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Moisture content (MC) estimation is important in the manufacturing process ofdrying bulky filter media products as it is the prerequisite for dryingoptimization. In this study, a dataset collected by performing 161 dryingindustrial experiments is described and a methodology for MC estimation in annon-destructive and online manner during industrial drying is presented. Anartificial neural network (ANN) based method is compared to state-of-the-art MCestimation methods reported in the literature. Results of model fitting andtraining show that a three-layer Perceptron achieves the lowest error.Experimental results show that ANNs combined with oven settings data, dryingtime and product temperature can be used to reliably estimate the MC of bulkyfilter media products.

Online Non-Destructive Moisture Content Estimation of Filter Media During Drying Using Artificial Neural Networks