Effect of vapor-liquid equilibrium data on the design of separation sequences by distillation

Dynamic Matrix Control Algorithm is a powerful control method widely applied to indus- trial processes. The idea of this work is to use the Genetic Algorithms (GA) with the elitism strategy to optimize the tuning parameters of the Dynamic Ma- trix Controller for SISO (single-input single-output) and MIMO (multi-input multi-output) processes with constraints. A comparison is made between the computational method proposed here with the tun- ing guidelines described in the literature, showing advantages of the GA method.

[1]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  M. Streich,et al.  Property inaccuracies influence low temperature designs , 1979 .

[4]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[5]  Arthur W. Westerberg,et al.  Synthesis of Distillation-Based Separation Systems , 1996 .

[6]  Enrique A. Campanella,et al.  Azeotropic distillation: effect of the thermodynamic model , 1999 .

[7]  Dale E. Seborg,et al.  Predictive controller design for single-input/single-output (SISO) systems , 1988 .

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Megan Jobson,et al.  Recycle Selection for Homogeneous Azeotropic Distillation Sequences , 2005 .

[10]  Megan Jobson,et al.  Multicomponent homogeneous azeotropic distillation. 3. Column sequence synthesis. , 2001 .

[11]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[12]  J. Rawlings,et al.  The stability of constrained receding horizon control , 1993, IEEE Trans. Autom. Control..

[13]  Megan Jobson,et al.  Multicomponent homogeneous azeotropic distillation. 2. Column design , 2001 .

[14]  Jay H. Lee,et al.  Tuning of model predictive controllers for robust performance , 1994 .

[15]  J. S. Rowlinson,et al.  Molecular Thermodynamics of Fluid-Phase Equilibria , 1969 .

[16]  Vincent Wertz,et al.  Using genetic algorithms to optimize the design parameters of generalized predictive controllers , 2001 .

[17]  James R. Matey,et al.  How Statistical Design Concepts Can Improve Experimentation in the Physical Sciences , 1993 .

[18]  Wallace B. Whiting,et al.  Techniques for assessing the effects of uncertainties in thermodynamic models and data , 1999 .

[19]  Kazuo Kojima,et al.  Isobaric vapor-liquid equilibria for acetone + chloroform + benzene and the three constituent binary systems , 1991 .

[20]  Robert Schaefer,et al.  Foundations of Global Genetic Optimization , 2007, Studies in Computational Intelligence.

[21]  Stanley I. Sandler,et al.  Sensitivity of distillation process design and operation to VLE data , 1983 .

[22]  Franz Rothlauf,et al.  On the importance of the second largest eigenvalue on the convergence rate of genetic algorithms , 2001 .

[23]  Carlos E. García,et al.  Fundamental Process Control , 1988 .

[24]  D. Dougherty,et al.  Tuning Guidelines of a Dynamic Matrix Controller for Integrating (Non-Self-Regulating) Processes , 2003 .

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

[26]  Wolfgang Marquardt,et al.  Rapid screening of design alternatives for nonideal multiproduct distillation processes , 2004, Comput. Chem. Eng..