Dimethyl ether (DME) is a clean and efficient synthetic fuel with the potential to substitute liquefied petroleum gas (LPG) and diesel. From both the environmental and economic viewpoints, there is a strong preference...
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Dimethyl ether (DME) is a clean and efficient synthetic fuel with the potential to substitute liquefied petroleum gas (LPG) and diesel. From both the environmental and economic viewpoints, there is a strong preference to use biomass as the feedstock of synthetic fuels. In this study, state-of-the-art technologies were surveyed and a superstructure representation for the DME production process involving the use of biomass as a feedstock was investigated. Moreover, a mixed-integer nonlinear programming (MINLP) model was proposed for developing a DME production process based on biomass gasification. A combination of optimal technologies was adopted;detailed studies are presented to demonstrate the key features of the proposed superstructure.
In this work we propose a mixed-integer nonlinear programming model to address the line pressure optimization problem for shale gas gathering systems. This model is designed to determine: a) the optimal timing for tur...
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In this work we propose a mixed-integer nonlinear programming model to address the line pressure optimization problem for shale gas gathering systems. This model is designed to determine: a) the optimal timing for turning prospective wells in-line, b) the optimal pressure profile within a gathering network, and c) the necessary compression power for delivering produced gas to long-distance transmission lines. We rely on a pressure-normalized decline curve model to quantify how line pressure variations impact the gas production of individual wells. The reservoir model itself is incorporated in a transmission optimization framework which rigorously evaluates pressure drops along pipeline segments. Moreover, we explicitly consider compression requirements to lift line pressure from gas gathering levels to setpoints dictated by transmission pipeline companies. Since the resulting optimization models are large-scale, nonlinear and nonconvex, we propose a solution procedure based on an efficient initialization strategy. Finally, we present a detailed case study, and show that the proposed optimization framework can be used effectively to manage line pressures in shale gas gathering systems by properly scheduling when, and how many, new wells are brought online.
This study recommends a stochastic optimisation model for the security-constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generat...
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This study recommends a stochastic optimisation model for the security-constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. outage unit/line. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC-OPF). The AC-OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and OTS action. While the stochastic SCUC model with the OTS action based on the AC-OPF is a mixed-integer non-linear programming model, this study transforms it into a mixed-integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of the AC-OPF, which allows the reactive power and voltage to be considered directly in power flow model. This transformation guarantees achievement of a global optimal solution by the existing software and algorithms. The proposed stochastic SCUC problem is evaluated on the 6-bus and IEEE 118-bus test systems in pre- and post-transmission switching action. Obtained results demonstrate the effectiveness of the proposed model.
This paper proposes an uncertain multi-period bi-level network interdiction problem with uncertain arc capacities. It is proved that there exists an equivalence relationship between uncertain multi-period network inte...
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This paper proposes an uncertain multi-period bi-level network interdiction problem with uncertain arc capacities. It is proved that there exists an equivalence relationship between uncertain multi-period network interdiction problem and the obtained deterministic correspondent. Application of the generalized Benders' decomposition algorithm is considered as the solution approach to the resulting mixed-integer nonlinear programming problem. Finally, a numerical example is presented to illustrate the model and the algorithm.
In this study a firm in a monopoly environment is considered. The firm sells two types of perishable products using a commodity bundling practice. This study aims to find the optimal strategy of selling these products...
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In this study a firm in a monopoly environment is considered. The firm sells two types of perishable products using a commodity bundling practice. This study aims to find the optimal strategy of selling these products. We determine whether they should be offered separately or in a bundle, define their optimal prices and determine the initial amount of the bundle which should be made from components with limited stocks. A benefit-lost cost in a case that the customer segment does not find their desirable product is considered along with a shortage cost in a case that the customer segment selects a product or the bundle but there is not enough of it to satisfy all of the customer segment demands. In this study several different customer segments with different behaviors and reservation prices are assumed. The problem is solved using the mixed-integer Non-Linear programming solver in LINGO software and a Genetic Algorithm. Finally, the superiority of obtained model and results are presented. This is the first study to locate optimal strategy of selling two types of perishable products using a commodity bundling practice. This study introduces a new mathematical optimization model based on previous gaps. Moreover, it covers the significant gaps of previous studies with respect to both practical and theoretical aspects.
DNA tiles are self-assembled nanostructures, which offer exciting opportunities for synthesis of novel materials. A challenge for structural design of DNA tiles is to identify optimal locations for so-called crossover...
