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Multi-objective optimization of subsurface CO2 capture, utilization, and storage using sequential quadratic programming with stochastic gradients

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COMPUTATIONAL GEOSCIENCES 2024年第2期28卷 195-210页

作者： Nguyen, Quang Minh Onur, Mustafa Alpak, Faruk Omer Univ Tulsa Petr Engn Dept Tulsa OK 74104 USA Shell Int Explorat & Prod Inc Shell Technol Ctr Houston Houston TX 77082 USA

Carbon capture, utilization, and storage (CCUS) is a crucial part of the energy industry nowadays, aiming to reduce the overall carbon emission into the environment. One solution to CCUS is via the means of CO2 enhanced recovery processes in a depleted oil reservoir. In such a case, life-cycle production optimization plays a crucial component, referring to optimizing a production-driven objective function via varying well controls during a reservoir's lifetime. One challenge is to obtain the optimal cash flow while trying to maintain the maximum CO2 storage. Another challenge is the nonlinear constraints (such as field liquid production rate) which need to be honored due to the capacity of the processing facilities. This study presents an application of a stochastic gradient-based framework to solve the CO2 storage multi-objective optimization problem. Our study focuses on carbon capture and storage via the means of nonlinearly constrained production optimization workflow for a CO2 enhanced recovery process, in which we aim to bi-objectively maximize both the net-present-value (NPV) and the net present carbon tax credits (NPCTC). The main framework used in this work is line-search sequential quadratic programming (LS-SQP) with stochastic simplex approximated gradients (StoSAG). We demonstrate the performance and results of the algorithmic framework in a field-scale realistic problem. The case study being investigated is a multiphase flow Brugge model under CO2 injection, simulated using a commercial compositional reservoir simulator. Results show that the LS-SQP algorithm with StoSAG gradients performs computationally efficiently and effectively in handling nonlinear state constraints imposed onto the problem. The workflow successfully solves both the single-objective and the multi-objective optimization problems with minimal and acceptable constraint violations. Various numerical settings have been experimented with to estimate the Pareto front for the bi-objective

关键词： Multi-objective optimization CCUS Well control optimization Pareto front sequential quadratic programming Stochastic simplex approximate gradient Brugge model Reservoir simulation

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Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期44卷 591-596页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands over a certain period of time so as to operate an electric power system most economically. This paper proposes a hybrid methodology integrating bee colony optimization with sequential quadratic programming for solving dynamic economic dispatch problem of generating units considering valve-point effects. This hybrid method incorporates bee colony optimization as a base level search which can give a good direction to the optimal region and sequential quadratic programming as a local search procedure which is used to fine tune that region for achieving the final solution. Numerical results of a ten-unit system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared with those obtained from hybrid of particle swarm optimization and sequential quadratic programming and hybrid of evolutionary programming and sequential quadratic programming. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Dynamic economic dispatch Bee colony optimization sequential quadratic programming

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An efficient interior point method for sequential quadratic programming based optimal power flow

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IEEE TRANSACTIONS ON POWER SYSTEMS 2000年第4期15卷 1179-1183页

作者： Nejdawi, IM Clements, KA Davis, PW Worcester Polytech Inst Worcester MA 01609 USA

This paper presents a new sequential quadratic programming algorithm for solving the optimal power flow problem. The algorithm is structured with an outer linearization loop and an inner optimization loop. The inner loop solves a relaxed reduced quadratic programming problem. Because constraint relaxation keeps the inner loop problem of small dimension, the algorithm is quite efficient. Its outer loop iteration counts are comparable to Newton power flow, and the inner loops are efficient interior point iterations. Several IEEE test systems were run. The results indicate that both outer and inner loop iteration counts do not vary greatly with problem size.

关键词： interior-point methods nonlinear programming optimal power flow sequential quadratic programming

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Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2008年第33-40期197卷 3080-3091页

作者： Fesanghary, M. Mahdavi, M. Minary-Jolandan, M. Alizadeh, Y. Amir Kabir Univ Technol Dept Mech Engn Tehran 158754413 Iran Sharif Univ Technol Dept Comp Engn Tehran 113658639 Iran Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA

This study presents a hybrid harmony search algorithm (HHSA) to solve engineering optimization problems with continuous design variables. Although the harmony search algorithm (HSA) has proven its ability of finding near global regions within a reasonable time, it is comparatively inefficient in performing local search. In this study sequential quadratic programming (SQP) is employed to speed up local search and improve precision of the HSA solutions. Moreover, an empirical study is performed in order to determine the impact of various parameters of the HSA on convergence behavior. Various benchmark engineering optimization problems are used to illustrate the effectiveness and robustness of the proposed algorithm. Numerical results reveal that the proposed hybrid algorithm, in most cases is more effective than the HSA and other meta-heuristic or deterministic methods. (C) 2008 Elsevier B.V. All rights reserved.

