| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Outline of Final Research Achievements |
We aim to develop a new method that can optimize operating conditions of commercial-scale equipment to achieve scale-up from pilot-scale equipment even when only a small number of experiments can be performed with commercial-scale equipment. The proposed method, combined task Bayesian optimization (CTBO), uses not only data of a target task, e.g., a commercial-scale plant, but also data of a source task, e.g., a pilot-scale plant. CTBO determines new operating conditions in the target task sequentially by BO while information of the source task is exploited by transfer learning. CTBO was compared with BO and LW-PLS + jDE (locally weighted partial least squares + self-adaptive differential evolution) through their applications to a pharmaceutical granulation process. CTBO remarkably outperformed the other methods. CTBO is expected to be useful not only for scale-up but also for technology transfer.
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