| 1 |
Advanced modeling of HPGR power consumption based on operational parameters by BNN: A "Conscious-Lab" development
Tohry A, Yazdani S, Hadavandi E, Mahmudzadeh E, Chelgani SC
Powder Technology, 381, 280, 2021 |
| 2 |
Separation Units 4.0-Trenntechnik in der chemischen Industrie auf dem Weg in die digitale Zukunft. Eindrucke und erste Ergebnisse vom Tutzing-Symposion 2019
Bart HJ, Grunewald M, Repke JU, Scholl S
Chemie Ingenieur Technik, 92(7), 807, 2020 |
| 3 |
Using Big Data to Establish Mathematical Model Method to Identify the Safety Displacement System of Oil Storage Tank
Wei J, Yang W
Chemistry and Technology of Fuels and Oils, 56(4), 593, 2020 |
| 4 |
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 |
| 5 |
Gaussian Discriminative Analysis aided GAN for imbalanced big data augmentation and fault classification
Zhuo Y, Ge ZQ
Journal of Process Control, 92, 271, 2020 |
| 6 |
증류탑을 위한 머신러닝 기반 플랫폼 개발
오광철, 권혁원, 노지원, 최영렬, 박현도, 조형태, 김정환
Korean Chemical Engineering Research, 58(4), 565, 2020 |
| 7 |
Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data
Malz EC, Verendel V, Gros S
Renewable Energy, 162, 766, 2020 |
| 8 |
A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning
Fan C, Xiao F, Yan CC, Liu CL, Li ZD, Wang JY
Applied Energy, 235, 1551, 2019 |
| 9 |
Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?
Li FGN, Bataille C, Pye S, O'Sullivan A
Applied Energy, 239, 991, 2019 |
| 10 |
A time series clustering approach for Building Automation and Control Systems
Bode G, Schreiber T, Baranski M, Muller D
Applied Energy, 238, 1337, 2019 |
