The article introduces the current issue, which contains selected papers from the "Scientific Meeting 'Numerical Methods for Local and Global Optimization: Sequential and Parallel Algorithms,' including o...
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The article introduces the current issue, which contains selected papers from the "Scientific Meeting 'Numerical Methods for Local and Global Optimization: Sequential and Parallel Algorithms,' including one about a local optima smoothing approach for solving global optimization problems, and another suggesting a method for constructing test functions for global optimization.
Continuous flow gas lift is one of the most common artificial lift methods widely used in the oil industry. A continuous volume of high-pressure gas is injected as deep as possible into the tubing, to gasify the oil c...
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Continuous flow gas lift is one of the most common artificial lift methods widely used in the oil industry. A continuous volume of high-pressure gas is injected as deep as possible into the tubing, to gasify the oil column, and thus facilitate the production. If there is no restriction in the amount of injection gas available, sufficient gas can be injected into each oil well to reach maximum production. However, the injection gas available is generally insufficient. An inefficient gas allocation in a field with limited gas supply reduces the revenues, since excessive gas injection is expensive due to the high gas prices and compressing costs. Therefore, it is necessary to assign the injection gas into each well in optimal form to obtain the field maximum oil production rate. The gas allocation optimization can be considered as a maximization of a nonlinear function, which models the total oil production rate for a group of wells. The variables or unknowns for this function are the gas injection rates for each well, which are subject to physical restrictions. In this work a nonlinear optimization technique, based on an objective function with constraints, was implemented to find the optimal gas injection rates. A new mathematical fit to the gas-lift performance curve (GLPC) is presented and the numeric results of the optimization are given and compared with those of other methods published in the specialized literature. The GLPC can be either measured in the field, or alternatively generated by computer simulations, by mean of nodal analysis. The optimization technique proved fast convergence and broad application.
We show that indefinitely preconditioned symmetric Krylev-subspace methods are very efficient for solving linearized KKT systems arising in equality constrained optimization. We give a numerical comparison of various ...
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We show that indefinitely preconditioned symmetric Krylev-subspace methods are very efficient for solving linearized KKT systems arising in equality constrained optimization. We give a numerical comparison of various Krylov subspace methods in three different forms (original system, null-space transformation, range-space transformation). Furthermore, we give a survey of our previous results concerning indefinite preconditioners and merit functions and prove new propositions.
Generalized geometric programming (GGP) is an optimization method in which the objective function and constraints are nonconvex functions. Thus, a GGP problem includes multiple local optima in its solution space. When...
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ISBN:
(纸本)9781595930101
Generalized geometric programming (GGP) is an optimization method in which the objective function and constraints are nonconvex functions. Thus, a GGP problem includes multiple local optima in its solution space. When using conventional nonlinear programming methods to solve a GGP problem, local optimum may be found, or the procedure may be mathematically tedious. To find the global optimum of a GGP problem, a bio-immune-based approach is considered. This study presents an artificial immune system (AIS) including: an operator to control the number of antigen-specific antibodies based on an idiotypic network hypothesis; an editing operator of receptor with a Cauchy distributed random number, and a bone marrow operator used to generate diverse antibodies. The AIS method was tested with a set of published GGP problems, and their solutions were compared with the known global GGP solutions. The testing results show that the proposed approach potentially converges to the global solutions.
The chemical process synthesis problem involves selecting the optimal flowsheet structures and operating parameters to gain the maximum economic profit. In general, the flowsheet structures adopted or not are expresse...
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The chemical process synthesis problem involves selecting the optimal flowsheet structures and operating parameters to gain the maximum economic profit. In general, the flowsheet structures adopted or not are expressed by integer variables and the operation parameters are expressed as continuous variables, and these form a superstructure model. In order to simultaneously optimize the operation parameters and flowsheet structure of the chemical process, the superstructure model formulated is generally a MINLP (mixed integer nonlinear programming) problem. How to solve the above model is the key to optimize the chemical process. A new method which is called the integer variables continuation technique was proposed, which is based on the character of the binary variables(the integer variables and the continuous variables), they can be treated as mere continuous variables by adding some constrains, and then the difficulties brought by the binary variables in the process of optimization can be avoided. Thereby the optimum structure and the optimum operation parameters are determined in a easier way. The HDA process was taken as a case for studying the capabilities of the proposed method. The result proves that the proposed method can treat complex MINLP problems properly.
In order to enhance the scientificity of making the blending plan of refineries, the nonlinear programming (NIP) models of the blending optimization problems was researched in detail, including the formulation of the ...
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In order to enhance the scientificity of making the blending plan of refineries, the nonlinear programming (NIP) models of the blending optimization problems was researched in detail, including the formulation of the objective function and constraints. The simulated annealing algorithm is selected to solve the problem according to the characteristics of the model. Finally, the validity of blending optimization model is verified by the application of a refinery.
The damage-mitigating and life-extending control (DMLEC) based on multiobjective optimization (MOO) is presented by DMLEC policy analysis and synthesis. The progress of solving DMLEC policy by using nonlinear programm...
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The damage-mitigating and life-extending control (DMLEC) based on multiobjective optimization (MOO) is presented by DMLEC policy analysis and synthesis. The progress of solving DMLEC policy by using nonlinear programming is analysed, which is combined with the dynamics modelling, the structural analysis of turbine blade and the damage modelling of a certain liquid-propellant rocket engine. Then, the start-up process of the LRE under the control of DMLEC is simulated. The result indicates that the damage of blade is mitigated and the service life of LRE is extended considerably while the system performance is only debased a little.
The authors propose a super-resolution time difference of arrival (TDOA) estimator. In the proposed approach TDOA estimation is first converted into a parameter estimation problem of sinusoidal signals with low-pass e...
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The authors propose a super-resolution time difference of arrival (TDOA) estimator. In the proposed approach TDOA estimation is first converted into a parameter estimation problem of sinusoidal signals with low-pass envelope. Then TDOAs are estimated based on eigenanalysis and sequential quadratic programming (ESQP). Compared with the conventional approaches, the proposed method is applicable to signals with narrowband spectra. Furthermore, it does not require a priori knowledge of the transmitted signal. Simulation results demonstrate the improved performance of the proposed method when compared to the conventional correlation and MUSIC algorithm.
作者：
Mijangos, EUPV
EHU Dept Appl Math & Stat & Operat Res Bilbao 48080 Spain
The minimization of a nonlinear function with linear and nonlinear constraints and simple bounds can be performed by minimizing an augmented Lagrangian function, including only the nonlinear constraints. This procedur...
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The minimization of a nonlinear function with linear and nonlinear constraints and simple bounds can be performed by minimizing an augmented Lagrangian function, including only the nonlinear constraints. This procedure is particularly interesting when the linear constraints are flow conservation equations, as there exist efficient techniques for solving nonlinear network problems. It is then necessary to estimate their multipliers, and variable reduction techniques can be used to carry out the successive minimizations. This work analyzes the possibility of estimating the multipliers of the nonlinear constraints using Newton-like methods. Also, an algorithm is designed to solve nonlinear network problems with nonlinear inequality side constraints, which combines first and superlinear-order multiplier methods. The computational performance of this method is compared with that of MINOS 5.5.
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