Reusable Surrogate Models for Distillation Columns
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916975745171456 |
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| author | Bubel, Martin Seidel, Tobias Bortz, Michael |
| author_facet | Bubel, Martin Seidel, Tobias Bortz, Michael |
| contents | Surrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks where traditional flowsheet simulators are computationally prohibitive. However, the state-of-the-art is dominated by surrogate models trained for a narrow range of fixed chemical systems and operating conditions, limiting their reusability. This work introduces a paradigm shift towards reusable surrogates by developing a single model for distillation columns that generalizes across a vast design space. The key enabler is a novel ML-fueled modelfluid representation which allows for the generation of datasets of more than $1,000,000$ samples. This allows the surrogate to generalize not only over column specifications but also over the entire chemical space of homogeneous ternary vapor-liquid mixtures. We validate the model's accuracy and demonstrate its practical utility in a case study on entrainer distillation, where it successfully screens and ranks candidate entrainers, significantly reducing the computational effort compared to rigorous optimization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reusable Surrogate Models for Distillation Columns Bubel, Martin Seidel, Tobias Bortz, Michael Computational Engineering, Finance, and Science Surrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks where traditional flowsheet simulators are computationally prohibitive. However, the state-of-the-art is dominated by surrogate models trained for a narrow range of fixed chemical systems and operating conditions, limiting their reusability. This work introduces a paradigm shift towards reusable surrogates by developing a single model for distillation columns that generalizes across a vast design space. The key enabler is a novel ML-fueled modelfluid representation which allows for the generation of datasets of more than $1,000,000$ samples. This allows the surrogate to generalize not only over column specifications but also over the entire chemical space of homogeneous ternary vapor-liquid mixtures. We validate the model's accuracy and demonstrate its practical utility in a case study on entrainer distillation, where it successfully screens and ranks candidate entrainers, significantly reducing the computational effort compared to rigorous optimization. |
| title | Reusable Surrogate Models for Distillation Columns |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.06638 |