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Optimal design of extractive distillation for acetic acid dehydration with N-methyl acetamide

CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2017年 120卷 301-316页

作者： You, Xinqiang Gu, Jinglian Peng, Changjun Rodriguez-Donis, Ivonne Liu, Honglai East China Univ Sci & Technol State Key Lab Chem Engn Shanghai 200237 Peoples R China East China Univ Sci & Technol Dept Chem Shanghai 200237 Peoples R China Chongqing Univ Sch Chem & Chem Engn Chongqing 400044 Peoples R China Univ Toulouse INRA ENSIACET INPT Lab Chim Agroind Toulouse France

A distinctive strategy for entrainer recycling is proposed in this work for acetic acid (AA) dehydration by extractive distillation by using N methyl acetamide (NMA). The use of standard entrainers such as DMF or DMSO has the main drawback of forming an azeotrope with acetic acid. However, the vapour liquid equilibrium AA NMA exhibits a tangential pinch point at NMA end composition. The new strategy rises from the thermodynamic analysis of the ternary diagram that which involves no azeotrope. As a result, acetic acid with high purity can be obtained by the recycling of the entrainer with a relaxed constraint in its purity. Optimization studies are discussed by using two approaches: two-step optimization method with sequential quadratic programming (TSOM case) and the multi-objective genetic algorithm. The multi-objective genetic algorithm allowed the computation of the optimal acetic acid dehydration with an impurity of 3% in the recycled entrainer. Significant cost savings are achieved thanks to the optimization of both columns together. Energy consumption is reduced by 12.8% and 56.9% whereas TAC is saved by 28.4% and 56.3% compared with optimal case TSOM (impurity content 1%) and a published "Case Ref" (impurity content 0.01%), respectively.

关键词： Acetic acid dehydration Extractive distillation sequential quadratic programming Multi-objective optimization Genetic algorithm

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A novel data-driven controller for plug-in hybrid electric vehicles with improved adaptabilities to driving environment

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JOURNAL OF CLEANER PRODUCTION 2022年 334卷

作者： Liu, Yu Zhang, Yuanjian Yu, Hanzhengnan Nie, Zhigen Liu, Yonggang Chen, Zheng China Automot Technol & Res Ctr Co Ltd Tianjin 300300 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Yunnan Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Sch Automot Engn Chongqing 400044 Peoples R China

Instantaneous application optimality is one of the indispensable indicators to assess energy management performance of plug-in hybrid electric vehicles (PHEVs). The momentary optimality, nevertheless, cannot be flexibly reachable under various driving environments due to the partial unobservabilities in control algorithms. To cope with it, a novel data-driven controller for PHEVs is proposed in this paper to achieve the instantaneous optimality of energy management. The well-designed machine learning based controller translates the knowledge of global optimization to real-time controlling scheme with the consideration of adaptabilities to disperse driving conditions. To start with, the universal global optimal control policies for varying driving environment are generated offline based on the chaotic quantum particle swarm optimization with sequential quadratic programming (CQPSO-SQP). Then, the offline optimized global control policies are assembled to construct the dataset for training the least square support vector machine (LSSVM) based controller, which features the superior capability in instantly optimal policy making under different driving conditions. At last, the detailed assessment is performed in simulation test and hardware-in-loop (HIL) test to validate the promising role of CQPSO-PSO and LSSVM in designing the novel energy management controller, and the corresponding results highlight the preferable controlling performance of the proposed novel controller in practical applications.

关键词： Plug-in hybrid electric vehicles Quantum particle swarm optimization Chaotic optimization sequential quadratic programming Least square support vector machine

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Superlinear convergence of a stabilized SQP method to a degenerate solution

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 1998年第3期11卷 253-275页

作者： Wright, SJ Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA

We describe a slight modification of the well-known sequential quadratic programming method for nonlinear programming that attains superlinear convergence to a primal-dual solution even when the Jacobian of the active constraints is rank deficient at the solution. We show that rapid convergence occurs even in the presence of the roundoff errors that are introduced when the algorithm is implemented in floating-point arithmetic.

