Optimization of a True Moving Bed unit and determination of its feasible operating region using a novel Sliding Particle Swarm Optimization
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||14|
|Journal||COMPUTERS AND INDUSTRIAL ENGINEERING|
|Publication status||Published - 1 Sep 2019|
|Publication type||A1 Journal article-refereed|
The optimization of Simulated Moving Bed units has become an important topic to be developed in this field. The present work proposes a novel methodology to optimize a True Moving Bed unit and draw the feasible operating region through a novel improved Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients and Mutable Searching Region, here called Sliding Particle Swarm Optimization, concomitantly with an adapted method to define the feasible operating region. Two main contributions can be highlighted, first the process optimization with its feasible operational regions, leading to better results when compared with the numerical optimization based on the equilibrium theory, second a new particle swarm method is presented which can deal more efficiently with multi local minima problems. Finally, the concept of feasible operating region concept is presented as a support tool for the process operation. Three different operational scenarios were simulated here in order to verify the consistence and efficiency of the methodology. The main advantage of the methodology here proposed is the possibility to tracking all the possible operating regime of the unit while meeting a given process requirement.