Maximization of the steam efficiency of a multiple stage evaporator employed for concentrating black liquor in pulp and paper mills carries immense significance and relevance in today's scenario. Nonlinear mathema...
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Maximization of the steam efficiency of a multiple stage evaporator employed for concentrating black liquor in pulp and paper mills carries immense significance and relevance in today's scenario. Nonlinear mathematical models of heptads' effects backward feed flow with various energy saving schemes namely, steam-split, feed-split, feed-preheating and their hybrid operations have been developed. The steam economy as a cost function translates the problem into a nonlinear optimal search problem. The mass and heat balance equations act as nonlinear equality constraints while vapor temperatures and liquor flows appear as inequality constraints. The formulated problem has been solved efficiently to attain optimal solution using genetic algorithm approach which demonstrates advantages of convergence and relative less sensitivity towards initial values versus conventional algorithms. The simulations indicate that a hybrid of steam-split, feed-split and feed-preheating process arrangements with backward feed flow could provide the highest heat transfer across evaporator effects with an optimum steam economy of 6.47 and consumption of 6541.93 kg/h. (C) 2017 Elsevier Inc. All rights reserved.
Methods for searching for the optimum chiller configuration for a heat source plant were examined. In addition to the genetic algorithm (GA), a hybrid of the GA and the Nelder-Mead downhill simplex method was develope...
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Methods for searching for the optimum chiller configuration for a heat source plant were examined. In addition to the genetic algorithm (GA), a hybrid of the GA and the Nelder-Mead downhill simplex method was developed. By using the Nelder-Mead downhill simplex method, fewer calculations were required to determine the optimum chiller configuration. Due to applying the GA, the calculations for the developed system did not become trapped in a local optimum, and the algorithm could then determine the global optimum after a few iterations of the Nelder-Mead downhill simplex method. The performance of the developed system was examined for the heating/cooling energy plant of a building;the plant consisted of a centrifugal chiller, a gas-fired absorption chiller, air-source heat pumps, and boilers. It was confirmed that the ability of the GA to find the optimum configuration for the chiller was improved by combining the GA with the Nelder-Mead downhill simplex method. (C) 2013 Published by Elsevier Ltd.
We searched for appropriate adsorption geometries of a MoO(2)Cl(2)precursor on a -H terminated beta-SiO(2)surface using genetic algorithm (GA). The adsorption geometries were configured by translating and rotating the...
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We searched for appropriate adsorption geometries of a MoO(2)Cl(2)precursor on a -H terminated beta-SiO(2)surface using genetic algorithm (GA). The adsorption geometries were configured by translating and rotating the precursor located near the surface. Six parameters decided the translation and rotation of the precursor along and aroundX,Y, andZaxes, and the six parameters were optimized by using the GA to search for energetically favorable adsorption geometries. For accurate and fast convergence of the GA, a dataset of adsorption geometry and the adsorption energy pairs was collected by grid search. Using this dataset, the hyper-parameters for the GA were optimized to search for the energetically favorable adsorption geometries. The GA found more energetically favorable adsorption geometries than the grid search with less computation time. The GA would be applicable to finding appropriate adsorption geometries of other types of precursors and surfaces.
Nowadays, we live an unprecedented evolution in cloud computing technology that coincides with the development of the vast amount of complex interdependent data which make up the scientific workflows. All these circum...
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Nowadays, we live an unprecedented evolution in cloud computing technology that coincides with the development of the vast amount of complex interdependent data which make up the scientific workflows. All these circumstances developments have made the issue of workflow scheduling very important and of absolute priority to all overlapping parties as the provider and customer. For that, work must be focused on finding the best strategy for allocating workflow tasks to available computing resources. In this paper, we consider the scientific workflow scheduling in cloud computing. The main role of our model is to optimize the time needed to run a set of interdependent tasks in cloud and in turn reduces the computational cost while meeting deadline and budget. To this end, we offer a hybrid approach based on genetic algorithm for modelling and optimizing a workflow-scheduling problem in cloud computing. The heterogeneous earliest finish time (HEFT), an heuristic model, intervenes in the generation of the initial population. Based on results obtained from our simulations using real-world scientific workflow datasets, we demonstrate that the proposed approach outperforms existing HEFT and other strategies examined in this paper. In other words, experiments show high efficiency of our proposed approach, which makes it potentially applicable for cloud workflow scheduling. For this, we develop a GA-based module that was integrated to the WorkflowSim framework based on CloudSim.
