Software
The following software packages have been developed by our group. We are not responsible for any risks or damages arising from the use of these packages.
Simulated moving bed (SMB) chromatography
Optimization of simulated moving bed chromatography, Pyomo DAEKensuke Suzuki, Doctoral Student
Reference: Suzuki et al. Journal of Advanced Manufacturing and Processing, e10103, 2021
https://doi.org/10.1002/amp2.10103
Uncertainty quantification, Bayesian estimation
Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantificationTatsuki Maruchi, M.S. March 2026
Reference：Maruchi et al. AIChE Journal, e70319, 2026
https://doi.org/10.1002/aic.70319
Uncertainty quantification of parameters in chromatographic process model by Bayesian estimation using sequential Monte Carlo
Yota Yamamoto, M.S. March 2021
Reference：Yamamoto et al. Chemical Engineering Research and Design, 175, 223-237, 2021
https://doi.org/10.1016/j.cherd.2021.09.003
Physics informed Neural Network
Parameter estimation for chromatographic process by Physics informed Neural NetworkZou Tao, currently PhD student in 2024
Reference: Zou et al. Journal of Chromatography A, 1730, 16, 465077, 2024
https://doi.org/10.1016/j.chroma.2024.465077