关键词： nonlinear programming sequential quadratic programming degenerate solutions

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Optimization of Impulsive Trajectories from Circular Orbit to an Excess Velocity Vector

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2012年第1期35卷 234-244页

作者： Jones, D. R. Ocampo, C. Univ Texas Austin Dept Aerosp Engn & Engn Mech WR Woolrich Labs Austin TX 78712 USA

Feasible transfer trajectories are constructed to serve as initial guesses for determining constrained optimal impulsive escape trajectories, from a circular orbit to a target hyperbolic excess velocity vector. The proximity of these feasible solutions to their corresponding optima are quantified. The objective of this work is to improve the feasible solutions, thereby enabling general anytime escape trajectories to be targeted that result in substantially reduced fuel expenditure. The procedure is currently restricted to one- and three-impulse escape trajectories from a circular parking orbit. Two separate three-impulse methods are presented, each specific to whether the time of flight is free or fixed. Numerical results, obtained using nonlinear programming algorithms, are presented to validate the robustness of the method. Analysis and results are nondimensional, and therefore are applicable to departures from a circular orbit about any celestial body.

关键词： Trans Earth Injection Parking Orbit Nonlinear programming Periapsis sequential quadratic programming Kepler's Equation Earth Trajectory Optimization Numerical Integration Space Exploration

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Combining the regularization strategy and the SQP to solve MPCC - A MATLAB implementation

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2011年第18期235卷 5348-5356页

作者： Monteiro, M. Teresa T. Rodrigues, Helena Sofia Univ Minho Dept Prod & Sistemas P-4710057 Braga Portugal Inst Politecn Viana Castelo Escola Super Ciencias Empresariais P-4930678 Valenca Portugal

Mathematical Program with Complementarity Constraints (MPCC) plays a very important role in many fields such as engineering design, economic equilibrium, multilevel games, and mathematical programming theory itself. In theory its constraints fail to satisfy a standard constraint qualification such as the linear independence constraint qualification (LICQ) or the Mangasarian-Fromovitz constraint qualification (MFCQ) at any feasible point. As a result, the developed nonlinear programming theory may not be applied to MPCC class directly. Nowadays, a natural and popular approach is trying to find some suitable approximations of an MPCC so that it can be solved by solving a sequence of nonlinear programs. This work aims to solve the MPCC using nonlinear programming techniques, namely the SQP and the regularization scheme. Some algorithms with two iterative processes, the inner and the external, were developed. A set of AMPL problems from MacMPEC database (Leyffer, 2000) [8] were tested. The comparative analysis regarding performance of algorithms was carried out. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Mathematical program with complementarity constraints sequential quadratic programming Nonlinear programming Regularization scheme

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A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects

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ENERGY 2012年第1期38卷 346-353页

作者： Cai, Jiejin Li, Qiong Li, Lixiang Peng, Haipeng Yang, Yixian Sun Yat Sen Univ Sch Phys & Engn Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sino French Inst Nucl Engn & Technol Guangzhou 510275 Guangdong Peoples R China S China Univ Technol State Key Lab Subtrop Bldg Sci Guangzhou 510640 Guangdong Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr Beijing 100876 Peoples R China

In this paper, a hybrid method integrating the fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm and the sequential quadratic programming (SQP) techniques, named FCASO-SQP method, is presented for solving the economic dispatch (ED) problems in power systems. The FCASO algorithm is the main optimizer in the hybrid method and the SQP technique is used to fine tune its results to improve the solution. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters psi(d) and r(i) of chaotic ant swarm optimization (CASO). The proposed method was applied to three different cases of power systems with three units, thirteen units and forty units, and the simulation results demonstrate its applicability and effectiveness to solve the ED problems with valve-point effect. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch Fuzzy adaptive chaotic ant swarm optimization sequential quadratic programming Evolutionary algorithm Power system

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Parallel computing in water network analysis and leakage minimization

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE 2000年第4期126卷 251-260页

作者： Alonso, JM Alvarruiz, F Guerrero, D Hernández, V Ruiz, PA Vidal, AM Martínez, F Vercher, J Ulanicki, B Univ Politecn Valencia Dept Sistemas Informat & Computac Parallel Comp Grp Valencia 46022 Spain Univ Politecn Valencia Dept Ingn Hidraul & Med Ambiente Valencia 46022 Spain De Montfort Univ Leicester LE1 9BH Leics England

In this paper a parallel computing based software demonstrator for the simulation and leakage minimization of water networks is presented. This demonstrator, based on the EPANET package, tackles three different types of problems making use of parallel computing. First, the solution of the hydraulic problem is treated by means of the gradient method. The key point in the parallelization of the method is the solution of the underlying linear systems, which is carried out by means of a multifrontal Choleski method. Second, the water quality simulation problem is approached by using the discrete volume element method. The application of parallel computing is based on dividing the water network in several parts using the multilevel recursive bisection graph partitioning algorithm. Finally, the problem of leakage minimization using pressure reducing valves is approached. This results in the formulation of an optimization problem for each time step, which is solved by means of sequential quadratic programming. Because these subproblems are independent of each other, they can be solved in parallel.

