Frame (networking)

Simulated annealing

Frame work

Mathematics

Genetic algorithm

Algorithm

Mathematical optimization

Machine learning

Engineering

Architectural engineering

Computer science

Differential evolution

Telecommunications

Selection (genetic algorithm)

Particle swarm optimization

Chemical Engineering & Technology

Genetic algorithms (GA) have been very seldom applied in the optimization of chemical reactors with distributed parameters, described by models containing differential equations. GA are applied here in the frame of four case studies. In all four cases, the GA are superior in comparison to other methods, such as maximum principle (up to 20.72 %), simulated annealing (up to 0.11 %), and particle swarm optimization (up to 36.74 %). Possibly, the solutions obtained by GA can be improved by a better selection of the values of the method parameters, but this was not the aim of the present work. The use of optimal control can produce an important economic improvement, especially by increasing the selectivity, which significantly enhances the use of raw materials.

Optimal Control for Chemical Reactors with Distributed Parameters Using Genetic Algorithms