关键词： heuristic harmony search algorithm hybridization sequential quadratic programming engineering optimization

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An improved sequential quadratic programming algorithm for solving general nonlinear programming problems

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JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 2014年第2期409卷 777-789页

作者： Guo, Chuan-Hao Bai, Yan-Qin Jian, Jin-Bao Shanghai Univ Dept Math Shanghai 200444 Peoples R China Guangxi Univ Dept Math & Informat Sci Nanning 530004 Guangxi Peoples R China Yulin Normal Univ Headmasters Off Yulin 537000 Guangxi Peoples R China

In this paper, a class of general nonlinear programming problems with inequality and equality constraints is discussed. Firstly, the original problem is transformed into an associated simpler equivalent problem with only inequality constraints. Then, inspired by the ideals of the sequential quadratic programming (SQP) method and the method of system of linear equations (SLE), a new type of SQP algorithm for solving the original problem is proposed. At each iteration, the search direction is generated by the combination of two directions, which are obtained by solving an always feasible quadratic programming (QP) subproblem and a SLE, respectively. Moreover, in order to overcome the Maratos effect, the higher-order correction direction is obtained by solving another SLE. The two SLEs have the same coefficient matrices, and we only need to solve the one of them after a finite number of iterations. By a new line search technique, the proposed algorithm possesses global and superlinear convergence under some suitable assumptions without the strict complementarity. Finally, some comparative numerical results are reported to show that the proposed algorithm is effective and promising. (C) 2013 Elsevier Inc. All rights reserved.

关键词： General nonlinear programming sequential quadratic programming Method of quasi-strongly sub-feasible directions Global convergence Superlinear convergence

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Memetic computing through bio-inspired heuristics integration with sequential quadratic programming for nonlinear systems arising in different physical models

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SPRINGERPLUS 2016年第1期5卷 2063页

作者： Raja, Muhammad Asif Zahoor Kiani, Adiqa Kausar Shehzad, Azam Zameer, Aneela COMSATS Inst Informat Technol Dept Elect Engn Attock Campus Attock Pakistan Fed Urdu Univ Arts Sci & Technol Dept Econ Islamabad Pakistan Preston Univ Dept Math Islamabad Campus Islamabad Pakistan PIEAS Dept Comp & Informat Sci Islamabad 45650 Pakistan

Background: In this study, bio-inspired computing is exploited for solving system of nonlinear equations using variants of genetic algorithms (GAs) as a tool for global search method hybrid with sequential quadratic programming (SQP) for efficient local search. The fitness function is constructed by defining the error function for systems of nonlinear equations in mean square sense. The design parameters of mathematical models are trained by exploiting the competency of GAs and refinement are carried out by viable SQP algorithm. Results: Twelve versions of the memetic approach GA-SQP are designed by taking a different set of reproduction routines in the optimization process. Performance of proposed variants is evaluated on six numerical problems comprising of system of nonlinear equations arising in the interval arithmetic benchmark model, kinematics, neurophysiology, combustion and chemical equilibrium. Comparative studies of the proposed results in terms of accuracy, convergence and complexity are performed with the help of statistical performance indices to establish the worth of the schemes. Conclusions: Accuracy and convergence of the memetic computing GA-SQP is found better in each case of the simulation study and effectiveness of the scheme is further established through results of statistics based on different performance indices for accuracy and complexity.

关键词： Nonlinear systems Hybrid computing Genetic algorithms sequential quadratic programming Nonlinear equations

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A sequential quadratic programming algorithm for nonlinear minimax problems

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BULLETIN OF THE AUSTRALIAN MATHEMATICAL SOCIETY 2007年第3期76卷 353-368页

作者： Hu, Qing-jie Hu, Ju-zhou Hunan Business Coll Dept Informat Changsha 410205 Peoples R China Hunan Univ Inst Appl Math Changsha 410082 Peoples R China

In this paper, an active set sequential quadratic programming algorithm with non-monotone line search for nonlinear minmax problems is presented. At each iteration of the proposed algorithm, a main search direction is obtained by solving a reduced quadratic program which always has a solution. In order to avoid the Maratos effect, a correction direction is yielded by solving the reduced system of linear equations. Under mild conditions without the strict complementarity, the global and superlinear convergence can be achieved. Finally, some preliminary numerical experiments axe reported.

