimal queuing system design includes deciding about some variables such as location, capacity, price, and delivery time. In order to develop an optimized system, it is essential to solve the optimization problem for all the decision variables simultaneously. In this paper, a make-to-order system is considered. In this system, some facilities are developed in different points. According to the make-to-order system definition, these facilities keep no inventory from final product. Orders are received from customers and the requested products are assembled. Due to existence of some differences among customers, they are divided into two distinct categories including express and regular... 

Sensor networks have recently emerged as a premier research topic. Sensor networks pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. While designing the sensor networks according to performed computation, the limited number of sensors to cover an area will be considered, so the proper placing of this limited number of sensors will cause costs to reduce regarding to coverage and development of the network in the next stage. In this paper we will present a genetic algorithm to solve the designing issue of the sensor network. The most important... 

A method based on the Genetic Algorithm (GA) was developed to study the phase behavior of multicomponent and multiphase systems. Upon application of the GA to the thermodynamic models which are commonly used to study the VLE, VLLE and LLE phase equilibria, the physically meaningful values for the Binary Interaction Parameters (BIP) of the models were obtained. Using the method proposed in this work the activity coefficients for components at infinite dilution, obtained from the local composition based models, can be accurately predicted comparing to the experimental data available in the literature. In this work, a Global Optimization Procedure (GOP) based on the GA was developed to obtain... 

For large and complex reacting systems, computational efficiency becomes a critical issue in process simulation, optimization and model-based control. Mechanism simplification is often a necessity to improve computational speed. We present a novel approach to simplification of reaction networks that formulates the model reduction problem as an optimization problem and solves it using genetic algorithm (GA).The aim of simplification kinetics modeling is to derive the simplest reaction system, which retains the essential features of the full system. Numerical results for H2/O2 combustion reaction mechanism illustrate the potential and proficiency of this approach  

4E analysis is used on a Brayton-Rankine combined cycle power plant (CCPP) with a dual pressure heat recovery steam generation (HRSG) system. A multi-objective genetic-based evolutionary optimization has been used to estimate the most optimal exergy efficiency status, exergy cost reduction, carbon emission reduction, and NOx emission reduction. For the validation of the data, the simulation results are compared with the plant's data. This study investigates the effect of every decisive parameter on the objective performance parameters of the CCPP. The primary estimated results are the emission rates, efficiencies, and the exergoeconomic cost of the system. At the optimum operational state,... 

Nowadays, deliberate or unwanted fire incidents have created much attention to the behavior of structures against these types of events. Since the properties of structural members are influenced by the increase in the temperature of the members, it is more difficult to predict the general and local behavior of the structures during the fire. In this research, a method has been proposed to calculate the critical temperature in two-dimensional structures at its collapse with desirable accuracy. In this process, the upper-bound theory of plastic analysis is used. The plastic analysis is performed by applying the initial fire scenario to the structure, and its collapse load factor with the... 

This study is focused on the development of a systematic computational approach which implements Genetic Algorithm (GA) to find the optimal rigorous kinetic models. A general Kinetic model for hydrogenolysis of dibenzothiophene (DBT) based on Langmuir Hinshelwood type has been obtained from open literature. This model consists of eight continuous parameters(e.g., Arrhenus and Van't Hoff parameters) and six discrete parameters representing the order of the reaction with respect to each concentration. The optimal value of these parameters have been obtained based on Genetic Algorithm. Furthermore, the best type of Genetic operators and their corresponding parameters for this type of problems... 

Shape optimization of a submarine for minimized noise emission has been the objective of this work. Boundary element method and finite element methods have been employed to determine the acoustic field around the object. A combined neural network and genetic algorithm scheme is developed to find the optimum external geometric ratios of the submarine for a minimized emitted acoustic energy in certain reference points. The obtained optimum geometric values stay in normal range for minimum hydrodynamic forces and show a close agreement with the trend of change for models coming to operation. © 2004 IEEE  

In this paper we discuss an improved method for the calculation of Available Transfer Capability (ATC) in a restructured power system. The ATC calculation is performed through an optimal power flow approach. The objective function is to maximize the sum of the receiving-end load in a specified area as well as to send generation to other control areas while a minimum cost of generation is continuously achieved. The constraints are ac power flow equations and system operation limits. It is assumed that the generation cost curve of each generator is available. A genetic algorithm is used for the optimization process. An IEEE 30 bus test system is used for testing the proposed algorithm and the...