Biegler, 2007 - Google Patents
Efficient nonlinear programming algorithms for chemical process control and operationsBiegler, 2007
View PDF- Document ID
- 3954074021571220577
- Author
- Biegler L
- Publication year
- Publication venue
- IFIP Conference on System Modeling and Optimization
External Links
Snippet
Optimization is applied in numerous areas of chemical engineering including the development of process models from experimental data, design of process flowsheets and equipment, planning and scheduling of chemical process operations, and the analysis of …
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
-
- 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
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Biegler et al. | Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization | |
Seki et al. | Industrial application of a nonlinear model predictive control to polymerization reactors | |
Zavala et al. | Interior-point decomposition approaches for parallel solution of large-scale nonlinear parameter estimation problems | |
Biegler et al. | Advances in sensitivity-based nonlinear model predictive control and dynamic real-time optimization | |
Du et al. | A time scale-bridging approach for integrating production scheduling and process control | |
Zavala et al. | Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control | |
Chen et al. | Model predictive control of nonlinear singularly perturbed systems: Application to a large-scale process network | |
Zavala et al. | Fast implementations and rigorous models: Can both be accommodated in NMPC? | |
Biegler | Advanced optimization strategies for integrated dynamic process operations | |
Hong et al. | A quasi‐sequential approach to large‐scale dynamic optimization problems | |
Jamaludin et al. | Approximation of closed-loop prediction for dynamic real-time optimization calculations | |
Cervantes et al. | Large-scale dynamic optimization for grade transitions in a low density polyethylene plant | |
Biegler | Integrated optimization strategies for dynamic process operations | |
Biegler | New directions for nonlinear process optimization | |
BenAmor et al. | Polymer grade transition control using advanced real-time optimization software | |
Biegler | Nonlinear programming strategies for dynamic chemical process optimization | |
Biegler | A survey on sensitivity-based nonlinear model predictive control | |
Kang et al. | Fast and reliable computational strategy for developing a rigorous model-driven soft sensor of dynamic molecular weight distribution | |
Behrooz | Robust synthesis of the pressure-swing distillation process under azeotropic feed composition disturbance—study of the tetrahydrofuran/methanol system | |
Marusak | A numerically efficient fuzzy MPC algorithm with fast generation of the control signal | |
Ye et al. | Global self-optimizing control with active-set changes: A polynomial chaos approach | |
Zavala et al. | A fast computational framework for large-scale moving horizon estimation | |
Biegler | Efficient nonlinear programming algorithms for chemical process control and operations | |
Silva et al. | Optimization and nonlinear model predictive control of batch polymerization systems | |
Zavala et al. | Fast solvers and rigorous models: Can both be accomodated in nmpc |