| 1 |
A case study of space-time performance comparison of wind turbines on a wind farm
Ding Y, Kumar N, Prakash A, Kio AE, Liu X, Liu L, Li QC
Renewable Energy, 171, 735, 2021 |
| 2 |
Data science-based modeling of the lysine fermentation process
Tokuyama K, Shimodaira Y, Terawaki T, Kusunose Y, Nakai H, Tsuji Y, Toya Y, Matsuda F, Shimizu H
Journal of Bioscience and Bioengineering, 130(4), 409, 2020 |
| 3 |
A data-driven approach for predicting nepheline crystallization in high-level waste glasses
Sargin I, Lonergan CE, Vienna JD, McCloy JS, Beckman SP
Journal of the American Ceramic Society, 103(9), 4913, 2020 |
| 4 |
The promise of artificial intelligence in chemical engineering: Is it here, finally?
Venkatasubramanian V
AIChE Journal, 65(2), 466, 2019 |
| 5 |
Blind identification of fully observed linear time-varying systems via sparse recovery
Dobbe R, Liu S, Yuan Y, Tomlin C
Automatica, 100, 330, 2019 |
| 6 |
Realistic interplays between data science and chemical engineering in the first quarter of the 21st century: Facts and a vision
Piccione PM
Chemical Engineering Research & Design, 147, 668, 2019 |
| 7 |
Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables
Fujinami M, Seino J, Nukazawa T, Ishida S, Iwamoto T, Nakai H
Chemistry Letters, 48(8), 961, 2019 |
| 8 |
SS-DAC: A systematic framework for selecting the best modeling approach and pre-processing for spectroscopic data
Rato TJ, Reis MS
Computers & Chemical Engineering, 128, 437, 2019 |
| 9 |
What's in the box?! Towards explainable machine learning applied to non-residential building smart meter classification
Miller C
Energy and Buildings, 199, 523, 2019 |
| 10 |
Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling
Radaideh MI, Kozlowski T
International Journal of Energy Research, 43(14), 7866, 2019 |
