| 31 |
Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography
Rizkin BA, Popovich K, Hartman RL
Computers & Chemical Engineering, 121, 584, 2019 |
| 32 |
Forecasting of process disturbances using k-nearest neighbours, with an application in process control
Borghesan F, Chioua M, Thornhill NF
Computers & Chemical Engineering, 128, 188, 2019 |
| 33 |
Process control practice and education: Past, present and future
Bequette BW
Computers & Chemical Engineering, 128, 538, 2019 |
| 34 |
The one-third rule for PI controller tuning
Hagglund T
Computers & Chemical Engineering, 127, 25, 2019 |
| 35 |
Reinforcement Learning - Overview of recent progress and implications for process control
Shin J, Badgwell TA, Liu KH, Lee JH
Computers & Chemical Engineering, 127, 282, 2019 |
| 36 |
Process systems Engineering: Academic and industrial perspectives
Grossmann IE, Harjunkoski I
Computers & Chemical Engineering, 126, 474, 2019 |
| 37 |
Operational safety of chemical processes via Safeness-Index based MPC: Two large-scale case studies
Zhang ZH, Wu Z, Rincon D, Garcia C, Christofides PD
Computers & Chemical Engineering, 125, 204, 2019 |
| 38 |
A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing
Su QL, Ganesh S, Moreno M, Bommireddy Y, Gonzalez M, Reklaitis GV, Nagy ZK
Computers & Chemical Engineering, 125, 216, 2019 |
| 39 |
Production scheduling and linear MPC: Complete integration via complementarity conditions
Simkoff JM, Baldea M
Computers & Chemical Engineering, 125, 287, 2019 |
| 40 |
Operability study on small-scale BOG (boil-off gas) re-liquefaction processes
Son H, Kim JK
Energy, 185, 1263, 2019 |
