Efficient Surrogate-Based Optimization of Prefractionation Column Using Self-Adaptive Kriging Model with Modified Firefly Algorithm Article Swipe
Yifan Huang
,
Qibing Jin
,
Bin Wang
,
Yifan Huang
,
Qibing Jin
,
Bin Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app152211962
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app152211962
The optimization of distillation columns is critically important due to their substantial contribution to operational costs in the petrochemical industry. This paper introduces a computationally efficient surrogate-based optimization framework designed explicitly for prefractionation columns. To address the challenges of high computational cost and model accuracy in model-based optimization, a self-adaptive Kriging model, which features automated hyperparameter tuning via Bayesian optimization, is implemented and trained using Latin hypercube sampling of historical process data. By integrating a self-adaptive Kriging model with a modified firefly algorithm, the framework efficiently identifies optimal operating conditions that maximize economic profit while adhering to operational constraints. Case studies demonstrate that the proposed framework achieves superior economic performance, increasing the average final profit by 0.17–0.31% compared to non-adaptive surrogate benchmarks. Furthermore, it is exceptionally stable, achieving a minimal relative standard deviation of only 0.037% in the final profit across 30 independent runs, significantly lower than the 0.266% and 0.237% achieved by the benchmark methods. This study provides a practical and efficient tool to optimize complex distillation columns with limited computational resources.
Related Topics
Concepts
Kriging
Computer science
Firefly algorithm
Mathematical optimization
Latin hypercube sampling
Hyperparameter
Bayesian optimization
Fractionating column
Benchmark (surveying)
Algorithm
Process (computing)
Distillation
Surrogate model
Profit (economics)
Optimization problem
Workflow
Matching (statistics)
Bayesian probability
Discretization
Biomanufacturing
Metamodeling
Firefly protocol
Optimization algorithm
Hyperparameter optimization
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app152211962
- https://www.mdpi.com/2076-3417/15/22/11962/pdf?version=1762854432
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7104695535
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7104695535Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app152211962Digital Object Identifier
- Title
-
Efficient Surrogate-Based Optimization of Prefractionation Column Using Self-Adaptive Kriging Model with Modified Firefly AlgorithmWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-11-11Full publication date if available
- Authors
-
Yifan Huang, Qibing Jin, Bin Wang, Yifan Huang, Qibing Jin, Bin WangList of authors in order
- Landing page
-
https://doi.org/10.3390/app152211962Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/15/22/11962/pdf?version=1762854432Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/22/11962/pdf?version=1762854432Direct OA link when available
- Concepts
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Kriging, Computer science, Firefly algorithm, Mathematical optimization, Latin hypercube sampling, Hyperparameter, Bayesian optimization, Fractionating column, Benchmark (surveying), Algorithm, Process (computing), Distillation, Surrogate model, Profit (economics), Optimization problem, Workflow, Matching (statistics), Bayesian probability, Discretization, Biomanufacturing, Metamodeling, Firefly protocol, Optimization algorithm, Hyperparameter optimizationTop concepts (fields/topics) attached by OpenAlex
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