Abstract
A neural net was trained to simulate a single-zone tenter frame dryer over a wide range of operating conditions. The dryer fabric inputs and first zone air temperature were used to compute the first zone outputs of a multi-zone dryer. These outputs (fabric temperature and moisture content) along with the second zone air temperature were then input to the same single-zone model to predict the second zone outputs. This process was repealed until the final dryer outputs were obtained. This recursive modeling scheme provided an excellent simulation of a six-zone dryer over a range of fabric inlet conditions, fabric velocity, and zone air temperatures. This recursive modeling approach should be applicable to other distributed parameter systems, and also to dynamic systems simulation.
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