Search

Condensate stabilization is a process where hydrocarbon condensate recovered from natural gas reservoirs is processed to meet the required storage, transportation, and export specifications. The pr...

Rivers are an integral part of the hydrological cycle and are the major geological agents which erode the continents and transport water and sediments to the oceans. Thus rivers act an important li...

This study aims to enhance the transesterification process to convert supercritical methanol (SCM) to biodiesel by applying artificial neural network-coupled genetic algorithm (ANN-GA) optimization...

In the present work, the Adaptive-Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.

The study deals with the design and optimization of external and internal geometry of micro-wind turbines blades. A specified objective function which consists of the power coefficient and the starting time was defined and the genetic algorithm optimization technique in conjunction with the blade-element momentum theory was adopted to find the geometry of the blades including the distributions of the chord, the twist angle and also the shell thickness. Moreover, the allowable stress of the blades was considered as a constraint to the objective function. Results show that a reasonable compromise is achievable such that the starting time of the blades reduces noticeably in return for a small drop in the power coefficient. The significant improvement of the hollow blades over the solid ones indicates that the power coefficient and the starting performance could be improved through the appropriate distributions of the considered decision variables, i.e. the chord, the twist angle and also the shell th...

Earthquakes have caused more than 50,000 lives and $4,000 million economic losses over a period of 20 years in Iran. Having accurate risk assessment and effective future plans can significantly reduce these losses. In this paper, two key parameters, which are effective in both human and economic loss reduction, have been defined, one of which is the number of saved lives before and after taking mitigation measures and the other one is the household expense ratio related to the cost of earthquake mitigation measures. For this reason, Shiraz city, located in the southwest of Iran, has been selected for evaluation. Detailed building taxonomy is performed, and for seismic vulnerability evaluation, vulnerability curves exclusively derived for Iranian buildings are used. Multi-objective genetic algorithm optimization method is conducted to find a balance between the costs of seismic mitigation measures and number of casualties. This balance proposes the extent of mitigation actions which is representative of a specific layer of risk and is identified as a set of Pareto Front for all types of buildings. The optimal risk layer can play a vital role in shaping management policies for decision-makers in order to reduce the inherent risk of country.

Economic statistical designs aim at minimizing the cost of process monitoring when a specific scenario or a set of estimated process and cost parameters is given. However, in practical situations the process may be affected by more than one scenario which may lead to severe cost penalties for upsetting the true scenario. This paper presents the robust economic statistical design (RESD) of the T^2 chart to reduce the monetary losses when there are multiple distinct scenarios. The genetic algorithm optimization method is employed here to minimize the total expected monitoring cost across all distinct scenarios. Through two numerical examples the proposed method is illustrated. Simulation studies indicate that the robust economic statistical designs should be encouraged in practice.

Recently, researchers have tried to design the T2 chart economically to achieve the minimum possible quality cost; however, when T2 chart is designed, it is important to consider multiple scenarios. This research presents the robust economic designs of the T2 chart where there is more than one scenario. An illustrative example is used to demonstrate the effect of the model parameters on the optimal designs. The genetic algorithm optimization method is employed to obtain the optimal designs. Simulation studies show that the robust economic designs of T2 chart are more effective than traditional economic design in practice.

Yanmaz, Ersin (Balikesir Author), In this work, the EC-GA method, a hybrid 4D-QSAR approach that combines the electron conformational (EC) and genetic algorithm optimization (GA) methods, was applied in order to explain pharmacophore (Pha) and predict anti-HIV-1 activity by studying 115 compounds in the class of 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) derivatives as non-nucleoside reverse transcriptase inhibitors (NNRTIs). The series of NNRTIs were partitioned into four training and test sets from which corresponding quantitative structure-activity relationship (QSAR) models were constructed. Analysis of the four QSAR models suggests that the three models generated from the training and test sets used in previous works yielded comparable results with those of previous studies. Model 4, the data set of which was partitioned randomly into two training and test sets with 11 descriptors, including electronical and geometrical parameters, showed good statistics both in the regression (r(training)(2) = 0.867, r(test)(2) = 0.923) and cross-validation (q(2) = 0.811, q(ext1)(2) = 0.909, q(ext2)(2) = 0.909) for the training set of 80 compounds and the test set of 27 compounds. The prediction of the anti-HIV-1 activity of HEPT compounds by means of the EC-GA method allowed for a quantitatively consistent QSAR model. In addition, eight novel compounds never tested experimentally have been designed theoretically using model 4., Erciyes University (BAP) FBA-06-04

In this article, load frequency control of an interconnected power system is achieved by two methods. The first is based on the classical h∞ control method and is subjected to linear matrix inequalities. The second uses a proportional-integral controller that is tuned by genetic algorithm optimization and is also subjected to the same linear matrix inequalities in order to obtain robustness against disturbances. Both controllers are tested on a two-area power system with three scenarios of load disturbances, as well as on power systems in a deregulated environment to demonstrate their robust performances.

A genetic algorithm (GA) is used as a search tool to determine the optimal geometry for maximizing bandwidth or efficiency in electrically small, volumetric antenna structures. Folded shaped helix structures, including the folded conical helix and the folded spherical helix, are placed in the same population and optimized using GA. A dense-top, spherical helix-like structure is chosen for its optimal performance.

In this article, the tuning of feedback gains of a variable structure load frequency controller by genetic algorithm optimization of selected performance indices is presented. The load frequency control of a single- and multi-area power system has been carried out. The effect of generator rate constraints has been studied. The system performance against step load variations has been simulated and compared with the conventional trial-and-error method. The results show that the system dynamic performance has been improved, the control effort has been reduced, the controller is robust against large disturbances, and the system is insensitive to wide parameter variations.

Mixed gas working fluids can be used within Joule-Thomson devices to achieve a greater refrigeration effect than is possible with a pure substance. Lower temperatures and higher cooling powers can be obtained with appropriate mixtures, resulting in lower operating pressures and mass flow rates. This paper describes a computational tool that allows the composition of gas mixtures to be optimized for a particular operating condition. Automated optimization algorithms are described, evaluated, and validated for two- and three-component mixtures consisting of nitrogen and various hydrocarbons. A genetic optimization algorithm was found to be the most robust and reliable technique and was adapted to this application. Subsequently, the optimization space was extended to a larger number of components for a range of load temperatures and operating pressures. The performance of optimized hydrocarbon gas mixtures is compared with that of nonflammable hydrofluorocarbon mixtures for a range of load temperatures and s...

With the advent of smaller, less expensive, and generally more effective computers, simulation models have become increasingly popular tools for solving engineering problems. More and more, engineers are turning to simulation environments to achieve increased system performance at a reduced cost. One such environment found to be very effective is WITNESS, a modeling program developed by AT&T and Istel. This article describes an effort to link a genetic algorithm with WITNESS to optimize a model of a manufacturing process called filament winding. Results show the genetic algorithm to be an effective, optimization tool for use with WITNESS models.

A significant proportion of building investment expenditure goes to replacement expenditure for organizations owning a large building stock or portfolio. Over the years, researchers have attempted to develop asset replacement models to aid decision-making in building portfolio management, based upon' a statistical or an heuristic approach. This study attempts to use genetic algorithms to develop models for forecasting long term asset replacement strategies, aiming at smoothing fluctuations of expenditure and resource requirements, and most importantly minimizing the total maintenance and replacement costs. Scenarios are presented to demonstrate how these can be achieved. Further refinement for practical application of the models is also presented.

Numerical flow analysis in turbomachinery is an indispensable tool for water turbines design. Following the design of axial runner, the energetic and cavitation characteristics can be predicted usi...