(616h) Application of Particle Swarm Optimization In Phase Equilibrium Calculations

Application of Particle Swarm Optimization in Phase
Equilibrium Calculations

Sameer
Punnapala¹, Ali Elkamel^{1, 2}, Hatem
Zeineldin³ and Francisco M Vargas¹

1.     
Department of Chemical Engineering,
Petroleum Institute, Abu Dhabi, UAE

2.     
Department of Chemical Engineering,
University of Waterloo, Ontario, Canada

3.     
Department of Electrical
Engineering, Masdar Institute, Abu Dhabi, UAE

AUTHORS EMAIL ADDRESS: spunnapala@pi.ac.ae,
aelkamel@pi.ac.ae, aelkamel@uwaterloo.ca, hzainaldin@masdar.ac.ae, fvargas@pi.ac.ae

CORRESPONDING
AUTHOR FOOTNOTE: email- fvargas@pi.ac.ae, Phone- 00971-561266149

Phase equilibrium calculations
play a vital role in the design, development, operation, optimization and
control of chemical processes. The performance of Particle Swarm Optimization
(PSO), a novel evolutionary stochastic global optimization method that is
recently gaining importance among the chemical engineering community [1], is
investigated on typical thermodynamic applications. Initially, PSO is tested on
different benchmark problems involving several local minima. This study focuses
on using PSO for parameter estimation in Vapor-Liquid Equilibrium (VLE)
modeling for different systems using both Equations of State and Activity
Coefficient models, accurate prediction of which are of prime importance in
industrial operations. 

The objective functions in
nonlinear parameter estimation problems are mostly non-convex and therefore
have potentially multiple local optima. Conventional solution methods may not
be reliable since they do not guarantee convergence to the global optimum for
the estimated parameters [2]. This leads to a discrepancy in the parameter
value for the same model reported by different authors in literature. Our
results show that PSO is very promising, reliable and offers the best
performance for global minimization problems compared to other evolutionary
techniques like Genetic Algorithms or Simulated Annealing [3].

Keywords: Particle Swarm
Optimization, Phase Equilibrium, Global Optimization, benchmark problems,
Vapor-Liquid Equilibrium, non-convex, Evolutionary techniques.

References

 ADDIN EN.REFLIST 1.         A. Bonilla-Petriciolet,
J.G. Segovia-Hernandez, Fluid Phase Equilib. 289 (2010) 110?121.

2.         C.Y. Gau et al., Fluid Phase Equilib. 168 (2000) 1?18.

3.         A. Bonilla-Petriciolet et al., Fluid Phase
Equilib. 287 (2010) 111?125