关键词： sequential quadratic programming Linear system of equations Linear equation Supralinearity line retrieval Complementarity iterative methods

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A computationally efficient feasible sequential quadratic programming algorithm

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SIAM JOURNAL ON OPTIMIZATION 2001年第4期11卷 1092-1118页

作者： Lawrence, CT Tits, AL Alphatech Inc Arlington VA 22201 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Syst Res Inst College Pk MD 20742 USA

A sequential quadratic programming (SQP) algorithm generating feasible iterates is described and analyzed. What distinguishes this algorithm from previous feasible SQP algorithms proposed by various authors is a reduction in the amount of computation required to generate a new iterate while the proposed scheme still enjoys the same global and fast local convergence properties. A preliminary implementation has been tested and some promising numerical results are reported.

关键词： sequential quadratic programming SQP feasible iterates feasible SQP FSQP

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MHD slip flow through nanofluids for thermal energy storage in solar collectors using radiation and conductivity effects: A novel design sequential quadratic programming-based neuro-evolutionary approach

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MODERN PHYSICS LETTERS B 2024年第0期

作者： Butt, Zeeshan Ikram Ahmad, Iftikhar Raja, Muhammad Asif Zahoor Hussain, Syed Ibrar Ilyas, Hira Shoaib, Muhammad Univ Gujrat Dept Math Gujrat 50700 Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Yunlin 64002 Taiwan Univ Palermo Dept Math & Comp Sci Via Archirafi 34 I-90123 Palermo Italy Univ Houston Dept Math Houston TX 77004 USA Univ Chenab Dept Phys Sci Gujrat 50700 Pakistan Yuan Ze Univ Artificial Intelligence Cross Domain Innovat Appli Taoyuan Taiwan

In this research, a novel design stochastic numerical technique is presented to investigate the unsteady form magnetohydrodynamic (MHD) slip flow along the boundary layer to analyze the transportation and heat transfer in a solar collector through nano liquids which is a revolution in the field of neurocomputing. Thermal conductivity in variable form is dependent on temperature and wall slips are assumed over the boundary. For mathematical modeling, the solar collector is assumed in the form of a nonlinear stretching sheet and a quite new artificial neural networks (ANNs) based approach is used to solve the current problem in which inverse multiquadric radial basis (IMRB) kernel is sandwiched between a global search solver named genetic algorithms (GAs) and a highly effective local solver named sequential quadratic programming (SQP) i.e. IMRB-GASQP solver. The governing boundary value problem is altered in the form of a system of nonlinear ordinary differential equations (ODEs) through the utilization of similarity transformation and then the obtained system of ODEs is solved using IMRB-GASQP solver by altering the values of distinguished parameters involved in it to observe the fluctuation in the velocity and temperature profiles of nanofluid. The obtained results are effectively compared with the reference solutions using the Adams numerical technique in graphical and tabulated form. An exhaustive error analysis using performance operators is presented while the efficacy of the designed solver using various statistical operators is also part of this research.

关键词： Magnetohydrodynamic artificial neural networks genetic algorithms sequential quadratic programming inverse multiquadric radial basis function nanofluid heat transfer

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A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using sequential quadratic programming

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2020年第2期29卷 261-273页

作者： Kim, Youngseok Carbonetto, Peter Stephens, Matthew Anitescu, Mihai Univ Chicago Dept Stat Chicago IL 60637 USA Univ Chicago Dept Human Genet Chicago IL 60637 USA Univ Chicago Res Comp Ctr Chicago IL 60637 USA Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Lemont IL 60439 USA

Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker and Mizera shows that modern convex optimization techniques-in particular, interior point methods-are substantially faster and more accurate than EM. Here, we develop a new solution based on sequential quadratic programming (SQP). It is substantially faster than the interior point method, and just as accurate. Our approach combines several ideas: first, it solves a reformulation of the original problem;second, it uses an SQP approach to make the best use of the expensive gradient and Hessian computations;third, the SQP iterations are implemented using an active set method to exploit the sparse nature of the quadratic subproblems;fourth, it uses accurate low-rank approximations for more efficient gradient and Hessian computations. We illustrate the benefits of the SQP approach in experiments on synthetic datasets and a large genetic association dataset. In large datasets (n approximate to 106observations,m approximate to 103mixture components), our implementation achieves at least 100-fold reduction in runtime compared with a state-of-the-art interior point solver. Our methods are implemented in Julia and in an R package available on CRAN (). Supplementary materials for this article are available online.

关键词： Active set methods Convex optimization Mixture models Nonparametric empirical Bayes Rank-revealing QR decomposition sequential quadratic programming

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