Electrical impedance spectroscopy and machine learning for impurity detection from wood chips

In circular economy, the use of wood chips from recycled and waste wood with varying composition has increased. Thus, the challenges with impurities have increased and it would be advantageous to be able to detect, identify and quantify the impurities. The goal of the study was to find out if wood chips with impurities of aluminum, recycled plastic or paper can be analysed using electrical impedance spectroscopy (EIS) and machine learning (ML). An EIS-measurement prototype using frequency range 1–500 kHz was developed for biomass samples. Five electrodes were used to measure the samples in two main directions. Multiple and Gaussian process regression (MR, GPR) were used for regression analyses and k-nearest neighbor (KNN), decision tree (DT), and support vector machines (SVM) for classification. In the study, frozen, unfrozen and partly unfrozen samples were used. Moisture content (MC) variation was from oven-dry to 68% (wet-basis). Recycled plastic and shredded aluminium could be classified with 95% correct classification rate (CCR), for shredded paper the CCR was 75%. The amounts of impurities (volumetric content of shredded aluminium) could be determined with 2% accuracy (RMSE) using EIS, volume weight and GPR. An interesting finding was that the classification was more accurate for frozen samples than for the unfrozen samples.