2023 Fiscal Year Final Research Report
Machine Learning to Estimat the Behavior of a Batch Process
| Project/Area Number |
21K04766
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 27020:Chemical reaction and process system engineering-related
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | プロセスモニタリング / バッチプロセス / 化学プラント / 機械学習 / モデル化 |
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| Outline of Final Research Achievements |
Batch plants are used in the chemical and pharmaceutical industries. They are important for quality control, efficiency improvement, and optimization. Recently, data-driven modeling techniques have been used to achieve these objectives. However, batch plants often produce many different products in small quantities, which makes it difficult to construct models with sufficient accuracy. We developed a data-driven method that can create highly accurate models with limited data. It uses data from different batches, including data from different products.
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| Free Research Field |
プロセスシステム工学
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| Academic Significance and Societal Importance of the Research Achievements |
この研究はデータ駆動型機械学習を発展し，学習データの不足という問題を解決する新たなアプローチを提案しています．この手法は、少量のデータからも高精度な予測モデルを構築可能とし，機械学習の理論と実践のギャップを埋める学術的意義の高いものです．
社会的には，この研究成果は化学産業や製薬産業におけるバッチプラントの生産性や効率，品質，安全性を向上させることに直接寄与します．エネルギー消費の削減や原料の使用効率を高め，環境負荷の軽減にも繋がります．また，作業員のリスクを減少させ，より持続可能な製造業の実現を支援します．
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