论文部分内容阅读
Gene expression programming (GEP) is an evolutionary algorithm that combines the characteristics of genetic algorithm and
genetic programming by incorporating genotype and phenotype representations in its chromosome. Although many methods exist for modeling and optimization, none can be considered universal, hence there is a never ending search for algorithms and
methods that can be applied in chemical process modeling and optimization.
The main aim of this thesis is to explore the possibility of applying GEP to chemical process modeling and optimization. Having been invented in the recent past, there is a need to investigate its applicability to various fields. This thesis presents practical examples of steady-state modeling of styrene-butadiene rubber (SBR) formulation, concrete mix formulation and an ethylbenzene dehydrogenation reactor using GEP. It also presents results obtained from the global optimization of benchmark problems (both NLP and MINLP). In both cases the results obtained show that GEP can also be applied to chemical
process modeling and optimization.