关键词： sequential quadratic programming pressure reducing valves PARALLEL PROCESSING (COMPUTERS) parallelization Network analysis

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A hybrid optimisation method for intercepting satellite trajectory based on differential game

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AERONAUTICAL JOURNAL 2023年第1312期127卷 900-922页

作者： Wu, W. Chen, J. Liu, J. Northwestern Polytech Univ ShaanXi Aerosp Flight Vehicle Design Key Lab Xian 710072 Shaanxi Peoples R China Beijing Inst Astronaut Syst Engn Beijing 100076 Peoples R China Sci & Technol Space Phys Lab Beijing 100076 Peoples R China

This study addresses orbit design and optimisation for the situation of satellite interception in which the target spacecraft is capable of manoeuvring using continuous magnitude restricted thrust. For the purpose of designing a long-range continuous thrust interception orbit, the orbit motion equations of two satellites with J2 perturbation are constructed. This problem is assumed to be a typical pursuit-evasion problem in differential game theory;using boundary constraint conditions and a performance index function that includes time and fuel consumption, the saddle point solution corresponding to the bilateral optimal is derived, and then this pursuit-evasion problem is transformed into a two-point boundary value problem. A hybrid optimisation method using a genetic algorithm (GA) and sequential quadratic programming (SQP) is derived to obtain the optimal control strategy. The proposed model and algorithm are proved to be feasible for the given simulation cases.

关键词： Satellite interception Differential game Genetic algorithm sequential quadratic programming Orbit design and optimisation

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Computational Design of Microvascular Radiative Cooling Panels for Nanosatellites

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2018年第3期32卷 605-616页

作者： Tan, Marcus Hwai Yik Bunce, Devin Ghosh, Alexander R. M. Geubelle, Philippe H. Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn 306F Talbot Lab104 South Wright St Urbana IL 61801 USA

This novel application of microvascular panels as nanosatellite radiator panels involves the key challenge of satisfying design constraints involving the coolant temperatures and pressure drop across the microchannel network. To address this challenge, the radiator panels are represented by dimensionally reduced hydraulic and nonlinear thermal models. The interface-enriched generalized finite element method and the Newton-Raphson scheme are then combined to solve the resulting nonlinear equations. Next, an interface-enriched generalized finite element method-based sensitivity analysis of the nonlinear equations is developed and combined with an existing sequential quadratic programming algorithm to solve an optimization problem specifically formulated to optimize the thermal performance of the radiator. The resulting thermal performance of the optimized designs is not only superior to that of the reference designs but is also in excellent agreement with an analytical model derived based on the assumption of near-monotonic variation in the coolant temperature along the microchannel network. A feasibility study on a reference design and an optimized design shows that only the latter can satisfy all design constraints with appropriately chosen flow rates. Solutions of the thermal and hydraulic models are also verified with ANSYS FLUENT simulations.

关键词： Coolant Nanosatellite Generalized Finite Element Method Radiator Panel Dirac Delta Function ANSYS FLUENT Thermal Performance sequential quadratic programming Radiative Cooling Thermal Modeling

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Non-monotone trust region methods for nonlinear equality constrained optimization without a penalty function

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MATHEMATICAL programming 2003年第1期95卷 103-135页

作者： Ulbrich, M Ulbrich, S Tech Univ Munich Zentrum Math D-80290 Munich Germany

We propose and analyze a class of penalty-function-free nonmonotone trust-region methods for nonlinear equality constrained optimization problems. The algorithmic framework yields global convergence without using a merit function and allows nonmonotonicity independently for both, the constraint violation and the value of the Lagrangian function. Similar to the Byrd-Omojokun class of algorithms, each step is composed of a quasi-normal and a tangential step. Both steps are required to satisfy a decrease condition for their respective trust-region subproblems. The proposed mechanism for accepting steps combines nonmonotone decrease conditions on the constraint violation and/or the Lagrangian function, which leads to a flexibility and acceptance behavior comparable to filter-based methods. We establish the global convergence of the method. Furthermore, transition to quadratic local convergence is proved. Numerical tests are presented that confirm the robustness and efficiency of the approach.

关键词： nonmonotone trust-region methods sequential quadratic programming penalty function global convergence equality constraints local convergence large-scale optimization

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