Advanced Control of a Fluidized Bed Using a Model-predictive Controller

The control of fluidized-bed processes remains an area of intensive research due to their complexity and the inherent nonlinearity and varying operational dynamics involved. There are a variety of problems in chemical engineering that can be formulated as Nonlinear Programming (NLP) problems. The quality of the solution developed significantly affects the performance of such a system. Controller design involves tuning of the process controllers and their implementation to achieve a specified performance of the controlled variables. Here we used a Sequential Quadratic Programming (SQP) method to tackle the constrained high-NLP problem, in this case a modified mathematical model of gas-phase olefin polymerisation in a fluidized-bed catalytic reactor. The objective of this work was to present a comparative study; PID control was compared to an advanced neural network-based MPC decentralised controller, and the effect of SQP on the performance of the controlled variables was studied. The two control approaches were evaluated for set-point tracking and load rejection properties, both giving acceptable results.

[1]  Sirish L. Shah,et al.  Constrained nonlinear MPC using hammerstein and wiener models: PLS framework , 1998 .

[2]  Hector A. Rosales-Macedo.  Nonlinear Programming: Theory and Algorithms (2nd Edition) , 1993 .

[3]  Francis J. Doyle,et al.  Hybrid model-based approach to batch-to-batch control of particle size distribution in emulsion polymerization , 2003, Comput. Chem. Eng..

[4]  Evanghelos Zafiriou,et al.  A methodology for on-line setpoint modification for batch reactor control in the presence of modeling error , 1999 .

[5]  M. Hussain,et al.  Simulation, optimization and parametric studies of a solid catalyzed gas phase ethylene polymerization fluidized bed reactor , 2005 .

[6]  Lorenz T. Biegler,et al.  Improved infeasible path optimization for sequential modular simulators. , 1983 .

[7]  Yuan Tian,et al.  Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models , 2002 .

[8]  Ahmet Palazoglu,et al.  Control of nonlinear processes using functional expansion models , 2003, Comput. Chem. Eng..

[9]  Wolfgang Marquardt,et al.  Productivity optimization of an industrial semi-batch polymerization reactor under safety constraints , 2000 .

[10]  J. Romagnoli,et al.  On-line multi-variable predictive control of molar mass and particle size distributions in free-radical emulsion copolymerization , 2005 .

[11]  Yaman Arkun,et al.  Quasi-Min-Max MPC algorithms for LPV systems , 2000, Autom..

[12]  Philip E. Gill,et al.  Practical optimization , 1981 .

[13]  Francis J. Doyle,et al.  A dynamic neural network approach to nonlinear process modeling , 1997 .

[14]  Thomas Reslow Kruth.  Interior-Point Algorithms for Quadratic Programming , 2008 .

[15]  Mohd Azlan Hussain,et al.  Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors , 2008 .

[16]  Rui Vilela Mendes,et al.  Characteristic Functions and Process Identification by Neural Networks , 1997, Neural Networks.

[17]  Francis J. Doyle,et al.  Modeling of particle size distribution in emulsion co-polymerization: comparison with experimental data and parametric sensitivity studies , 2002 .

[18]  Babatunde A. Ogunnaike,et al.  An alternative structure for next generation regulatory controllers: Part I: Basic theory for design, development and implementation , 2006 .

[19]  Yaman Arkun,et al.  Estimation and control of an α-olefin polymerization reactor , 1995 .

[20]  D. Ang,et al.  Regularization of an inverse two-phase Stefan problem , 1998 .

[21]  Stephen J. Wright,et al.  Application of Interior-Point Methods to Model Predictive Control , 1998 .

[22]  R. Sala,et al.  A fluid-dynamic study of a continuous polymerisation reactor , 1974 .

[23]  Zhihua Xiong,et al.  Modelling and optimal control of fed-batch processes using a novel control affine feedforward neural network , 2004, Neurocomputing.

