This paper presents the cost optimization of an underground gas storage (UGS), designed from lined rock caverns (LRCs). The optimization is performed by the non-linear programming (NLP) approach. For this purpose, the...
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ISBN:
(纸本)9781845644642
This paper presents the cost optimization of an underground gas storage (UGS), designed from lined rock caverns (LRCs). The optimization is performed by the non-linear programming (NLP) approach. For this purpose, the NLP optimization model OPTUGS was developed. The model comprises the cost objective function, which is subjected to geomechanical and design constraints. It is proposed that the geotechnical problem will be solved simultaneously. In such a way, the optimization enables not only that the solution is optimal, but also that the rock mass achieves enough strength, stability and safety. It is proposed that the optimization will be performed for the phase of the conceptual design. The numerical example at the end of the paper demonstrates the efficiency of the introduced optimization approach.
Enterprises need to develop a process to determine how to find and develop new product ideas and finally, how to successfully introduce them to the marketplace. To address this problem, conceptions of Optimal introduc...
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ISBN:
(纸本)9781424427239
Enterprises need to develop a process to determine how to find and develop new product ideas and finally, how to successfully introduce them to the marketplace. To address this problem, conceptions of Optimal introduction Period and Correlative Profit were presented. Based on the quantitative description of the product life cycle, a non-linear Semi-infinite programming model of new product introduction was proposed. The proposed model was solved by improved Particle Swarm Optimization (PSO) algorithms. Optimal solution of the given example shows that Particle Swarm Optimization has become the hotspot of evolutionary computation because of its excellent performance and simplicity for implement in solving combined optimization problems.
This paper inherits the fundamental ideas of inequality and optimization techniques from the previous work and converts the real-time obstacle avoidance problem into a semi-infinite constrained optimization problem wi...
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ISBN:
(纸本)0780386450
This paper inherits the fundamental ideas of inequality and optimization techniques from the previous work and converts the real-time obstacle avoidance problem into a semi-infinite constrained optimization problem with the help of a delicate mathematical transformation, which leads to an efficient real-time robotics' path planning approach. Motivated by the practical requirements of obstacle representation, a generalized semi-infinite optimization problem (GSOP) with not only intersection but also union operations was proposed and a mathematical solution to it was developed. Simulation results in 3D space have been presented to show its merits.
This paper is devoted to non-linear single path routing problems, which are known to be NP-hard even in the simplest cases. We propose a Best Response algorithm, based on Game Theory, providing single-path routings wi...
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The paper presents approximate analytical solutions of the problem of finding the shape of a plane convex figure that encloses the least area but whose torsion constant and the least second moment of area about any ce...
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Super-resolution Reconstruction (SRR) is technique to increase the spatial resolution of images. It is especially useful for hyperspectral images (HSI), which have good spectral resolution but low spatial resolution. ...
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ISBN:
(纸本)9781509033324
Super-resolution Reconstruction (SRR) is technique to increase the spatial resolution of images. It is especially useful for hyperspectral images (HSI), which have good spectral resolution but low spatial resolution. In this study, we propose an improvement to our previous work and present a novel MAP-MRF (maximum a posteriori-Markov random Fields) based approach for the SRR of HSI. The key point of our approach is to find the abundance maps of an HSI and perform SRR on the abundance maps using MRF based energy minimization, without needing any other additional source of information. In order to do so, first, PCA is used to determine the endmembers. Second, SISAL and fully constraint least squares (FCLS) are used to estimate the abundance maps. Third, in order to find the high resolution abundance maps, the ill-posed inverse SRR problem for abundances is regularized with a MAP-MRF based approach. The MAP-MRF formulation is restricted with the constraints which are specific to the abundances. Using the non-linear programming (NLP) techniques, the convex MAP formulation is minimized and High Resolution (HR) abundance maps are obtained. Then, these maps are used to construct the HR HSI. This improved SRR method is verified on real data sets, and quantitative performance comparison is achieved using PSNR, SSIM and PSNR metrics. Our results indicate that this improved method gives very close results to the original high resolution images, keeps the spectral consistency, and performs better than the compared algorithms.
This paper proposes security constrained economic power dispatch (SCED) of the generators in the presence of secure bilateral transactions for hybrid electricity markets. The proposed non-linear optimization problem c...
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ISBN:
(纸本)9781424417636
This paper proposes security constrained economic power dispatch (SCED) of the generators in the presence of secure bilateral transactions for hybrid electricity markets. The proposed non-linear optimization problem considers simultaneous minimization of deviations from scheduled transactions and minimization of fuel cost of the generators. The impact of bilateral transactions on fuel costs of generators and generation pattern has also been studied. The proposed technique has been applied on IEEE 24-bus reliability test system (IRTS).
Energy consumption optimization constitutes one of the main challenges in wireless sensor networks. Most of the power based routing strategies proposed in the literature are based on global power consumption optimizat...
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ISBN:
(纸本)9781424405220
Energy consumption optimization constitutes one of the main challenges in wireless sensor networks. Most of the power based routing strategies proposed in the literature are based on global power consumption optimization rather than maximizing the network lifetime. In this paper, we propose an adaptive routing strategy that maximizes the lifetime of the sensor network, where optimal routes are selected in order to delay, as long as possible, the first energy run out among sensor nodes. The proposed routing strategy also takes into account both the reception and the transmission energy dissipations. Moreover, in order to adapt to the changes that may occur in the network topology or characteristics, we propose a global adaptive framework that dynamically reconfigures routes according to the current network state. The proposed strategy is modelled and simulation results are presented.
Forecasting and Time series techniques are frequently used and play extremely important roles in managerial activities and decision-making processes. Holt-Winters (HW) which is one of the most popular forecasting meth...
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ISBN:
(纸本)9781479960651
Forecasting and Time series techniques are frequently used and play extremely important roles in managerial activities and decision-making processes. Holt-Winters (HW) which is one of the most popular forecasting methods is utilized in cases where data show seasonality and/or trend. The method involves the selection of several parameters for optimum prediction results. Heuristics are usually utilized for optimum parameter selection and it appears to be an open field for improvement. In this study "Spreadsheet modeling" of HW method is improved by minimizing the mean squared error (i.e. prediction error). Since the forecast error is nonlinear function;Holt-Winters parameter optimization in this study is achieved by "Excel nonlinear Solver" and "Differential Evolution Search" techniques. To increase the usability of the spreadsheet modeling advance macro programming techniques are incorporated with the developed models in Microsoft Excel.
Industrial wastewater is one of the crucial environmental concerns. Biosorption is an effective method used to remove toxic heavy metals from wastewater. Biosorption utilizes the capability of biomass to remove metal ...
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ISBN:
(纸本)9781467329453
Industrial wastewater is one of the crucial environmental concerns. Biosorption is an effective method used to remove toxic heavy metals from wastewater. Biosorption utilizes the capability of biomass to remove metal ions from wastewater. The determination of operating conditions, namely, the type and the amount of biomass to be used in the process as well as the processing time, are the key issues in the process. This paper proposes an optimization model for removal of zinc from wastewater. A biomass marine alga, Gracilaria Corticata (GC), is used as biosorbent. Derivation of zinc absorption kinetics model is first provided. A non-linear optimization model is then developed to determine the optimal operating conditions subject to satisfying a desired removal percentage while minimizing the total cost. The experimental results show that GC can be used well for zinc removal and the proposed optimization model can effectively reduce the process cost. The proposed approach is illustrated through a simplified numerical example.
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