The present study reports mathematical analysis of the experimental data based on the paper by M. Konczak et al. (https://doi.org/10.1016/j.chemosphere.2021.130447) where two groups of the five biochars (two – residues of sewage sludge or SS, three – residues of biogas production or RBP) were compared by structure and sorption ability to decontaminate water from fulvic acids. As a reference, a non-waste silvergrass (Miscanthus sp or MI) biochar was involved. From the experimental results, it was concluded that MI structurally is close to RBP and by sorption properties it is as effective, as SS. The present research has been developed after the publication and can serve as a theoretical continuation, where mathematical approaches from Data Science, such as 2-Means Clustering, Principal Components and Logistic Regression serve to prove the assumption about the mentioned above duality of MI. The experimental data were separated by structural and adsorption criteria, whereupon 2 clusters were established by structure (1: RBP + MI and 2: SS) or by adsorption (1: SS + MI and 2: RBP). Each clustering has been supported by respective Principal Components after removing the variables with the lowest R2 and highly-collinear interaction. The resulting Principal Components have served, in turn, as variables to build Logistic Regression models to predict and distinguish both clusters: after the iterations, a single Principal Component is enough to describe the clusters as by structural, as by adsorption criterion (Fig.1). The computation results can be helpful to develop machine learning algorithms to classify the biochars by their structural-sorption properties. Fig.1. The Logistic Regression plots to classify the clusters by adsorption (A) or by structural (B) criteria of the biochars.