State-Space Predictive-P Control for Liquid Level in an Industrial Coke Fractionation Tower

In this study, a predictive-p control system is developed for the level process in an industrial coke fractionation tower. Such processes typically have integrating and nonlinear dynamics causing the performance of conventional control designs and tuning to be poor or to require significant effort in practice. The process model is derived using data of step-response test and control implementation is designed through a new state-space structure. The closed-loop control system contains both the improved predictive control and P control. The performance of the proposed control for regulatory/servo, disturbance rejection, and measurement noise problems are studied and the obtained results show that the control system is of simple implementation with more robustness and provides better responses than conventional predictive control.

[1]  Xiaobing Kong,et al.  Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation , 2014, IEEE Transactions on Automation Science and Engineering.

[2]  Shuqing Wang,et al.  Support vector machine based predictive functional control design for output temperature of coking furnace , 2008 .

[3]  Dexian Huang,et al.  Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace , 2008 .

[4]  G. Unni Krishnan What HAZOP studies cannot do , 2005 .

[5]  Vasileios Exadaktylos,et al.  Multi-objective performance optimisation for model predictive control by goal attainment , 2010, Int. J. Control.

[6]  Brian Glennon,et al.  Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller , 2014 .

[7]  Tyler Ryan,et al.  LMI-Based Gain Synthesis for Simple Robust Quadrotor Control , 2013, IEEE Transactions on Automation Science and Engineering.

[8]  Debasish Dutta,et al.  Feedback Matching Framework for Semantic Interoperability of Product Data , 2012, IEEE Transactions on Automation Science and Engineering.

[9]  Furong Gao,et al.  Temperature Modeling in a Coke Furnace with an Improved RNA-GA Based RBF Network , 2014 .

[10]  Furong Gao,et al.  Predictive control optimization based PID control for temperature in an industrial surfactant reactor , 2014 .

[11]  B. Bequette,et al.  Process Control: Modeling, Design and Simulation , 2003 .

[12]  Chen-Fu Chien,et al.  Overlay Error Compensation Using Advanced Process Control With Dynamically Adjusted Proportional-Integral R2R Controller , 2014, IEEE Transactions on Automation Science and Engineering.

[13]  Antonio Ferramosca,et al.  Steady-state target optimization designs for integrating real-time optimization and model predictive control , 2014 .

[14]  P. Young,et al.  An improved structure for model predictive control using non-minimal state space realisation , 2006 .

[15]  Li Wang,et al.  Cross-Domain Model Building and Validation (CDMV): A New Modeling Strategy to Reinforce Understanding of Nanomanufacturing Processes , 2013, IEEE Transactions on Automation Science and Engineering.

[16]  Chuanhou Gao,et al.  Constructing Multiple Kernel Learning Framework for Blast Furnace Automation , 2012, IEEE Transactions on Automation Science and Engineering.

[17]  Furong Gao,et al.  Multivariable decoupling predictive functional control with non-zero-pole cancellation and state weighting: Application on chamber pressure in a coke furnace , 2013 .

[18]  A. Armaou,et al.  Piece-wise constant predictive feedback control of nonlinear systems , 2014 .

[19]  黄德先,et al.  Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace , 2008 .

[20]  K. Xiao Nonlinear Model Predictive Control for DFIG-based Wind Power Generation , 2013 .

[21]  José Luis Guzmán,et al.  Implementation of feedback linearization GPC control for a solar furnace , 2013 .

[22]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[23]  Furong Gao,et al.  Design of dynamic matrix control based PID for residual oil outlet temperature in a coke furnace , 2014 .

[24]  A. Xue,et al.  Dynamic Modeling and Nonlinear Predictive Control Based on Partitioned Model and Nonlinear Optimization , 2011 .

[25]  Friedman Yz Why coker APC applications are tough , 2005 .

[26]  Bradley J. Nelson,et al.  Robust Electromagnetic Control of Microrobots Under Force and Localization Uncertainties , 2014, IEEE Transactions on Automation Science and Engineering.

[27]  Aniruddha B. Pandit,et al.  Petroleum residue upgrading via delayed coking: A review , 2007 .