ACES  Vol.6 No.1 , January 2016
Augmented Lagrangian Genetic Algorithm Based Decentralized Control Configuration Design for Fluid Catalytic Cracking Units
Abstract: In this work, three decentralized control configuration designs—independent, sequential and simultaneous designs—were used in multivariable feedback configurations for PI control of the riser and regenerator temperatures of FCCU in order to compare their performances. Control design was formulated as optimization problem to minimize infinity norm of weighted sensitivity functions subject to μ-interaction measure bound on diagonal complementary functions of the closed loop system. The optimization problem was solved using augmented Lagrangian genetic algorithm. Simulation results show that simultaneous and independent designs give good response with less overshoot and with no oscillation. Bound on μ-interaction measure is satisfied for both designs meaning that their nominal stabilities are guaranteed; however, it is marginal for simultaneous design. Simultaneous design outperforms independent design in term of robust performance while independent design gives the best performance in terms of robust stability. Sequential design gives the worst performance out of the three designs.
Cite this paper: Araromi, D. , Salam, K. and Sulayman, A. (2016) Augmented Lagrangian Genetic Algorithm Based Decentralized Control Configuration Design for Fluid Catalytic Cracking Units. Advances in Chemical Engineering and Science, 6, 1-19. doi: 10.4236/aces.2016.61001.

[1]   Jose, A.R., Jesus, V. and Hector, P. (2004) Multivariable Control Configurations for Composition Regulation in a Fluid Catalytic Cracking Unit. Chemical Engineering Journal, 99, 187-120.

[2]   Jose, R.H., Richart, V.R. and Daniel, S.S. (2006) Multiplicity of Steady States in FCC Units: Effect of Operating Conditions. Fuel, 85, 849-859.

[3]   Pandimadevi, V., Indumathi, G. and Selvakumar, P. (2010) Design of Controllers for a Fluidized Catalytic Cracking Process. Chemical Engineering Research and Design, 8, 875-880.

[4]   Iancu, M. and Agachi, P.S. (2010) Optimal Process Control and Operation of an Industrial Heat Integrated Fluid Catalytic Cracking Plant Using Model Predictive Control. In: Ferraris, S.P., Ed., 20th European Symposium on Computer Aided Process Engineering, Elsevier B.V.

[5]   Raji, O.Y., El-Nafaty, U.A., Jibril, M. and Danjuma, B.M. (2012) Modelling and Optimization of Fluid Catalytic Cracking Unit (FCCU) Using Hysys. International Journal of Emerging Trends in Engineering and Development, 3, 1-9.

[6]   Ansari, R.M. and Tade, M. (2000) Constrained Nonlinear Multivariable Control of a Fluid Catalytic Cracking Process. Journal of Process Control, 10, 539-555.

[7]   Raluca, R., Zoltán, K.N., Frank, A. and Serban, P.A. (2005) Dynamic Modeling and Nonlinear Model Predictive Control of a Fluid Catalytic Cracking Unit. In: Espuña, Ed., L.P., European Symposium on Computer-Aided Process Engineering-15, Elsevier Science B.V.

[8]   Ahmed, D.F. (2011) Decoupling Control of Fluid Catalytic Cracking Unit. Journal of Chemistry and Chemical Engineering, 5, 12-19.

[9]   Garcia, D., Karimi, A. and Longchamp, R. (2005) PID Controller Design for Multivariable Systems Using Gershgorin Bands. IFAC.

[10]   Rosinová, D., Thuan, N.Q. and Vesely, V.M. (2012) Robust Decentralized Controller Design: Subsystem Approach. Journal of Electrical Engineering, 63, 28-34.

[11]   Skogested, S. and Postlethwaite, I. (2005) Multivariable Feedback Control Analysis and Design. John Wiley Sons, Chichester, New York, Brisbane, Toronto and Singapore.

[12]   Campo, P.J. and Morari, M. (1994) Achievable Closed-Loop Properties of Systems under Decentralized Control: Conditions Involving the Steady-State Gain. IEEE Transactions on Automatic Control, 39, 932-943.

[13]   Skogestad, S. and Morari, M. (1989) Robust Performance of Decentralized Control Systems by Independent Designs. Automatica, 25, 119-125.

[14]   Hovd, M. and Skogestad, S. (1993) Procedure for Regulatory Control Structure Selection with Application to the FCC Process. AIChE Journal, 39, 1938-1953.

[15]   Alsabei, R.M. (2011) Model Based Approach for the Plant-Wide Economic Control of Fluid Catalytic Cracking Unit. PhD Thesis, Loughborough University, Loughborough.

[16]   McFarlane, R.C., Reineman, R.C., Bartee, J.F. and Georgakis, C. (1990) Dynamic Simulator for a Model IV Fluid Catalytic Cracking Unit. Proceedings of the American Institute of Chemical Engineers Annual Meeting, Chicago, 14-16 November 1990.

[17]   Coleman, B. and Babu, J. (2002) Techniques of Model Based Control. Prentice Hall, Upper Saddle River.

[18]   Zhou, K. (1999) Essentials of Robust Control. Pretence Hall, Upper Saddle River.

[19]   Morari, M. (1983) Design of Resilient Processing Plants: A General Framework for the Assessment of Dynamic Resilience. Chemical Engineering Science, 38, 1881-1891.

[20]   Deb, K. and Srivastava, S. (2013) A Genetic Algorithm Based Augmented Lagrangian Method for Constrained Optimization. Computational Optimization and Applications, 53, 869-902.

[21]   Lewis, R. and Torczon, V. (2002) A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds. SIAM Journal on Optimization, 12, 1075-1089.

[22]   Costa, L., Santo, A.E. and Fernandes, E.M. (2012) A Hybrid Genetic Pattern Search Augmented Lagrangian Method for Constrained Global Optimization. Applied Mathematics and Computation, 218, 9415-9426.

[23]   Goldberg, D. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.

[24]   Melanie, M. (1989) An Introduction to Genetic Algorithms. MIT Press, Cambridge.