Optimization of ideal reactive distillation column using hybrid PSO algorithm

Optimization has developed into a prime field of engineering. Its applications can be widely seen in industrial as well as research sectors. Particle Swarm Optimization (PSO) is one of the various tools used in research and industry to maximize efficiency out of process or industry by optimization. PSO has gained much credit due to its simplicity and faster convergence compared to other evolutionary algorithms. However there are certain limitations that need to be overcome like premature convergence and decreased acceleration when reaching the optimal solution. In this thesis, Hybrid-Integer Particle Swarm Optimization Algorithm is developed which can optimize mixed Integer problems with a very fast pace and sure convergence.;It is noticed that the optimization leads to a process design with lesser fuel consumption and same product purity. The optimization algorithm is also tested for standard test problems and the results are compared to those cited.;An example of an industrial process is Reactive Distillation Column (RDC). It has modernized the petrochemical industry in the area of separation of products by the methods of distillation. Distillation requires higher temperature to be maintained in RDC, the burners that supply the necessary heat to maintain the temperature require constant supply of fuel. RDC exhibits non-linearity due to chemical reactions. The design of the RDC is a factor that determines the amount of fuel consumed. Thus optimization at design level will lead to optimal fuel consumption of process. The developed Hybrid-Integer PSO in this research is applied to find an optimal design for minimum fuel consumption and attain the same purity of separated products.