Online Non-Destructive Moisture Content Estimation of Filter Media During Drying Using Artificial Neural Networks
This work addresses moisture content estimation for filter media manufacturing, enabling non-destructive online monitoring to optimize drying processes, but it is incremental as it applies existing ANN techniques to a specific industrial context.
The study tackled the problem of estimating moisture content during industrial drying of filter media by developing an artificial neural network method, which achieved the lowest error compared to existing methods using a dataset from 161 drying experiments.
Moisture content (MC) estimation is important in the manufacturing process of drying bulky filter media products as it is the prerequisite for drying optimization. In this study, a dataset collected by performing 161 drying industrial experiments is described and a methodology for MC estimation in an non-destructive and online manner during industrial drying is presented. An artificial neural network (ANN) based method is compared to state-of-the-art MC estimation methods reported in the literature. Results of model fitting and training show that a three-layer Perceptron achieves the lowest error. Experimental results show that ANNs combined with oven settings data, drying time and product temperature can be used to reliably estimate the MC of bulky filter media products.