Determining the Moisture Content of Wood Chips in Inline Industry Applications Using UWB Radio Transmission Signals and Machine Learning

Abstract

Determining moisture content (MC) in wood chips finds its application in many industries, including energy production. In this letter, we aim to develop an automated method for determining MC in woodchips using ultrawideband (UWB) radio signals and machine learning algorithms. First, to acquire UWB signals through wood chips on conveyor belts in industrial plants, we use measurement devices with a radio transmitter and receiver, and a laser sensor to determine the thickness of the wood chips. UWB and laser data corresponding to 1923 samples from four power plants is acquired. Second, we extract the amplitude and delay-based features, and these are finally fed to three different machine learning algorithms, namely, linear regression, artificial neural network (ANN), and ensemble trees to determine the MC. The proposed method achieves best results when the ANN is used. More specifically, our method achieves a mean absolute error (MAE) of 2.75% when the features from both UWB and laser sensors are used for determining MC. The MAE of 3.95% is achieved when features only from UWB data (without the laser) are used for determining MC. Our results for industrial data suggest that the proposed method is effective for determining MC in industrial applications.