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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.18529 |
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| _version_ | 1866916541764730880 |
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| author | Mitrai, Ilias Daoutidis, Prodromos |
| author_facet | Mitrai, Ilias Daoutidis, Prodromos |
| contents | Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we discuss recent advances in (i) the representation of decision-making problems for machine learning tasks, (ii) algorithm selection, and (iii) algorithm configuration for monolithic and decomposition-based algorithms. Finally, we discuss open problems related to the application of machine learning for accelerating process optimization and control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18529 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating process control and optimization via machine learning: A review Mitrai, Ilias Daoutidis, Prodromos Systems and Control Machine Learning Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we discuss recent advances in (i) the representation of decision-making problems for machine learning tasks, (ii) algorithm selection, and (iii) algorithm configuration for monolithic and decomposition-based algorithms. Finally, we discuss open problems related to the application of machine learning for accelerating process optimization and control. |
| title | Accelerating process control and optimization via machine learning: A review |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2412.18529 |