scholarly journals Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Zhen Yan ◽  
Jie-Sheng Wang ◽  
Shao-Yan Wang ◽  
Shou-Jiang Li ◽  
Dan Wang ◽  
...  
Keyword(s):  
System Identification ◽  
State Space ◽  
Predictive Control ◽  
Chromatographic Separation ◽  
Control Method ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Separation Technology ◽  
Subspace System Identification

Simulated moving bed (SMB) chromatographic separation is a new type of separation technology based on traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology, which includes inlet-outlet liquid, liquid circulation, and feed liquid separation. The input-output data matrices were constructed based on SMB chromatographic separation process data. The SMB chromatographic separation process was modeled by utilizing two subspace system identification algorithms: multivariable output-error state-space (MOESP) identification algorithm and numerical algorithms for subspace state-space system identification (N4SID), so as to obtain the 3rd-order and 4th-order state-space yield models of the SMB chromatographic separation process, respectively. The model predictive control method based on the established state-space models is used in the SMB chromatographic separation process. The influence of different control indicators on the predictive control system response performance is discussed. The output response curves of the yield models were obtained by changing the related parameters so that the yield model parameters are optimized set meanwhile. Finally, the simulation results showed that the yield models are successfully controlled based on the each control period and given yield range.

Dynamic Optimization of a Simulated Moving Bed (SMB) Chromatographic Separation Process

2006 ◽  
Vol 45 (26) ◽  
pp. 9033-9041 ◽  
Author(s):  
María-Sonia G. García ◽  
Eva Balsa-Canto ◽  
Julio R. Banga ◽  
Alain Vande Wouwer
Keyword(s):  
Dynamic Optimization ◽  
Chromatographic Separation ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed

Digitized Simulation of the Moving Bed

2020 ◽  
Vol 987 ◽  
pp. 157-161
Author(s):  
Chao Fan Xie ◽  
Rey Chue Huang ◽  
Lin Xu ◽  
Fu Quan Zhang ◽  
Lu Xiong Xu
Keyword(s):  
Stationary Phase ◽  
Chromatographic Separation ◽  
Manufacturing Process ◽  
Theoretical Basis ◽  
Mobile Phase ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Simulated Moving Bed Chromatography ◽  
Chemical Products

Chromatographic separation is an indispensable and important technology in the manufacturing process of chemical products and biomedicine. It uses the distribution differences of a compound in the stationary phase and mobile phase to achieve the separation of the mixture. It is of great value to study the separation process of substances by simulated moving bed chromatography. By digitally simulating the process of moving bed, we can observe the influence of parameter changes on substance analysis by chromatography, and then find out the law of substance separation, which can provide theoretical basis for scientific research of biopharmaceuticals.

OPTIMAL CONTROL of the SIMULATED MOVING BED (SMB) CHROMATOGRAPHIC SEPARATION PROCESS

2007 ◽  
Vol 40 (5) ◽  
pp. 183-188 ◽  
Author(s):  
María-Sonia G. García ◽  
Eva Balsa-Canto ◽  
Alain Vande Wouwer ◽  
Julio R. Banga
Keyword(s):  
Optimal Control ◽  
Chromatographic Separation ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed

Discrete and Analytic Model Predictive Control for Path Following of Underactuated Ships

2009 ◽  
Author(s):  
Xiaofei Wang ◽  
Zaojian Zou ◽  
Tieshan Li ◽  
Weilin Luo
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Feedback Linearization ◽  
Control Method ◽  
State Space Model ◽  
Path Following ◽  
Analytic Model ◽  
Space Model ◽  
Path Following Control

The control problem of underactuated surface ships and underwater vehicles has attracted more and more attentions during the last years. Path following control aims at forcing the vehicles to converge and follow a desired path. Path following control of underactuated surface ships or underwater vehicles is an important issue to study nonlinear systems control, and it is also important in the practical implementation such as the guidance and control of marine vehicles. This paper proposes two nonlinear model predictive control algorithms to force an underactuated ship to follow a predefined path. One algorithm is based on state space model, the other is based on analytic model predictive control. In the first algorithm, the state space GPC (Generalized Predictive Control) method is used to design the path-following controller of underactuated ships. The nonlinear path following system of underactuated ships is discretized and re-arranged into state space model. Then states are augmented to get the new state space model with control increment as input. Thus the problem is becoming a typical state space GPC problem. Some characters of GPC such as cost function, receding optimization, prediction horizon and control horizon occur in the design procedure of path-following controller. The control law is derived in the form of control increment. In the second algorithm, an analytic model predictive control algorithm is used to study the path following problem of underactuated ships. In this path-following algorithm, the output-redefinition combined heading angle and cross-track error is introduced. As a result, the original single-input multiple-output (SIMO) system is transformed into an equivalent single-input single-output (SISO) system. For the transformed system, we use the analytic model predictive control method to get path-following control law in the analytical form. The analytic model predictive controller can be regarded as special feedback linearization method optimized by predictive control method. It provides a systematic method to compute control parameters rather than by try-and-error method which is often used in the exact feedback linearization control. Relative to GPC, the analytic model predictive control method provides an analytic optimal solution and decreases the computational burden, and the stability of closed-loop system is guaranteed. The path-following system of underactuated ships is guaranteed to follow and stabilize onto the desired path. Numerical simulations demonstrate the validity of the proposed control laws.

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