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DNA tiles are self-assembled nanostructures, which offer exciting opportunities for synthesis of novel materials. A challenge for structural design of DNA tiles is to identify optimal locations for so-called crossovers, which are bridges between DNA double helices formed by pairs of single-stranded DNA. An optimization-based approach is presented to identify optimal locations for such crossovers. Minimization of a potential-energy model for a given structural design demonstrates the importance of local minima. Both deterministic global optimization of a reduced model and multistart optimization of the full model are applied successfully to identify the global minimum. MINLP optimization using a branch-and-bound algorithm (GAMS/SBB) identifies an optimal structural design of a DNA tile successfully with significant reduction in computational load compared to exhaustive enumeration, which demonstrates the potential of the proposed method to reduce trial-and-error efforts for structural design of DNA tiles. (c) 2016 American Institute of Chemical Engineers AIChE J, 63: 1804-1817, 2017
Integrating solar thermal energy into conventional coal-fired power plant has been proved to be an efficient way to use solar energy and improve the generation efficiency of coal-fired power plant. Because of the vari...
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Integrating solar thermal energy into conventional coal-fired power plant has been proved to be an efficient way to use solar energy and improve the generation efficiency of coal-fired power plant. Because of the variability of solar irradiance, an optimization strategy based on switching solar integrate mode according to the solar irradiance is proposed in this paper. A parabolic trough solar aided coal-fired combined heat and power (SACF-CHP) system model is built to analyze the system performance. In order to obtain the minimum Levelized Electricity Costs (LEC), a generic superstructure of solar integrate mode is established, and a mixedintegernonlinearprogramming approach is applied to optimize the oil-water heat exchanger area and solar integrate mode. The optimized results of a 150 MW SACF-CHP system indicate that switching integrate mode according to the solar irradiance would have a lower LEC than fixing the integrate mode invariably, and the optimal LEC of those two integrate modes are 0.0771 $/kW h and 0.0794 $/kW h respectively. Besides, the LEC decreases at first and then increases with the increase of the oil-water heat exchanger area. And the optimal heat exchanger area for changeable integrate mode are 725 m(2). (C) 2017 Elsevier Ltd. All rights reserved.
The robust optimization of reservoir performance under geological uncertainty typically requires the simulation of multiple geological realizations at each iteration of the optimization run. This results in high compu...
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The robust optimization of reservoir performance under geological uncertainty typically requires the simulation of multiple geological realizations at each iteration of the optimization run. This results in high computational expense, particularly when simulation models are highly resolved and many realizations are employed to characterize geological uncertainty. In recent work we introduced a multilevel optimization procedure that uses a sequence of upscaled models to accelerate field development optimization. The core optimizer is a particle swarm optimization-mesh adaptive direct search (PSO-MADS) hybrid technique. Coarse-scale models are constructed from the fine-grid geological characterization using an accurate global transmissibility upscaling procedure. In this paper we extend the multilevel framework to enable efficient optimization under uncertainty. New treatments include the use of a sample validation procedure for realization selection and the use of the standalone MADS optimizer (rather than PSO-MADS) after the first optimization stage. Numerical results are presented for two example systems, and for each case optimization over both ten and 100 realizations is performed. For ten-realization cases we achieve comparable results, and speedups of a factor of 10 or more, relative to the conventional single-level optimization procedure. Speedups are estimated to be even more substantial for 100 realization cases, for which conventional optimization is not practical. We also investigate the application of a multilevel Monte Carlo approach as an alternative to our proposed techniques for optimization under uncertainty. Although this method is faster than the conventional approach, it is not as efficient as the multilevel procedures developed in this work.
When using the standard McCormick inequalities twice to convexify trilinear monomials, as is often the practice in modeling and software, there is a choice of which variables to group first. For the important case in ...
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When using the standard McCormick inequalities twice to convexify trilinear monomials, as is often the practice in modeling and software, there is a choice of which variables to group first. For the important case in which the domain is a nonnegative box, we calculate the volume of the resulting relaxation, as a function of the bounds defining the box. In this manner, we precisely quantify the strength of the different possible relaxations defined by all three groupings, in addition to the trilinear hull itself. As a by-product, we characterize the best double-McCormick relaxation. We wish to emphasize that, in the context of spatial branch and bound for factorable formulations, our results do not only apply to variables in the input formulation. Our results apply to monomials that involve auxiliary variables as well. So, our results apply to the product of any three (possibly complicated) expressions in a formulation.
A systematic approach is presented to retrofit design of a hydrogen distribution network. A methodology is based on the steady-state flexibility index (FIs) for optimizing network and enhancing the efficiency of purif...
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A systematic approach is presented to retrofit design of a hydrogen distribution network. A methodology is based on the steady-state flexibility index (FIs) for optimizing network and enhancing the efficiency of purifiers. The main object of this technique is to investigate hydrogen network flexibility under severe operation uncertainty and to reduce streams to purifiers by consideration inlet feed purity of purifiers. The presented approach is applied to the hydrogen network of, a real installation. (C) 2016 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
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