Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons.

An interpretative strategy (factorial design experimentation+total resolution analysis+chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables.