Surrogate-assisted modeling and optimization of a natural-gas liquefaction plant

Ali, Wahid; Khan, Mohd Shariq; Qyyum, Muhammad Abdul; Lee, Moonyong

doi:10.1016/j.compchemeng.2018.08.003

Cited by 41 publications

(17 citation statements)

References 51 publications

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“…Reduced function evaluation requirements and improved computation costs for optimisation are some of the benefits shown by from the topic of surrogate assisted optimisation [11], [20], [31], [35]- [39]. Wahid et al [36] investigated minimising the compression energy in a single mixed refrigerant process of natural gas liquefaction. The paper illustrated significant time reductions by using surrogate assisted optimisation, specifically using Radial basis functions as their surrogate model.…”

Section: Surrogate Assisted Optimisationmentioning

confidence: 99%

Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study

Stander

Woolway

Zyl

2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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Declaration of AuthorshipI, Liezl STANDER (715347), declare that this Dissertation titled, "Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study" and the work presented in it are my own. I confirm that:• This work was done wholly or mainly while in candidature for a research degree at this University.• Where any part of this Dissertation has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated.• Where I have consulted the published work of others, this is always clearly attributed.• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this Dissertation is entirely my own work.• I have acknowledged all main sources of help. • Where the Dissertation is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.

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Section: Surrogate Assisted Optimisationmentioning

confidence: 99%

Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study

Stander

Woolway

Zyl

2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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“…Numerous techniques have been presented in the surrogate-assisted chemical engineering optimisation literature. Wahid et al [1] and Shi et al [50] made use of Radial basis functions as their surrogate model for minimising compression energy in a single mixed refrigerant process of natural gas liquefaction and optimising crude oil distillation units. A Kriging surrogate model was implemented by Beck et al [5] in optimising the design of a vacuum/pressure swing adsorption system.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system

Stander¹,

Woolway²,

Zyl³

2022

Preprint

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Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs for these systems' mathematical formulations and simulation models. Research has illustrated the benefits of using machine learning surrogate models as substitutes for computationally expensive models during optimisation. This paper extends recent research into optimising chemical plant design and operation. The study further explores Surrogate Assisted Genetic Algorithms (SA-GA) in more complex variants of the original plant design and optimisation problems, such as the inclusion of parallel and feedback components. The novel extension to the original algorithm proposed in this study, Surrogate Assisted NSGA-II (SA-NSGA), was tested on a popular literature case, the Pressure Swing Adsorption (PSA) system. We further provide extensive experimentation, comparing various meta-heuristic optimisation techniques and numerous machine learning models as surrogates. The results for both sets of systems illustrate the benefits of using Genetic Algorithms as an optimisation framework for complex chemical plant system design and optimisation for both single and

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“…Khan [10] used particle swarm optimization to optimize SMR process. Compared with the above two methods, the radial basis function combined with thin plate spline method used by Ali [11] can obtain optimization results in a short time, thus obtaining an alternative model of SMR process and reducing the calculation amount of simulation optimization.…”

Section: Introductionmentioning

confidence: 99%

Operation analysis of C3-MR process cold box by grey system theory

Yang

Xiao

Jia³

2021

SN Appl. Sci.

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In this paper, the problems of high refrigerant line differential pressure and uneven distribution of cold energy in cold box regulation under C3-MR process are studied. Five reasons are predicted by engineering performance. Using gas chromatography experiment and grey system pure mathematics analysis, it is determined that the main causes of the problem are unreasonable distribution ratio of each group of mixed refrigerants and disordered latent heat of vaporization of refrigerants. Furthermore, the grey system model is used to study: 1. grey relation analysis model shows that the correlation degree of T3 temperature measuring point is 0.8552, which is the only main factor. The abnormal working condition is determined by the project to be caused by incorrect proportion of N2 components. 2. According to GM(1,N) model, the driving term of T3 temperature measuring point is 3.8304, which needs to be supplemented with N2 component to eliminate the problem. 3. After adding N2 to 10% (mol component), abnormal working conditions disappeared. The GM(1,N) model is used again to verify that the difference of driving results is small, the average relative error is 24.91%, and the accuracy of the model is in compliance.

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Surrogate-assisted modeling and optimization of a natural-gas liquefaction plant

Cited by 41 publications

References 51 publications

Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study

Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study

Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system

Operation analysis of C3-MR process cold box by grey system theory

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