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Oil sands extraction plant debottlenecking: an optimization approach

OPTIMIZATION LETTERS 2020年第4期14卷 945-957页

作者： Yuan, Yuan Li, Zukui Huang, Biao Univ Alberta Edmonton AB Canada

Debottlenecking is highly desirable to increase the production throughput for the oil sands industry. In this work, the bottleneck identification and capacity expansion problem is solved through optimization techniques. In the proposed debottlenecking procedure, first-principles method and Gaussian process modeling approach are applied to build process models. Depending on the type of process model used, the optimization problem is solved either as a parametric linear programming problem or as a nonlinear optimization problem. By solving the optimization problem, the bottlenecks can be identified and the necessary capacity expansion for process units for bottleneck removal is reported. The proposed method is demonstrated through applications in oil sands production process.

关键词： Debottlenecking Gaussian process modeling parametric linear programming Nonlinear optimization

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Complexity Results for Fourier-Motzkin Elimination

Complexity Results for Fourier-Motzkin Elimination

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作者： Delaram Talaashrafi University of Western Ontario

学位级别：硕士

In this thesis, we propose a new method for removing all the redundant inequalities generated by Fourier-Motzkin elimination. This method is based on Kohler s work and an improved version of Balas work. Moreover, this method only uses arithmetic operations on matrices. Algebraic complexity estimates and experimental results show that our method outperforms alternative approaches based on linear programming.

关键词： polyhedron projection Fourier-Motzkin elimination linear algebra polyhedral cone parametric linear programming

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Measurement of energy efficiency based on frontier approaches

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Science Foundation in China 2018年第2期26卷 63-80页

作者： ZHANG Ning ZHOU MengLing College of Economics Jinan UniversityGuangzhou 510632China

The improvement of energy efficiency has played an important role in the global low-carbon economy and the sustainable development *** are many methodologies for estimating energy efficiency in *** article introduces four frontier methods for energy efficiency evaluation:nonparametric data envelopment analysis(DEA),parametric linear programming,stochastic frontier analysis(SFA),and the meta-frontier *** advantages and disadvantages of these methods are ***,suggestions for model selection in energy efficiency measurement are offered.

关键词： Energy efficiency Data envelopment analysis （DEA） parametric linear programming Stochastic frontier analysis （SFA） Meta-frontier approach

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A new method for computing a p-solution to parametric interval linear systems with affine-linear and nonlinear dependencies

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BIT NUMERICAL MATHEMATICS 2017年第4期57卷 1109-1136页

作者： Skalna, Iwona Hladik, Milan AGH Univ Sci & Technol Krakow Poland Charles Univ Prague Malostranske Nam 25 Prague 11800 Czech Republic

We propose a new approach to computing a parametric solution (the so-called p-solution) to parametric interval linear systems. Solving such system is an important part of many scientific and engineering problems involving uncertainties. The parametric solution has many useful properties. It permits to compute an outer solution, an inner estimate of the interval hull solution, and intervals containing the lower and upper bounds of the interval hull solution. It can also be employed for solving various constrained optimisation problems related to the parametric interval linear system. The proposed approach improves both outer and inner bounds for the parametric solution set. In this respect, the new approach is competitive to most of the existing methods for solving parametric interval linear systems. Improved bounds on the parametric solution set guarantees improved bounds for the solutions of related optimisation problems.

关键词： Revised affine forms Affine-interval iterative methods parametric interval linear systems parametric solution parametric linear programming

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Optimization of Hierarchical Structures of Production Technological Processes Subsystems

Optimization of Hierarchical Structures of Production Techno...

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International Russian Automation Conference

作者： Mikhail Kharitonov Mathematics and IT Department Volgograd State University Volgograd

ISBN: (纸本)9781538649398

The article describes the methods and algorithms of numerical optimization for the hierarchical structures, described by hierarchical graphs with structurally dependent objective functions that can be represented as superpositions of elementary objective functions, specified at their vertices. On their basis, the authors built and implemented formal models and algorithms for analytical and numerical optimization by symmetric hierarchical structures of the production subsystems of enterprises with structurally dependent production functions of the Leontief type.

关键词： parametric linear programming Production subsystem Optimization of variable structure Technological graph Production function

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Evaluation of the Performance Analysis in Fuzzy Queueing theory

Evaluation of the Performance Analysis in Fuzzy Queueing the...

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International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE)

作者： Geetha, S. Ramalakshmi, Venkatachalapathy Bhuvaneeswari, Subramanian RameshKumar, Bharathi Natl Engn Coll Dept Math Kovilpatti Tamil Nadu India Sree Sowdambika Coll Engn Dept Math Aruppukottai Tamil Nadu India

ISBN: (纸本)9781467384377

This paper, analyze the parametric performance measures in Poisson arrival and Erlang service time with k phase queuing system the arrival rate and service rate being Triangular fuzzy numbers. Using Zaden Extension principle to estimate the uncertainty associated with the input parameters and triangular membership function. This function has been used to statistical applications to obtained the error performance of the waiting time in the Queue and in the system. The basic idea of M/E-k/1/infinity/FIFO is to transform to a fuzzy queue by applying the alpha - cut approach, since the system performance measures are expressed by membership function rather than by crisp values. A numerical example is included.

