Modelling a Transformer Oil Regeneration Process Using Genetic Programming

ABSTRACT

Genetic programming and neural network techniques were both used to predict the product distribution and yield of product oil from a reactor in a transformer oil regeneration process. All reactor models were developed by fitting laboratory-scale data. For the (relatively) small experimental data set available, it was found that the accuracy of the reactor model was significantly better when using genetic programming than neural network modelling techniques. A flowsheet of a pilot-scale version of the process was developed (using commercial simulation packages) based on the reactor model obtained using genetic programming, and the optimal operating conditions determined so as to give the maximum yield of regenerated transformer oil.