| 1 |
Rebooting data-driven soft-sensors in process industries: A review of kernel methods
Liu YQ, Xie M
Journal of Process Control, 89, 58, 2020 |
| 2 |
A hierarchical approach for causal modeling of process systems
Suresh R, Sivaram A, Venkatasubramanian V
Computers & Chemical Engineering, 123, 170, 2019 |
| 3 |
A hybrid model for human factor analysis in process accidents: FBN-HFACS
Zarei E, Yazdi M, Abbassi R, Khan F
Journal of Loss Prevention in The Process Industries, 57, 142, 2019 |
| 4 |
A bibliometric review of process safety and risk analysis
Amin MT, Khan F, Amyotte P
Process Safety and Environmental Protection, 126, 366, 2019 |
| 5 |
A Novel Fuzzy Bayesian Network-HFACS (FBN-HFACS) model for analyzing Human and Organization Factors (HOFs) in process accidents
Rostamabadi A, Jahangiri M, Zarei E, Kamalinia M, Banaee S, Samaei MR
Process Safety and Environmental Protection, 132, 59, 2019 |
| 6 |
Investigating written procedures in process safety: Qualitative data analysis of interviews from high risk facilities
Sasangohar F, Peres SC, Williams JP, Smith A, Mannan MS
Process Safety and Environmental Protection, 113, 30, 2018 |
| 7 |
Domino effect analysis of dust explosions using Bayesian networks
Yuan Z, Khalezad N, Khan F, Amyotte P
Process Safety and Environmental Protection, 100, 108, 2016 |
| 8 |
A new method for optimization of Solar Heat Integration and solar fraction targeting in low temperature process industries
Baniassadi A, Momen M, Amidpour M
Energy, 90, 1674, 2015 |
| 9 |
Preparing for major terrorist attacks against chemical clusters: Intelligently planning protection measures w.r.t. domino effects
Reniers GLL, Audenaert A
Process Safety and Environmental Protection, 92(6), 583, 2014 |
| 10 |
Major accident management in the process industry: An expert tool called CESMA for intelligent allocation of prevention investments
Reniers G, Brijs T
Process Safety and Environmental Protection, 92(6), 779, 2014 |
