A major shortcoming of polynomial approximation in the medelling of distillation columns isthe difficulty encountered while choosing the number and location of collocation points,which are usually doneby rule of the thumb,inevitably giving rise to high dimensionality and longer computation time for the resultingmodel.In order to take full advantage of polynomial approximation in the modelling of complicatedmulticomponent distillation columns,modifications must be made to the model reduction procedure originallyproposed by Cho.This is achieved by putting in special polynomials to each of the variable profiles.Furthermore,the number and location of the collocation points can be determined by the optimization of anappropriate objective function.This would bring about less dimensionality and less computation time for theresulting reduced--order model as compared with Cho’s procedure while its accuracy is still kept excellent.Theeffectiveness of such modifications is illustrated by two simulation examples.Bot A major shortcoming of polynomial approximation in the medelling of distillation columns is the difficulty encountered while choosing the number and location of collocation points, which are usually doneby rule of the thumb, inevitably giving rise to high dimensionality and longer computation time for the resultingmodel. Order to take full advantage of polynomial approximation in the modeling of complicated multicomponent distillation columns, modifications must be made to the model reduction procedure originallyproposed by Cho. This is achieved by putting in special polynomials to each of the variable profiles. Morerther, the number and location of the collocation points can be determined by the optimization of anappropriate objective function. This would bring about less dimensionality and less computation time for theresulting reduced - order model as compared with Cho’s procedure while its accuracy is still kept excellent. The effect of such modifications is illustrated by two simu lation examples.Bot