  The use of microwave tomography (MWT) in an industrial drying process is
demonstrated in this feasibility study with synthetic measurement data. The
studied imaging modality is applied to estimate the moisture content
distribution in a polymer foam during the microwave drying process. Such
moisture information is crucial in developing control strategies for
controlling the microwave power for selective heating. In practice, a
reconstruction time less than one second is desired for the input response to
the controller. Thus, to solve the estimation problem related to MWT, a neural
network based approach is applied to fulfill the requirement for a real-time
reconstruction. In this work, a database containing different moisture content
distribution scenarios and corresponding electromagnetic wave responses are
build and used to train the machine learning algorithm. The performance of the
trained network is tested with two additional datasets.
