Model predictive control for improving waste heat recovery in coke dry quenching processes

CDQ (coke dry quenching) is a widely used method for recovering waste heat in the steel industry. We have developed a novel, data driven modeling approach and model based control for a CDQ unit to increase steam generation in a cogeneration system. First, the correlation between steam generation and TCGB (the temperature of circulation gas entering the associated boiler) was confirmed. Subsequently, a nonlinear variable selection method was employed to build models of TCGB and the carbon monoxide concentration of the circulation gas. The models obtained were implemented to achieve MPC (model predictive control) for regulating the supplementary gas to maximize steam generation in an existing steelmaking plant. Upon comparison of the original process and the proposed modified operation, the effectiveness of the implementation of MPC was justified. The results showed that steam generation was increased by 7%. In our approach, the large amount of available operational data stored electronically was used to establish the models. Modification of the established system is not required. Taking into account that no capital investment is required, the process improvement is remarkable in terms of its return on investment.

[1]  Vijander Singh,et al.  Development of soft sensor for neural network based control of distillation column. , 2013, ISA transactions.

[2]  R. R. Hocking.  The analysis and selection of variables in linear regression , 1976 .

[3]  Frank Allgöwer,et al.  An Introduction to Nonlinear Model Predictive Control , 2002 .

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  Thomas F. Coleman,et al.  An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds , 1993, SIAM J. Optim..

[6]  G. Bisio,et al.  Energy saving and some environment improvements in coke-oven plants , 2000 .

[7]  John Bagterp Jørgensen,et al.  Model predictive control technologies for efficient and flexible power consumption in refrigeration systems , 2012 .

[8]  Jinliang Xu,et al.  Modeling and constrained multivariable predictive control for ORC (Organic Rankine Cycle) based waste heat energy conversion systems , 2014 .

[9]  Josep M. Sopena,et al.  Performing Feature Selection With Multilayer Perceptrons , 2008, IEEE Transactions on Neural Networks.

[10]  Octavian Pastravanu,et al.  A neural predictive controller for non-linear systems , 2002, Math. Comput. Simul..

[11]  Marko Bacic,et al.  Model predictive control , 2003 .

[12]  Mohd Azlan Hussain,et al.  Neural network based model predictive control for a steel pickling process , 2009 .

[13]  Jialin Liu,et al.  Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote , 2014 .

[14]  Xin-xin Zhang,et al.  Experimental and numerical investigations of coke descending behavior in a coke dry quenching cooling shaft , 2008 .

[15]  Cheng Zhai,et al.  PROCESS SIMULATION OF AN INTEGRATED COKE DRY QUENCHING COMBINED GASIFICATION , 2012 .

[16]  Mohd Ali Hashim,et al.  Thermal stress management of a solid oxide fuel cell using neural network predictive control , 2013 .

[17]  Qin,et al.  A mathematical model for optimized operation and control in a CDQ-Boiler system , 2005 .

[18]  M. Errera,et al.  Thermodynamic analysis of a coke dry quenching unit , 2000 .

[19]  Rui Araújo,et al.  A multilayer-perceptron based method for variable selection in soft sensor design , 2013 .

[20]  L. Breiman.  Better subset regression using the nonnegative garrote , 1995 .

[21]  Thomas F. Coleman,et al.  On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds , 1994, Math. Program..

[22]  Pei-Hsun Lin,et al.  Exergy Analysis of a Coke Dry Quenching System , 2009 .

[23]  Martin T. Hagan,et al.  Neural network design , 1995 .

[24]  Xin-xin Zhang,et al.  Mathematical model for fluid flow and heat transfer in the cooling shaft of Coke Dry Quenching unit , 2002 .