[24]  Mohd Azlan Hussain,et al.  Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network , 2002 .

[25]  Domitilla Del Vecchio,et al.  Boundary control for an industrial under-actuated tubular chemical reactor , 2005 .

[26]  Costas Kiparissides,et al.  Recent developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors: The effect of bubble size variation on the reactor's performance , 2000 .

[27]  John F. MacGregor,et al.  Latent variable MPC for trajectory tracking in batch processes , 2005 .

[28]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[29]  M. Mourad,et al.  Séchage de maïs en lit fluidisé à flottation II: modélisation de la cinétique de séchage , 1995 .

[30]  A. Lohi,et al.  Optimal control determination of MMA polymerization in non-isothermal batch reactor using bifunctional initiator , 2005 .

[31]  R. L. Tousain,et al.  Efficient dynamic optimization for nonlinear model predictive control-application to a high-density poly-ethylene grade change problem , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[32]  Morimasa Ogawa,et al.  Quality inferential control of an industrial high density polyethylene process , 1999 .

[33]  Joachim Horn,et al.  Trajectory tracking of a batch polymerization reactor based on input–output-linearization of a neural process model , 2001 .

[34]  Ravindra D. Gudi,et al.  Identification of complex nonlinear processes based on fuzzy decomposition of the steady state space , 2003 .

[35]  Wang Yang,et al.  Industrial application of a nonlinear model predictive control to polymerization reactors , 2001 .

[36]  Neal R. Amundson,et al.  Continuous polymerization models—part II: Batch reactor polymerization , 1965 .

[37]  L. Biegler,et al.  A robust and efficient mixed-integer non-linear dynamic optimization approach for simultaneous design and control , 2005 .

[38]  Z. Zeybek,et al.  Adaptive heuristic temperature control of a batch polymerisation reactor , 2004 .

[39]  Victor M. Zavala,et al.  Dynamic optimization of a semi-batch reactor for polyurethane production , 2005 .

[40]  Ernest L Baker,et al.  Modeling and Optimization of Shaped Charge Liner Collapse and Jet Formation , 1993 .

[41]  F. Doyle,et al.  An anti-windup scheme for multivariable nonlinear systems , 1997 .

[42]  Jinyoung Kim,et al.  Neural network modeling of temperature behavior in an exothermic polymerization process , 2002, Neurocomputing.

[43]  O. Bosgra,et al.  Stochastic closed-loop model predictive control of continuous nonlinear chemical processes , 2006 .

[44]  A. Helal,et al.  Numerical construction of bifurcation diagrams for non-linear complex reaction networks in catalytic reactors , 1990 .

[45]  Hung-Wen Chiou,et al.  On the Effect of Constraint Softening on the Stability and Performance of Model Predictive Controllers , 1992 .

[46]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[47]  Efstratios N. Pistikopoulos,et al.  Optimal grade transition and selection of closed-loop controllers in a gas-phase olefin polymerization fluidized bed reactor , 2003 .

[48]  Terrence L. Fine,et al.  Feedforward Neural Network Methodology , 1999, Information Science and Statistics.

[49]  E. L. Lima,et al.  Control strategies for complex chemical processes. Applications in polymerization processes , 2003, Comput. Chem. Eng..

[50]  G Shahaf,et al.  Learning in Networks of Cortical Neurons , 2001, The Journal of Neuroscience.

[51]  W. Ray,et al.  THE DYNAMIC BEHAVIOUR OF FLUIDIZED BED REACTORS FOR SOLID CATALYZED GAS PHASE OLEFIN POLYMERIZATION , 1985 .

[52]  Bruce D. Baker,et al.  Exploratory Application of Systems Dynamics Modeling to School Finance Policy Analysis. , 2002 .

[53]  Arun S. Mujumdar,et al.  USE OF NEURAL NETWORK TO PREDICT INDUSTRIAL DRYER PERFORMANCE , 1993 .