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A MINLP model for the layout design of subsea oil gathering- transportation system in deep water oil field considering avoidance of subsea obstacles and pipe intersections

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OCEAN ENGINEERING 2023年第1期277卷

作者： Hong, Cheng Wang, Yuxi Estefen, Segen F. Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Univ Fed Rio de Janeiro Ocean Engn Dept COPPE BR-21941972 Rio De Janeiro Brazil State key Lab Petr Pollut Control Beijing 102206 Peoples R China CNPC Res Inst Safety & Environm Technol Beijing 102206 Peoples R China

This paper focuses on the layout design of subsea oil gathering-transportation system in deep water oil field. In such a system, subsea manifolds are applied to gather and transport the produced fluids from subsea wells to floating processing terminals. A mixed integer nonlinear programming (MINLP) model is proposed, constrained by a series of operation and production requirements, aiming to minimize the total layout cost. Through the model, the pipe line network topology structure which reflects the allocations among the subsea wells, manifolds and processing terminals, the routes of pipes, as well as the size of the facilities could all be figured out. Two key contributions are made through this work. First, avoiding pipe intersections and subsea obstacles are integrated simultaneously, making the proposed model closer to practical situations. Second, a decomposition strategy based on Delaunay triangulation and gradient descent is constructed, achieving high quality initial solution and stable iteration process. The results of case studies indicate the validity, feasibility and stability of the proposed model and the solution method. Besides, the effect of manifold numbers on the layout optimization result is analyzed through the model, indicating its flexibility in the layout design analysis.

关键词： Subsea oil gathering-transportation Layout design mixed integer nonlinear programming Delaunay triangulation Gradient descent algorithm

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Mathematical programming techniques in water network optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第3期243卷 774-788页

作者： D'Ambrosio, Claudia Lodi, Andrea Wiese, Sven Bragalli, Cristiana Ecole Polytech CNRS F-91128 Palaiseau France Ecole Polytech LIX F-91128 Palaiseau France Univ Bologna DEI I-40136 Bologna Italy Univ Bologna DICAM I-40136 Bologna Italy

In this article we survey mathematical programming approaches to problems in the field of drinking water distribution network optimization. Among the predominant topics treated in the literature, we focus on two different, but related problem classes. One can be described by the notion of network design, while the other is more aptly termed by network operation. The basic underlying model in both cases is a nonlinear network flow model, and we give an overview on the more specific modeling aspects in each case. The overall mathematical model is a mixed integer nonlinear Program having a common structure with respect to how water dynamics in pipes are described. Finally, we survey the algorithmic approaches to solve the proposed problems and we discuss computation on various types of water networks. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Networks mixed integer nonlinear programming Combinatorial optimization Global optimization

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Modeling without categorical variables: a mixed-integer nonlinear program for the optimization of thermal insulation systems

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OPTIMIZATION AND ENGINEERING 2010年第2期11卷 185-212页

作者： Abhishek, Kumar Leyffer, Sven Linderoth, Jeffrey T. Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA United Airlines Enterprise Optimizat Elk Grove Village IL 60007 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

Optimal design applications are often modeled by using categorical variables to express discrete design decisions, such as material types. A disadvantage of using categorical variables is the lack of continuous relaxations, which precludes the use of modern integer programming techniques. We show how to express categorical variables with standard integer modeling techniques, and we illustrate this approach on a load-bearing thermal insulation system. The system consists of a number of insulators of different materials and intercepts that minimize the heat flow from a hot surface to a cold surface. Our new model allows us to employ black-box modeling languages and solvers and illustrates the interplay between integer and nonlinear modeling techniques. We present numerical experience that illustrates the advantage of the standard integer model.

