Cantó et al., 2012 - Google Patents
Dynamic optimization of a gas-liquid reactorCantó et al., 2012
View PDF- Document ID
- 14251193742361630659
- Author
- Cantó B
- Cardona S
- Coll C
- Navarro-Laboulais J
- Sánchez E
- Publication year
- Publication venue
- Journal of Mathematical Chemistry
External Links
Snippet
A dynamic gas-liquid transfer model without chemical reaction based on unsteady film theory is considered. In this case, the mathematical model presented for gas-liquid mass- transfer processes is based on mass balances of the transferred substance in both phases …
- 239000007788 liquid 0 title abstract description 27
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
-
- 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 |
---|---|---|
Zhu et al. | Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints | |
Ellis et al. | A tutorial review of economic model predictive control methods | |
Sanchez-Sanchez et al. | Simultaneous design and control under uncertainty using model predictive control | |
Zeng et al. | Economic model predictive control of wastewater treatment processes | |
Von Stosch et al. | Hybrid semi-parametric modeling in process systems engineering: Past, present and future | |
Malcolm et al. | Integrating systems design and control using dynamic flexibility analysis | |
Maußner et al. | Optimization under uncertainty in chemical engineering: Comparative evaluation of unscented transformation methods and cubature rules | |
Biegler | Advances in nonlinear programming concepts for process control | |
Francois et al. | Measurement-based real-time optimization of chemical processes | |
Tenny et al. | Closed‐loop behavior of nonlinear model predictive control | |
Ostrovsky et al. | Optimization problem with normally distributed uncertain parameters | |
Haugwitz et al. | Modeling and control of a novel heat exchange reactor, the open plate reactor | |
Palma-Flores et al. | Integration of design and NMPC-based control for chemical processes under uncertainty: An MPCC-based framework | |
Kravaris et al. | Understanding process dynamics and control | |
Blanco et al. | Interaction between process design and process operability of chemical processes: an eigenvalue optimization approach | |
Cantó et al. | Dynamic optimization of a gas-liquid reactor | |
Mate et al. | Multiparametric Nonlinear MPC: A region free approach | |
Zenger et al. | Modelling and control of a class of time-varying continuous flow processes | |
Tyagounov | High-performance model predictive control for process industry | |
Marchetti et al. | Self-optimizing control structures with minimum number of process-dependent controlled variables | |
Chen et al. | Model predictive control of nonlinear singularly perturbed systems: Application to a reactor-separator process network | |
Haugwitz et al. | Dynamic start-up optimization of a plate reactor with uncertainties | |
Huang et al. | Analysis of the Relation between Coupled Sink and Purification Based on Hydrogen Network Integration | |
Ellis et al. | On closed-loop economic performance under Lyapunov-based economic model predictive control | |
Yang et al. | Advanced-multi-step nonlinear model predictive control |