Optimization of reactor network design problem using Jumping Gene Adaptation of Differential Evolution

Gujarathi, Ashish M.; Purohit, Sharadkumar P.; Srikanth, B.

doi:10.1088/1742-6596/622/1/012044

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Furthermore, it would be interesting to implement various optimization techniques showing promising results such as Eltist-MODE, DE, and MDE methods to minimize aerosol formation in PCCC columns. 31,32 In terms of interaction energy, notably, the mixture of MDEA and MEA increased the CO 2 −ethanolamines interaction energy, implying that the ethanolamine mixture facilitates the solvent's absorption capability. Our future studies will explore the sensitivity of PM formation on other ethanolamines at different temperatures and pressures.…”

Section: Discussionmentioning

confidence: 99%

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Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO₂ Capture Columns

Shah

Saparov

Mansurov

et al. 2020

Ind. Eng. Chem. Res.

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Ethanolamines have traditionally been used for capturing CO 2 in a postcombustion carbon capture column. However, the use of ethanolamines, due to their high volatility and solvent losses, is not sustainable. One of the ways in which the solvent loss occurs is in the form of aerosols from the top of the column. The mechanism, rate of nucleation, growth rate, and interaction leading to the formation of aerosols, although essential for better process design, remain obscure. Using molecular dynamics simulations, we herein, analyze the formation of aerosols in columns based on aqueous monoethanolamine (MEA), aqueous methyldiethanolamine (MDEA), and their mixtures, using reference, pilot-scale, and industrial-scale data. In particular, the nucleation rate and cluster growth analyses were performed for five different cases. The results show that CO 2 concentration had a strong influence on the rate of aerosol formation, a factor that can be easily controlled for better process design. Moreover, the interactions within the formed aerosols were mainly dominated by CO 2 −water interactions. Taken together, our results and analysis contribute toward a better understanding of aerosol formation and present some practical value, namely, calculated nucleation rates and particulate growth rates can be used in process simulators to account for solvent losses, factors that were identified as contributing to formation of particulate matter can be controlled and adjusted in design process simulations and in real plants, providing better performance of postcombustion carbon capture columns and thus suggest ways to prevent solvent loss.

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Section: Discussionmentioning

confidence: 99%

“…However, temperature manipulation should not lead to supersaturation conditions in the vapor phase during the absorption. Furthermore, it would be interesting to implement various optimization techniques showing promising results such as Eltist-MODE, DE, and MDE methods to minimize aerosol formation in PCCC columns. , …”

Section: Discussionmentioning

confidence: 99%

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO₂ Capture Columns

Shah

Saparov

Mansurov

et al. 2020

Ind. Eng. Chem. Res.

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“…We used CDTLZ [35] test suite, MW [33] test suite, DASCMOP [36] test suite, DOC [27] test suite and LIRCMOP [15] For real-world CMOPs, we use three real-world CMOPs to test C3M and comparison algorithm. They are bulk carrier design [37], pressure vessel design [3], and reactor network design [38]; their parameter settings are shown in Table II. The bulk carrier design problem is a CMOP with nine inequality constraints.…”

Section: A Benchmark Suites and Real-world Cmopsmentioning

confidence: 99%

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

Sun

Zou

Liu

et al. 2023

IEEE Trans. Evol. Computat.

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There are usually multiple constraints in constrained multi-objective optimization. Those constraints reduce the feasible area of the constrained multi-objective optimization problems (CMOPs) and make it difficult for current multiobjective optimization algorithms (CMOEAs) to obtain satisfactory feasible solutions. In order to solve this problem, this paper studies the relationship between constraints, then obtains the priority between constraints according to the relationship between the Pareto Front (PF) of the single constraint and their common PF. Meanwhile, this paper proposes a multi-stage CMOEA and applies this priority, which can save computing resources while helping the algorithm converge. The proposed algorithm completely abandons the feasibility in the early stage to better explore the objective space, and obtains the priority of constraints according to the relationship; Then the algorithm evaluates a single constraint in the medium stage to further explore the objective space according to this priority, and abandons the evaluation of some less-important constraints according to the relationship to save the evaluation times; At the end stage of the algorithm, the feasibility will be fully considered to improve the quality of the solutions obtained in the first two stages, and finally get the solutions with good convergence, feasibility, and diversity. The results on five CMOP suites and three realworld CMOPs show that the algorithm proposed in this paper can have strong competitiveness in existing constrained multiobjective optimization.

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Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions

Sharma,

Raju

2023

Soft Comput

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Optimization of reactor network design problem using Jumping Gene Adaptation of Differential Evolution

Cited by 3 publications

References 11 publications

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO₂ Capture Columns

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO₂ Capture Columns

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions

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Optimization of reactor network design problem using Jumping Gene Adaptation of Differential Evolution

Cited by 3 publications

References 11 publications

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO2 Capture Columns

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO2 Capture Columns

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions

Contact Info

Product

Resources

About

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO₂ Capture Columns

Molecular Dynamics Simulations To Capture Nucleation and Growth of Particulates in Ethanolamine-Based Post-Combustion CO₂ Capture Columns