关键词： mixed integer nonlinear programming Modeling with binary variables Thermal insulation systems Categorical variables

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Feasibility pump for aircraft deconfliction with speed regulation

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JOURNAL OF GLOBAL OPTIMIZATION 2018年第3期71卷 501-515页

作者： Cafieri, Sonia D'Ambrosio, Claudia Univ Toulouse ENAC F-31055 Toulouse France Ecole Polytech LIX CNRS UMR7161 F-91128 Palaiseau France

We propose Feasibility Pump heuristics for the crucial problem of aircraft conflict avoidance arising in air traffic management. This problem can be modeled as a mixed integer nonlinear optimization problem, whose solution can be very computationally demanding. Feasibility Pump is an iterative algorithm that, at each iteration, solves alternatively two easier subproblems represented by relaxations of the original problem, minimizing the distance between their solutions. We propose in this paper specific formulations for the subproblems to be handled, tailored to the problem at hand. Numerical results show that, on the considered test problems, good-quality, in some cases optimal, feasible solutions are always obtained.

关键词： mixed integer nonlinear programming Feasibility pump Aircraft conflict avoidance Mathematical programming Reformulations Heuristic algorithm

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Event-triggered model predictive control for the cascade filling process of a hydrogen fuelling station

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ENGINEERING OPTIMIZATION 2024年第6期56卷 940-962页

作者： Xu, Jian Xie, Hui Huang, Lei Li, Anqi Zhao, Kangqi Wang, Yihui Aerosp Hydrogen Energy Technol Co Ltd Beijing Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

Efficient filling strategies for hydrogen fuel cell vehicles are critical for hydrogen utilization efficiency at hydrogen fuelling stations. A novel event-triggered model predictive control framework is proposed in this article for the filling process of a hydrogen fuelling station, which involves multiple compressors, cascade storage tanks, and multiple dispensers. The filling process is formulated as a mixed-integer nonlinear programming (MINLP) problem with the objective of minimizing the vehicle filling times and maximizing the hydrogen utilization efficiency. A solution approach that combines the mixed-integer linear programming and genetic algorithm is designed for solving the resulting MINLP problem. In addition, an event-triggered mechanism is proposed to increase the computational efficiency and to update the control inputs only when needed. Different sets of computational experiments are carried out to demonstrate the effectiveness of the mathematical formulation and the solution approach.

关键词： Hydrogen fuelling station event-trigger control mixed integer nonlinear programming model predictive control genetic algorithm

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The global solver in the LINDO API

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OPTIMIZATION METHODS & SOFTWARE 2009年第4-5期24卷 657-668页

作者： Lin, Youdong Schrage, Linus Lindo Syst Inc Chicago IL 60622 USA

The global solver in the LINDO Application programming Interface (LINDO API) finds guaranteed global optima to nonconvex, nonlinear and integer mathematical models using the branch and bound/relax approach. We describe (a) the class of problems for which it tends to be appropriate;(b) how to access it directly via the LINDO API or via various modelling language front ends;(c) heuristics used for finding good initial solutions;(d) methods for constructing easily solved relaxations;(e) branching rules for splitting a problem into more easily solved subproblems;and (f) some illustrative computational results.

关键词： global optimization nonsmooth optimization mixed integer nonlinear programming LINDOAPI

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An Extended Optimal Transmission Switching Algorithm Adapted for Large Networks and Hydro-Electric Context

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IEEE ACCESS 2020年 8卷 87762-87774页

作者： Belanger, Jacky Dessaint, Louis A. Kamwa, Innocent Cegep Sept Iles Inergia Intelligence Energet Sept Iles PQ G4R 5B7 Canada Ecole Technol Super Dept Genie Elect Montreal PQ H3C 1K3 Canada Inst Rech Elect Quebec IREQ Hydroquebec Varennes PQ J3X 1S1 Canada

Previous works have shown that the best topology of an electrical network depends on the total demand and its distribution. Optimal Transmission Switching (OTS) identifies lines to be opened to minimize operating costs. However, this cost-based criterion is not suitable for hydropower generation-based companies, where hydrology largely dictates the power generation. Moreover, no study about impact of OTS on voltage stability margins have been conducted in literature as it never been explicitly integrated in the problem formulation. Thus, OTS does not guarantee that proposed topologies are secure even if voltage bounds are respected. It also does not take full advantage of the real networks flexibility by including only line states and production distribution in the control vector. This leads to algorithms that are not suitable for weakly meshed (or mostly radial) networks. In this paper, we propose an extended OTS (E-OTS) that includes several improvements over current algorithms and overcomes the above-mentioned problems. We also propose two heuristics to reduce resolution time. Results on the PEGASE (1354 bus) and Polish (2236 bus) networks show that E-OTS identifies a topology improvement in less than 40 minutes, which is low enough to be implemented in a real energy management system. Tests have been made for three different criteria.

