Kähm et al., 2018 - Google Patents
Lyapunov exponents with Model Predictive Control for exothermic batch reactorsKähm et al., 2018
View PDF- Document ID
- 4687761265405699221
- Author
- Kähm W
- Vassiliadis V
- Publication year
- Publication venue
- IFAC-PapersOnLine
External Links
Snippet
Thermal runaways cause significant safety issues and financial loss for industrial batch reactors due to the disruption of normal operation. The intensification of processes is restricted, since control systems are not capable of detecting stability boundaries of the …
- 238000000034 method 0 abstract description 52
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103761450A (en) | Dynamic process fault forecasting method based on fuzzy self-adaptive prediction | |
Kähm et al. | Lyapunov exponents with Model Predictive Control for exothermic batch reactors | |
Kähm et al. | Stability criterion for the intensification of batch processes with model predictive control | |
Kanavalau et al. | Robust thermal stability for batch process intensification with model predictive control | |
Rashid et al. | Handling multi‐rate and missing data in variable duration economic model predictive control of batch processes | |
Heng et al. | Energy-oriented modeling and optimization of a heat treating furnace | |
Ahmadi et al. | Multimodel control of nonlinear systems: An improved gap metric and stability margin-based method | |
Sidikov et al. | Fuzzy synergetic control nonlinear dynamic objects | |
Kähm et al. | Optimal Laypunov exponent parameters for stability analysis of batch reactors with Model Predictive Control | |
Kummer et al. | NMPC-based control scheme for a semi-batch reactor under parameter uncertainty | |
Vladov et al. | Control signals of a predictive industrial PID controller | |
Li et al. | Event-based production control for energy efficiency improvement in sustainable multistage manufacturing systems | |
Kähm et al. | Thermal stability criterion of complex reactions for batch processes | |
Anderson et al. | Distributed economic model predictive control of a catalytic reactor: Evaluation of sequential and iterative architectures | |
Rohman et al. | Implementation of model predictive control in tracking dynamic optimal profiles of semi batch autocatalytic esterification reactor | |
Zhang et al. | Multi-Objective Optimization for Gas Distribution in Continuous Annealing Process | |
Guojun et al. | A real-time updated model predictive control strategy for batch processes based on state estimation | |
Kahm | Thermal stability criteria embedded in avanced control systems for batch process intensification | |
Clerget et al. | Dynamic optimization of a system with input-dependant time delays | |
Kumar et al. | Fuzzy Logic Control of Temperature in Nitration Process | |
Yu et al. | Predictive control based on neural networks of the chemical process | |
Son et al. | Model-plant mismatch learning offset-free model predictive control | |
Hutabarat et al. | Detection and quantification of valve stiction based on normality test and Hammerstein system identification | |
Alanqar et al. | Handling plant variation via error-triggered on-line model identification: Application to economic model predictive control | |
Gowthami et al. | Fault detection and diagnosis in continuous stirred tank reactor (CSTR) |