1
|
|
2
|
Model predictive control of an intensified continuous reactor using a neural network Wiener model. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.12.048] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
3
|
Shi L. Integration of Optimization and Model Predictive Control of an Intensified Continuous Three-Phase Catalytic Reactor. INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2015. [DOI: 10.1515/ijcre-2014-0101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Intensified continuous three-phase catalytic reactors working in high-pressure and -temperature conditions are particularly effective at coping with mass transfer limitations during three-phase catalytic reactions. They are highly nonlinear, multivariable systems and behave differently from conventional batch, fed-batch or continuous non-intensified reactors. This paper deals with an integration of real-time optimization and model predictive control (RTO–MPC) of an intensified continuous three-phase catalytic reactor. A steady-state model developed by regression method is used in optimization layer and gives the reference trajectory for control layer. At control layer, a linear MPC is proposed based on identified state space model. The performance of RTO–MPC is illustrated by simulation
Collapse
|