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

TL;DR

This paper presents a novel non-destructive, online moisture content estimation method during industrial drying of filter media using artificial neural networks, enabling more efficient drying processes.

Contribution

It introduces an ANN-based approach for real-time moisture estimation during drying, outperforming existing methods with a three-layer perceptron model.

Findings

ANN with oven, drying time, and temperature data reliably estimates moisture content.

The three-layer perceptron achieves the lowest error among tested models.

Method demonstrated on 161 industrial drying experiments.

Abstract

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.