关键词： mixed integer nonlinear programming optimal transmission switching reactive components switching power network modeling successive optimal power flows operations planning voltage stability margins parallel computing

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Preprocessing algorithm and tightening constraints for multiperiod blend scheduling: cost minimization

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JOURNAL OF GLOBAL OPTIMIZATION 2020年第3期77卷 603-625页

作者： Chen, Yifu Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn 1415 Engn Dr Madison WI 53706 USA

While a range of models have been proposed for the multiperiod blend scheduling problem (MBSP), solving even medium-size MBSP instances remains challenging due to the presence of bilinear terms and binary variables. To address this challenge, we develop solution methods for MBSP focusing on the cost minimization objective. We develop a novel preprocessing algorithm to calculate lower bounds on stream flows. We define product dedicated flow variables to address product specific features involved in MBSP. Bounds on stream flows and new product dedicated flow variables are then used to generate tightening constraints which significantly improve the solution time of the mixed integer nonlinear programming models as well as models based on linear approximations.

关键词： Preprocessing Multiperiod blending mixed integer nonlinear programming

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Penalty and relaxation methods for the optimal placement and operation of control valves in water supply networks

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2017年第1期67卷 201-223页

作者： Pecci, Filippo Abraham, Edo Stoianov, Ivan Imperial Coll London Dept Civil & Environm Engn London England

In this paper, we investigate the application of penalty and relaxation methods to the problem of optimal placement and operation of control valves in water supply networks, where the minimization of average zone pressure is the objective. The optimization framework considers both the location and settings of control valves as decision variables. Hydraulic conservation laws are enforced as nonlinear constraints and binary variables are used to model the placement of control valves, resulting in a mixed-integer nonlinear program. We review and discuss theoretical and algorithmic properties of two solution approaches. These include penalty and relaxation methods that solve a sequence of nonlinear programs whose stationary points converge to a stationary point of the original mixed-integer program. We implement and evaluate the algorithms using a benchmarking water supply network. In addition, the performance of different update strategies for the penalty and relaxation parameters are investigated under multiple initial conditions. Practical recommendations on the numerical implementation are provided.

关键词： Water distribution networks mixed integer nonlinear programming Mathematical programs with complementarity constraints nonlinear programming

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ε-OA for the solution of bi-objective generalized disjunctive programming problems in the synthesis of nonlinear process networks

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COMPUTERS & CHEMICAL ENGINEERING 2015年 72卷 199-209页

作者： Fattahi, Ali Turkay, Metin Koc Univ Dept Ind Engn TR-34450 Istanbul Turkey

There has been an increasing interest in multicriteria optimization (MCO) of nonlinear process network problems in recent years. Several mathematical models have been developed and solved using MCO methodologies including e-constraint, weighted sum, and minimum distance. In this paper, we investigate the bi-objective nonlinear network synthesis problem and propose an effective algorithm, epsilon-OA, based on augmented epsilon-constraint and logic-based outer approximation (OA). We provide theoretical characterization of the proposed algorithm and show that the solutions generated are efficient. We illustrate the effectiveness of our novel algorithm on two benchmark problems. The epsilon-OA is compared to the straightforward use of OA with augmented epsilon-constraint algorithm (epsilon-con + OA), the augmented epsilon-constraint without OA (E-MINLP), and the traditional epsilon-constraint (T-epsilon-con). Based on the results, our novel algorithm is very effective in solving the bi-objective generalized disjunctive programming problems in the synthesis of process networks. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Generalized disjunctive programming Multiple criteria optimization Outer approximation mixed integer nonlinear programming epsilon-Constraint method

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