This article compares genetic algorithm (GA) and genetic programming (GP) for system modeling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr(13)) was predicted by...
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This article compares genetic algorithm (GA) and genetic programming (GP) for system modeling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr(13)) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analyzed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.
Background: Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics - the practice of comparing genomic sequences from different species - plays an increasingly important role in understan...
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Background: Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics - the practice of comparing genomic sequences from different species - plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE ( Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program. Results: We tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy ( poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly;but significantly,
Integrating more features into one product makes the product more attractive, thereby increasing the product's initial sales;however, after having worked with the high-feature product, customers become dissatisfie...
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Integrating more features into one product makes the product more attractive, thereby increasing the product's initial sales;however, after having worked with the high-feature product, customers become dissatisfied with the usability problems caused by too many features. This phenomenon is called "feature fatigue". Feature fatigue will lead to negative Word-Of-Mouth, which damages the brand's long-term profit and ultimately decreases the manufacturer's customer equity (CE). To alleviate feature fatigue, it is imperative for designers to decide what features should be integrated to balance initial revenue and long-term profit so as to maximize CE. In this paper, a novel approach based on the SIR epidemic model and a genetic algorithm is proposed to help designers find an optimal feature combination that maximizes CE. Firstly, the SIR epidemic model is utilized to analyze customer purchase behavior under different feature combinations. CE can thus be calculated according to customer purchase analysis. Then a genetic algorithm is adopted to search an optimal feature combination, in which CE is used as the fitness function. Finally, a case example is illustrated to prove the effectiveness of the proposed approach.
A genetic algorithm for the optimization of composite laminates is proposed in this work. The well-known roulette selection criterion, one-point crossover operator, and uniform mutation operator are used in this genet...
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A genetic algorithm for the optimization of composite laminates is proposed in this work. The well-known roulette selection criterion, one-point crossover operator, and uniform mutation operator are used in this genetic algorithm to create the next population. To improve the hill-climbing capability of the algorithm, adaptive mechanisms designed to adjust the probabilities of the crossover and mutation operators are included, and the elite strategy is enforced to ensure the quality of the optimum solution. The proposed algorithm includes a new operator called the elite comparison, which compares and uses the differences in the design variables of the two best solutions to find possible combinations. This genetic algorithm is tested in four optimization problems of composite laminates. Specifically, the effect of the elite comparison operator is evaluated. Results indicate that the elite comparison operator significantly accelerates the convergence of the algorithm, which thus becomes a good candidate for the optimization of composite laminates.
The majority of the methods used for the radioelectric characterization of the microstrip antennas require long and tiresome numerical calculations in general. The goal of this study is to use simple and precise equiv...
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The majority of the methods used for the radioelectric characterization of the microstrip antennas require long and tiresome numerical calculations in general. The goal of this study is to use simple and precise equivalent electric models, making it possible to take account of the whole of the geometrical, electric and technological characteristics of the antennas and their feed. From the equivalent model of the transmission line, the synthesis of microstrip antennas in non-periodic arrays for various configurations of feed by microstrip line is elaborate. The method of synthesis of these arrays is developed by employing a stochastic technique of optimization based on the genetic algorithm.
In this study, optimal balancing of a planar articulated mechanism is investigated to minimize the shaking force and moment fluctuations. Balancing of a four-bar mechanism is formulated as an optimization problem. On ...
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In this study, optimal balancing of a planar articulated mechanism is investigated to minimize the shaking force and moment fluctuations. Balancing of a four-bar mechanism is formulated as an optimization problem. On the other hand, an objective function based on the sub-components of shaking force and moment is constituted, and design variables consisting of kinematic and dynamic parameters are defined. genetic algorithm is used to solve the optimization problem under the appropriate constraints. By using commercial simulation software, optimized values of design variables are also tested to evaluate the effectiveness of the proposed optimization process. This work provides a practical method for reducing the shaking force and moment fluctuations. The results show that both the structure of objective function and particularly the selection of weighting factors have a crucial role to obtain the optimum values of design parameters. By adjusting the value of weighting factor according to the relative sensitivity of the related term, there is a certain decrease at the shaking force and moment fluctuations. Moreover, these arrangements also decrease the initiative of mechanism designer on choosing the values of weighting factors.
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