Abstract
This work presents a real-coded genetic algorithm to perform the synthesis and integrated design of an activated sludge process using and advanced Multivariable Model-based Predictive Controller (MPC). The process synthesis and design are carried out simultaneously with the MPC tuning to obtain the most economical plant which satisfies the controllability indices that measure the control performance (H∞ and l1 norms of different sensitivity functions of the system). The mathematical formulation results into a mixed-integer optimization problem with non-linear constraints. The quality of the solutions obtained evidence that real-coded genetic algorithms are a valid and practical alternative to deterministic optimization methods for such complex problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Copp, J.B.: The COST Simulation Benchmark: Description and Simulator Manual. Office for Official Publications of the European Community (2002)
Costa, L., Oliveira, P.: Evolutionary algorithms approach to the solution of mixed integer-non-linear programming problems. Comp. Chem. Eng. 25, 257 (2001)
Elliott, L., Ingham, D., Kyne, A., Mera, N., Porkashanian, M., Whittaker, S.: Reaction mechanism reduction and optimization for modeling aviation fuel oxidation using standard and hybrid genetic algorithms. Comp. Chem. Eng. 30, 889–900 (2006)
Francisco, M., Vega, P.: Diseño Integrado de procesos de depuración de aguas utilizando Control Predictivo Basado en Modelos. Rev. Iberoamericana de Automática e Informática Industrial 3(4), 88–98 (2006)
Gen, M., Chen, R.: Genetic algorithms and engineering optimisation. John Wiley and Sons, Chichester (2000)
Goldberg, D.F.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Kookos, I., Perkins, J.: An algorithm for simultaneous process design and control. Ind. Eng. Chem. Res. 40, 4079 (2001)
Luyben, M., Floudas, C.: Analyzing the interaction of design and control–1. A multiobjective framework and application to binary distillation synthesis. Comp. Chem. Eng. 18, 933 (1994)
Maciejowsky, J.M.: Predictive Control with Constraints. Prentice Hall, Englewood Cliffs (2002)
Moreno, R., De Prada, C., Lafuente, J., Poch, M., Montague, G.: Non-linear predictive control of dissolved oxygen in the activated sludge process. In: IFAC BIO, vol. 2, pp. 289–298. Pergamon Press, Oxford (1992)
Revollar, S., Lamanna, R., Vega, P.: Algorithmic synthesis and integrated design for activated sludge processes using genetic algorithms. ESCAPE, Barcelona (2005)
Sakizlis, S., Perkins, J., Pistikopoulos, E.: Parametric controllers in simultaneous process and control design optimization. Ind. Eng. Chem. Res. 42, 4545–4563 (2003)
Sakizlis, S., Perkins, J., Pistikopoulos, E.: Recent advances in optimization-based simultaneous process and control design. Comp. Chem. Eng. 28, 2069–2086 (2004)
Summanwar, V., Jayaraman, V., Kulkarni, H., Kusumakar, H., Gupta, K., Rajesh, J.: Solution of constrained optimization problems by multiobjective genetic algorithm. Comp. Chem. Eng. 26, 1481–1492 (2002)
Tlacuahuac-Flores, A., Biegler, L.: Integrated Control and Process Design During Optimal Polymer Grade Transitions Operations. Comp. Chem. Eng (in press, 2008)
Tsai, M., Chang, C.: Water usage and treatment network design using genetic algorithms. Ind. Eng. Chem. Res. 40, 4874 (2001)
Vega, P., Francisco, M., Sanz, E.: Norm based approach for automatic tuning of Model Predictive Controllers. In: Proceedings of ECCE-6, Copenhague (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Revollar, S., Francisco, M., Vega, P., Lamanna, R. (2009). Genetic Algorithms for the Synthesis and Integrated Design of Processes Using Advanced Control Strategies. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-85863-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85862-1
Online ISBN: 978-3-540-85863-8
eBook Packages: EngineeringEngineering (R0)