Packed towers have been widely used in industrial productions. It is important to accurately predict the flooding velocity of packed towers. In industrial practice

there are many kinds of packings which can show different characteristics. Only using a single global model is still difficult to achieve satisfied prediction results. To overcome the problem

a new local modeling method is proposed to predict the flooding velocity. First

a recursive algorithm of ridge extreme learning machine with nodes growing is formulated

which can update the online model in an efficient manner. Moreover

using the just-in-time learning manner

the local recursive ridge parameter extreme learning machine (LRRELM)-based online modeling method is proposed. The experimental results show that the LRRELM model can explore more related information among data and thus to obtain better and more reliable prediction performance

compared with the related global models.