| 학회 | 한국공업화학회 |
| 학술대회 | 2020년 가을 (10/28 ~ 10/30, 광주 김대중컨벤션센터(Kimdaejung Convention Center)) |
| 권호 | 24권 1호 |
| 발표분야 | [국제협력] 우수외국인연구자세션 |
| 제목 | Predictive Analysis and Optimization of the Oxidative Dehydrogenation of Butylene to Butadiene using Machine Learning |
| 초록 | In this research, the Computational Fluid Dynamics (CFD) simulation of Butadiene over a ferrite catalyst in a 3-dimensional shell and tube multi-tubular reactor was carried out. A rigorous mathematical formulation of the process kinetics and heat transfer was incorporated into an OpenFOAM CFD model using a porous media. The model results showed close agreements with experimental data when compared. The CFD model was then used to generate data sets for developing a predictive machine-learning algorithm. The results obtained from the predictive algorithm were validated against the CFD model with minimal error. To enhance the efficiency and efficacy of the reactor system and to minimize carbon generation, the predictive model was further expanded to include optimization of the key process parameters, thereby improving the performance of the reactor. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019 R1F1A1058979) |
| 저자 | Gbadago Dela Quarme1, 문지영1, 황성원2 |
| 소속 | 1인하대, 2Inha Univ. |
| 키워드 | Reactor Design; CFD; reaction; machine learning; optimization |
