Lactic acid (LA) is a commercial fine chemical

used in food and medicine

which is readily available by biomass fermentation.As a chemical feedstock

LA is widely used due to the renewable biomass resources.In this paper

BP neural network was applied to simulating the dehydration reaction of lactic acid.The optimal reaction conditions were determined through orthogonal design that could be used to investigate the interrelated effects of pH

feed liquid flow rate

carrier gas flow rate and temperature on the yield of acrylic acid(AA).With the training samples from experimental data of orthogonal design

a new model that could simulate the process of the dehydrated reaction was established based on BP neural network theory.Then the network system trained was used to anticipate the effects of different ingredients and their interactions on AA yield.The three-dimensional graphs produced by the network could effectively express the relationships between reaction conditions and catalytic activity.Finally

with the help of the network

the highest AA yield of 27.45% was obtained

which was very close to the experimental result with the relative error of -0.4391%.