This paper presents a generalization of the ABC compensation theory based on mathematical optimization which integrates the neutral losses into the optimization model. The classical ABC theory is shown to be a particu...
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This paper presents a generalization of the ABC compensation theory based on mathematical optimization which integrates the neutral losses into the optimization model. The classical ABC theory is shown to be a particular case of the presented generalized compensation approach. The main contribution of this paper is the generalization of the ABC theory considering conflictive objectives which include among them the minimization of the network losses. The work presented here takes. into account not only conventional balanced and pure sinusoidal voltage source but also unbalances and harmonic distortions on the voltage. Four different compensation objectives are studied: invariant instantaneous power, constant power, unity power factor, and pure sinusoidal current. Through these four cases, the flexibility and simplicity of implementation of this approach is demonstrated. In addition, the proposed compensation strategy optimizes the line currents and therefore minimizes the network losses. The main contributions A simulation study which considers the switching effect, the control of the DC link and the size of the shunt compensator is presented. Experimental tests are carried out to verify the theory. (C) 2012 Elsevier B.V. All rights reserved.
The paper presents the cost optimization of an underground gas storage (UGS), designed from lined rock caverns (LRC). The optimization is performed by the non-linear programming (NLP) approach. For this purpose, the N...
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The paper presents the cost optimization of an underground gas storage (UGS), designed from lined rock caverns (LRC). 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. The geotechnical problem is proposed to be solved simultaneously. Geomechanical rock mass parameters are determined from geological conditions of a selected suitable UGS location and a special FE model is generated. The rock mass strength stability and safety of the system are then analyzed for various combinations between different design parameters like inner gas pressures, cavern depths, cavern diameters and cavern wall thickness. As a result, geornechanical constraints are approximated and put into the optimization model OPTUGS. This way, the optimization enables not only the obtaining of an optimal solution but also that the rock mass achieves sufficient strength stability and safety. The optimization is proposed to be performed for the phase of preliminary design. The numerical example at the end of the paper demonstrates the efficiency of the introduced optimization approach. (C) 2011 Elsevier Ltd. All rights reserved.
A non-linear programming model was developed to maximize the economic profit from an anaerobic co-digester. The model consists of a combination of technical and economic equations, linked through the biogas production...
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A non-linear programming model was developed to maximize the economic profit from an anaerobic co-digester. The model consists of a combination of technical and economic equations, linked through the biogas production variable. Five scenarios were simulated. These differed with regard to substrate inlet mass flow rate, organic loading rate and hydraulic retention time. The impact on biogas production was investigated and an economic analysis was undertaken based on the concepts of profitability and Net Present Value. The model results indicate that varying the substrate inlet mass flow rate and organic loading rate could have a positive impact on the profitability of co-digesters in Flanders. This can be achieved either by increasing the interval time between feedstock input, or by feeding individual streams of feedstock separately into the system, while at the same time reducing the hydraulic retention. time. (C) 2016 The Authors. Published by Elsevier Ltd.
Shakedown analysis is an extension of plastic limit analysis to the case of variable repeated loads and plays a significant role in safety assessment and structural design. This paper presents a solution procedure bas...
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Shakedown analysis is an extension of plastic limit analysis to the case of variable repeated loads and plays a significant role in safety assessment and structural design. This paper presents a solution procedure based on the meshless local Petrov-Galerkin (MLPG) method for lower-bound shakedown analysis of bounded kinematic hardening structures. The numerical implementation is very simple and convenient because it is only necessary to construct an array of nodes in the targeted domain. Moreover, the natural neighbour interpolation (NNI) is employed to construct trial functions for simplifying the imposition of essential boundary conditions. The kinematic hardening behaviour is simulated by an overlay model and the numerical difficulties caused by the time parameter are overcome by introducing the conception of load corner. The reduced-basis technique is applied to solve the mathematical programming iteratively through a sequence of reduced residual stress subspaces with very low dimensions and the resulting non-linear programming sub-problems are solved via the Complex method. Numerical examples demonstrate that the proposed solution procedure is feasible and effective to determine the shakedown loads of bounded kinematic hardening structures as well as unbounded kinematic hardening structures. (C)2010 Elsevier Masson SAS. All rights reserved.
Fouling of heat exchangers causes reduced heat transfer and other penalties. Regular cleaning represents one widely used fouling mitigation strategy, where the schedule of cleaning actions can be optimised to minimise...
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Fouling of heat exchangers causes reduced heat transfer and other penalties. Regular cleaning represents one widely used fouling mitigation strategy, where the schedule of cleaning actions can be optimised to minimise the cost of fouling. This paper investigates, for the first time, the situation where there are two cleaning methods available so that the mode of cleaning has to be selected as well as the cleaning interval. Ageing is assumed to convert the initial deposit, labelled 'gel', into a harder and more conductive form, labelled 'coke', which cannot be removed by one of the cleaning methods. The second method can remove both the gel layer and the coke layer, but costs more and requires the unit to be off-line longer for cleaning. Experimental data demonstrating the effects of ageing are presented. The industrial application is the comparison of cleaning-in-place methods with off-line mechanical cleaning. A process model is constructed for an isolated counter-current heat exchanger subject to fouling, where ageing is described by a simple two-layer model. Solutions generated by an NLP-based approach prove to be superior to a simpler heuristic. A series of case studies demonstrate that combinations of chemical and mechanical cleaning can be superior to mechanical cleaning alone for certain combinations of parameters. (C) 2011 Elsevier Ltd. All rights reserved.
This paper describes a general statical approach for shakedown analysis of structures of perfectly plastic material using non-linear optimization. The developed methods may be implemented with any displacement-based f...
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This paper describes a general statical approach for shakedown analysis of structures of perfectly plastic material using non-linear optimization. The developed methods may be implemented with any displacement-based finite element code. The temperature-dependence of the yield limit is taken into account in shakedown analysis. Temperature-dependent shakedown analysis of a pipe-junction and a thick tube are performed by different methods. Copyright (C) 2004 John Wiley Sons, Ltd.
In this paper, a new method is presented in optimization of hydrogen network. The mixed integer non-linear programming (MINLP) and non-linear programming (NLP) problems have been solved with two methods, simultaneousl...
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In this paper, a new method is presented in optimization of hydrogen network. The mixed integer non-linear programming (MINLP) and non-linear programming (NLP) problems have been solved with two methods, simultaneously. The linearization technique for non-linear programming models which proposed by McCormick (1976) and also a new method proposed by Faria and Bagajewicz (2011) have been used to solve these problems. Application of this new method is presented in global optimization of MINLP/NLP, and hydrogen network problem. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
The focus of study in this paper is the class of packing problems. More specifically, it deals with the placement of a set of N circular items of unitary radius inside an object with the aim of minimizing its dimensio...
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The focus of study in this paper is the class of packing problems. More specifically, it deals with the placement of a set of N circular items of unitary radius inside an object with the aim of minimizing its dimensions. Differently shaped containers are considered, namely circles, squares, rectangles, strips and triangles. By means of the resolution of non-linear equations systems through the Newton-Raphson method, the herein presented algorithm succeeds in improving the accuracy of previous results attained by continuous optimization approaches up to numerical machine precision. The computer implementation and the data sets are available at http://***/similar to egbirgin/packing/. (C) 2009 Elsevier Ltd, All rights reserved.
Received signal strength (RSS)-based localization techniques have attracted a lot of interest as they are easy to implement and do not require any localization-specific hardware. However, maximum likelihood (ML) formu...
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Received signal strength (RSS)-based localization techniques have attracted a lot of interest as they are easy to implement and do not require any localization-specific hardware. However, maximum likelihood (ML) formulation of RSS-based localization problem is non-convex, non-linear, and discontinuous, and cannot be solved using standard optimization techniques. We propose techniques that converts the ML objective function into an invex (invariant convex) function and solve them using gradient descent. We also employ coordinate descent to solve the invex problem in a completely distributed manner without any synchronization requirements. The coordinate descent-based technique can be implemented on the sensor nodes as it has low computational complexity and scales very well to large networks. We prove the convergence theoretically, derive the convergence rate, and provide a detailed computational complexity and communication overhead analysis of the techniques. We perform extensive performance analysis and compare our techniques with centralized and distributed localization methods, and demonstrate the superior performance of the proposed techniques in terms of convergence rate, localization accuracy, and execution time.
The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of effi-cient disease detection and control strategies for minimizing the number of infections and deaths in the population ...
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The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of effi-cient disease detection and control strategies for minimizing the number of infections and deaths in the population and halting the spread of the pandemic. Countries have shown different preparedness levels for promptly implementing disease detection strategies, via mass testing and isolation of identified cases, which led to a largely varying impact of the outbreak on the populations and health-care systems. In this paper, we propose a new pandemic resource allocation model for allocating limited disease detection and control resources, in particular testing capacities, in order to limit the spread of a pandemic. The pro-posed model is a novel epidemiological compartmental model formulated as a non-linear programming model that is suitable to address the inherent non-linearity of an infectious disease progression within the population. A number of novel features are implemented in the model to take into account impor-tant disease characteristics, such as asymptomatic infection and the distinct risk levels of infection within different segments of the population. Moreover, a method is proposed to estimate the vulnerability level of the different communities impacted by the pandemic and to explicitly consider equity in the resource allocation problem. The model is validated against real data for a case study of COVID-19 outbreak in France and our results provide various insights on the optimal testing intervention time and level, and the impact of the optimal allocation of testing resources on the spread of the disease among regions. The results confirm the significance of the proposed modeling framework for informing policymakers on the best preparedness strategies against future infectious disease outbreaks.(c) 2021 Elsevier B.V. All rights reserved.
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