| 1 |
Big Data, AI, and the Pleasures of Engineering
Kuhn M
Chemie Ingenieur Technik, 93(3), 364, 2021 |
| 2 |
A review of in-line and on-line measurement techniques to monitor industrial mixing processes
Bowler AL, Bakalis S, Watson NJ
Chemical Engineering Research & Design, 153, 463, 2020 |
| 3 |
Disassembly 4.0: A Review on Using Robotics in Disassembly Tasks as a Way of Automation
Poschmann H, Bruggemann H, Goldmann D
Chemie Ingenieur Technik, 92(4), 341, 2020 |
| 4 |
A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation
Destro F, Facco P, Munoz SG, Bezzo F, Barolo M
Journal of Process Control, 92, 333, 2020 |
| 5 |
Neural network prediction of parameters of biomass ashes, reused within the circular economy frame
Sakiewicz P, Piotrowski K, Kalisz S
Renewable Energy, 162, 743, 2020 |
| 6 |
Product Design and Engineering in Chemical Engineering: Past, Present State, and Future
Uhlemann J, Costa R, Charpentier JC
Chemical Engineering & Technology, 42(11), 2258, 2019 |
| 7 |
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 |
| 8 |
Wasserwirtschaft 4.0 im Chemiepark Bitterfeld-WolfenWater Management 4.0 in the Bitterfeld-Wolfen Chemical Park
Gahr A, Wazinski P, Andreas N
Chemie Ingenieur Technik, 91(10), 1375, 2019 |
| 9 |
Advances and opportunities in machine learning for process data analytics
Qin SJ, Chiang LH
Computers & Chemical Engineering, 126, 465, 2019 |
| 10 |
Locating experts and carving out the state of the art: A systematic review on Industry 4.0 and energy system analysis
Nolting L, Kies A, Schonegge M, Robinius M, Praktiknjo A
International Journal of Energy Research, 43(9), 3981, 2019 |
