Kummer et al., 2020 - Google Patents
NMPC-based control scheme for a semi-batch reactor under parameter uncertaintyKummer et al., 2020
View HTML- Document ID
- 8588757971902124131
- Author
- Kummer A
- Nagy L
- Varga T
- Publication year
- Publication venue
- Computers & Chemical Engineering
External Links
Snippet
Exothermic reactions are often performed in SBR because the generated reaction heat can be more easily kept under control in such construction. However, an unsuitable control system can lead to the development of thermal runaway, which may cause lethal damage …
- 238000006243 chemical reaction 0 abstract description 75
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
- G05B13/048—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
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
-
- 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
-
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- 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
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yoo et al. | Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation | |
Bhadriraju et al. | Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS) | |
Wang | Robust data-driven modeling approach for real-time final product quality prediction in batch process operation | |
Kummer et al. | NMPC-based control scheme for a semi-batch reactor under parameter uncertainty | |
Kadam et al. | Dynamic optimization in the presence of uncertainty: From off-line nominal solution to measurement-based implementation | |
Benkouider et al. | A hybrid approach to faults detection and diagnosis in batch and semi-batch reactors by using EKF and neural network classifier | |
Kummer et al. | Semi-batch reactor control with NMPC avoiding thermal runaway | |
Francois et al. | Measurement-based real-time optimization of chemical processes | |
Hosen et al. | Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation | |
Kanavalau et al. | Robust thermal stability for batch process intensification with model predictive control | |
Machalek et al. | A novel implicit hybrid machine learning model and its application for reinforcement learning | |
Rossi et al. | Online model-based optimization and control for the combined optimal operation and runaway prediction and prevention in (fed-) batch systems | |
Sass et al. | Multi-agent reinforcement learning-based exploration of optimal operation strategies of semi-batch reactors | |
Kubosawa et al. | Computing operation procedures for chemical plants using whole-plant simulation models | |
Loeblein et al. | Economic performance analysis in the design of on-line batch optimization systems | |
Joy et al. | Model-based control of continuous emulsion co-polymerization in a lab-scale tubular reactor | |
Kähm et al. | Stability criterion for the intensification of batch processes with model predictive control | |
Luo et al. | Model predictive control of nonlinear processes using neural ordinary differential equation models | |
Wang et al. | Globally optimal nonlinear model predictive control based on multi-parametric disaggregation | |
Lu et al. | Data‐driven optimal control of operational indices for a class of industrial processes | |
Himmel et al. | Machine learning for process control of (bio) chemical processes | |
Özkan et al. | Non-linear generalised predictive control of a jacketed well mixed tank as applied to a batch process—A polymerisation reaction | |
Soares et al. | An optimal control-based safety system for cost efficient risk management of chemical processes | |
Wang et al. | DRSM model for the optimization and control of batch processes | |
Maria et al. | Derivation of critical and optimal operating conditions for a semibatch reactor under parametric uncertainty based on failure probability indices |