Flocculation, as the formation of insoluble organo-metallic complexes, has been studied considerably in the field of soil science, as it emerges e.g. from podzolization where a soil layer with reduced permeability is created; but also in the field of water treatment, as a means to remove natural organic matter (NOM). It is based on the process where positively charged metal species – Aluminium (Al), Iron (Fe) and Zirconium (Zr) in this study – react with the negatively charged surface of dissolved organic matter (DOM) and precipitate to form flocs. Since pH affects the surface charge of the organic matter (OM) particles and the form in which the coagulants are present in solution, controlling physico-chemical parameters, such as the pH, would significantly improve the flocculation process and help to enhance the efficiency of water purification strategies and soil permeability reduction from a geo-engineering perspective. It was shown that (1) there are ideal intervals in terms of metal to Carbon ratio (Me/C), in which the coagulants should be added to cover the surface of the DOM particles and these vary with metal type and pH; (2) at pH = 1, the OM forms flocs only with protons when mixed with Al, while with Fe some, and with Zr all, metal is associated with the solid phase; (3) Zr generally forms larger flocs than Al and Fe, even at acidic pH = 1, but also at higher concentrations; and (4) as the Me/C increases, the floc size does as well.@en

Drinking water softening has primarily prioritized public health, environmental benefits, social costs and enhanced client comfort. Annually, over 35 billion cubic meters of water is softened worldwide, often utilizing three main techniques: nanofiltration, ion exchange and seeded crystallization by pellet softening. However, recent modifications in pellet softening, including changes in seeding materials and acid conditioning used post-softening, have not fully achieved desired flexibility and optimization. This highlights the need of an integral approach, as drinking water softening is just one step in the drinking water treatment chain, which includes ozonation, softening, biological active carbon filtration (BACF) and sand filtration among others. In addition, pellet softening is often practiced based on operator knowledge, lacking practical key reactor performance indicators (KPIs) for efficient control. For that reason, we propose a newly and improved integral mechanistic model designed to accurately predict (1) calcite removal rates in drinking water through seeded crystallization in pellet softening reactors, (2) the saturation of the filter bed in the subsequent treatment step, (3) values for the KPIs steering the softening efficiency. Our new mechanistic model integrates insights from hydrodynamics, thermodynamics, mass transfer kinetics, nucleation and reactor engineering, focussing on critical variables such as temperature, linear velocity, pellet particle size and saturation index with respect to calcite. Our model was validated with data from the Waternet Weesperkarspel drinking water treatment plant in Amsterdam, The Netherlands, but implies universal applicability for addressing industrial challenges beyond drinking water softening. The implementation of our model proposes five effective KPIs to optimize the softening process, chemical usage, and reactor design. The advantage of this model is that it eliminates the application of numerical methods and fills a significant gap in the field by providing predictions of the carry-over (i.e., the produced CaCO₃ fines leaving the fluidized bed) from water softening practices. With our model, the calcium removal rate is predicted with an average standard deviation (SD) of 40 % and the consequential clogging prediction of the BACF bed with an average SD of 130 %. Ultimately, our model provides crucial insights for operational management and decision-making in drinking water treatment plants, steering towards a more circular and environmentally sustainable process.

@en