Data-Driven Modelling of the Complex Interaction between Flocculant Properties and Floc Size and Structure

Abstract

Polymeric flocculants are widely used due to their ability to e ciently promote flocculation at low dosages. However, fundamental background knowledge about how they act and interact with the substrates is often scarce, or insu cient to infer the best chemical configuration for treating a specific e uent. Inductive, data-driven approaches o er a viable solution, enabling the development of e ective solutions for each type of e uent, overcoming the knowledge gap. In this work, we present such an inductive workflow that combines the statistical design of experiments and predictive modelling, and demonstrates its e ectiveness in the development of anionic polymeric flocculants for the treatment of a real e uent from the potato crisps manufacturing industry. Based on the results presented, it is possible to conclude that the hydrodynamic diameter, charged fraction and concentration are the parameters with a stronger influence on the characteristics of flocs obtained when using copolymers, while the charged fraction, concentration and hydrophobic content present a stronger influence on the characteristics of flocs obtained using terpolymers containing a hydrophobic monomer.