Formulating the optimization problem when using sequential quadratic programming applied to a simple LNG process

Wahl, Per Eilif; Løvseth, Sigurd Weidemann

doi:10.1016/j.compchemeng.2015.06.003

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…The intervals divide the entire temperature range of the overall hot composite curve with the same temperature distance. Wahl and Lovseth investigated the effects of the number of intervals as well as the model formulation on the level of success of LNG plant optimizations using sequential quadratic programming. The method developed by Kamath et al was employed to obtain the composite curves and to check the feasibility of the MSHE in this model …”

Section: Model Descriptionmentioning

confidence: 99%

“…The optimizations are sensitive to the formulation of the problem, bounds for the decision variables, and starting points. This effect was analyzed in detail by Wahl and Lovseth . As a result of these issues, dozens of optimization runs are performed in this study.…”

Section: Model Descriptionmentioning

confidence: 99%

“…This effect was analyzed in detail by Wahl and Lovseth. 22 As a result of these issues, dozens of optimization runs are performed in this study. The bounds and starting points are changed heuristically by considering the value change of the feasibility condition and the objective function during optimization.…”

mentioning

confidence: 99%

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Comparison of Multistage Compression Configurations for Single Mixed Refrigerant Processes

Tak

Lee

Kwon

et al. 2015

Ind. Eng. Chem. Res.

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This study investigates the effects of multistage compression on single mixed refrigerant processes in terms of specific work. Comparison of specific work published in the literature is not straightforward due to the variety of compression configurations and the design bases. Therefore, four configurations (two-, three-, and four-stage and pump-added three-stage compressions) along with three natural gas compositions were considered. To compare with the simulation and optimization results in the literature, these 12 cases, having the same design basis, were optimized by adjusting the optimization variables such as the flow rate and composition of the refrigerant, the compression ratio of each compressor, the inlet pressure of the first compressor, and the outlet temperatures of the hot and cold refrigerant streams. There were two important findings: (1) adding a pump reduces specific work more than adding a compressor or decreasing the minimum temperature difference value in the compressors; (2) among the four configurations, the refrigerant composition does not significantly change, although it greatly affects the efficiency. The former results from the compressor constraint of the gaseous inlet and the latter from the minimum temperature constraint of the multistream heat exchanger. Furthermore, direct comparisons to other studies were also performed showing the importance of optimization and the effect of the design basis.

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Section: Model Descriptionmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

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Comparison of Multistage Compression Configurations for Single Mixed Refrigerant Processes

Tak

Lee

Kwon

et al. 2015

Ind. Eng. Chem. Res.

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“…Hence, ASM is suitable to regulate the parameters. ASM has been implemented in many recent submissions, such as trade and industry load dispatch models [49], short-term hydrothermal supervision [50], bipedal walking robot dynamics [51], economic multiproduct manufacture [52], LNG process [53], model of heating in the thermal blow frame cycling [54], aircraft transportation [55], wind turbine support structures [56] and quadratic convex bilevel programming models [57]. In this study, the combination of GAASM is implemented to solve the second kind of LE-NSM, and the optimization process of GAASM is provided in Table 1.…”

Section: Network Optimizationmentioning

confidence: 99%

Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

et al. 2021

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In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

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“…Kamath et al (2012) combined the internal equation of state code with a general algebraic modeling system (GAMS) and adopted nonlinear programming to optimize the SMR process. Wahl and Løvseth (2015) applied the sequential quadratic programming method to investigate the influence of the model formula on the SMR process optimization. Various aspects of the formula, including the optimization variables and their boundaries, internal node numbers, starting points, and derivative estimates, were studied.…”

Section: Introductionmentioning

confidence: 99%

Optimization Design and Analysis of Single-Stage Mixed Refrigerant Liquefaction Process

Wang

Dai

et al. 2021

Front. Energy Res.

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Small-scale natural gas liquefaction processes have several clear advantages, particularly in the exploitation of ‘unconventional’ natural gas (NG) from sources such as difficult-to-access and offshore gas fields. Moreover, conventional liquefaction processes have a number of disadvantages such as high energy consumption, large cooling loads required in the refrigeration cycle, and non-uniform matching of cold and hot flows in liquified natural gas (LNG) heat exchanger (HE). The main objective of this study was to optimize the most commonly used mixed refrigerant process. The liquefaction performance of the optimized process was analyzed and the influence of gas parameters on the power consumption, exergy loss, freezing mixture circulation, and cooling water load were investigated. The results show that compressor power consumption can be reduced by 29.8%, the cooling water load can be reduced by 21.3%, and the system exergy efficiency can be increased by 41% with the optimized process. Furthermore, throttling and compression of the freezing mixture were increased during the refrigeration stage. It can be concluded that reducing the feed gas temperature and increasing the feed gas pressure can reduce the total power consumption, exergy loss, freezing mixture circulation, and cooling water load, which can significantly improve liquefaction performance.

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Formulating the optimization problem when using sequential quadratic programming applied to a simple LNG process

Cited by 17 publications

References 7 publications

Comparison of Multistage Compression Configurations for Single Mixed Refrigerant Processes

Comparison of Multistage Compression Configurations for Single Mixed Refrigerant Processes

Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

Optimization Design and Analysis of Single-Stage Mixed Refrigerant Liquefaction Process

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