关键词： Fuzzy sets Queueing theory Infinite Capacity Membership functions parametric linear programming

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On the complexity of the convex liftings-based solution to inverse parametric convex programming problems

On the complexity of the convex liftings-based solution to i...

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European Control Conference (ECC)

作者： Nguyen, N. A. Olaru, S. Rodriguez-Ayerbe, P. Univ Paris Sud CentraleSupelec L2S CNRSUMR 8506 3 Rue Joliot Curie F-91192 Gif Sur Yvette France

ISBN: (纸本)9783952426937

The link between linear model predictive control (MPC) and parametric linear/quadratic programming has reached maturity in terms of the characterization of the structural properties and the numerical methods available for the effective resolution. The computational complexity is one of the current bottlenecks for these control design methods and inverse optimality has been recently shown to provide a new perspective for this challenge. However, the question of the minimal complexity of inverse optimality formulation is still open and much under discussion. In this paper we revisit some recent results by pointing out unnecessary geometrical complications which can be avoided by the interpretation of the optimality conditions. Two algorithms for fine-tuning inverse optimality formulation will be proposed and the results will be interpreted via two illustrative examples in comparison with existing formulations.

关键词： computational complexity convex programming linear programming predictive control quadratic programming MPC convex lifting-based solution fine-tuning inverse optimality formulation inverse parametric convex programming problem linear model predictive control parametric linear programming parametric quadratic programming Computational complexity Cost function Europe programming Redundancy

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Another look at linear programming for feature selection via methods of regularization

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STATISTICS AND COMPUTING 2014年第5期24卷 885-905页

作者： Yao, Yonggang Lee, Yoonkyung SAS Inst Inc Cary NC 27513 USA Ohio State Univ Dept Stat Columbus OH 43210 USA

We consider statistical procedures for feature selection defined by a family of regularization problems with convex piecewise linear loss functions and penalties of l (1) nature. Many known statistical procedures (e.g. quantile regression and support vector machines with l (1)-norm penalty) are subsumed under this category. Computationally, the regularization problems are linear programming (LP) problems indexed by a single parameter, which are known as 'parametric cost LP' or 'parametric right-hand-side LP' in the optimization theory. Exploiting the connection with the LP theory, we lay out general algorithms, namely, the simplex algorithm and its variant for generating regularized solution paths for the feature selection problems. The significance of such algorithms is that they allow a complete exploration of the model space along the paths and provide a broad view of persistent features in the data. The implications of the general path-finding algorithms are outlined for several statistical procedures, and they are illustrated with numerical examples.

关键词： Grouped regularization l(1)-norm penalty parametric linear programming Quantile regression Simplex method Structured learning Support vector machines

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A Prototype Learning Framework Using EMD: Application to Complex Scenes Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第3期35卷 513-526页

作者： Ricci, Elisa Zen, Gloria Sebe, Nicu Messelodi, Stefano Univ Perugia Fac Ingn Dipartimento Ingn Elettron & Informaz I-06125 Perugia Italy Univ Trent Dipartimento Ingn & Sci Informaz DISI I-38123 Trento Italy Fdn Bruno Kessler FBK Irst I-38123 Trento Italy

In the last decades, many efforts have been devoted to develop methods for automatic Scene understanding in the context of video surveillance applications. This paper presents a novel nonobject centric approach for complex scene analysis. Similarly to previous methods, we use low-level cues to individuate atomic activities and create clip histograms. Differently from recent works, the task of discovering high-level activity patterns is formulated as a convex prototype learning problem. This problem results in a simple linear program that can be solved efficiently with standard solvers. The main advantage of our approach is that, using as the objective function the Earth Mover's Distance (EMD), the similarity among elementary activities is taken into account in the learning phase. To improve scalability we also consider some variants of EMD adopting L-1 as ground distance for 1D and 2D, linear and circular histograms. In these cases, only the similarity between neighboring atomic activities, corresponding to adjacent histogram bins, is taken into account. Therefore, we also propose an automatic strategy for sorting atomic activities. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches, often outperforming them.

关键词： Video surveillance complex scene analysis earth mover's distance parametric linear programming

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Computing multiple-output regression quantile regions

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第4期56卷 840-853页

作者： Paindaveine, Davy Siman, Miroslav Univ Libre Bruxelles ECARES B-1050 Brussels Belgium

A procedure relying on linear programming techniques is developed to compute (regression) quantile regions that have been defined recently. In the location case, this procedure allows for computing halfspace depth regions even beyond dimension two. The corresponding algorithm is described in detail, and illustrations are provided both for simulated and real data. The efficiency of a MATLAB implementation of the algorithm is also investigated through extensive simulations. (C) 2010 Elsevier By. All rights reserved.

关键词： Halfspace depth Multiple-output regression parametric linear programming Quantile regression

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