Just-in-time local modeling for flooding velocity prediction in packed towers

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.

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