Drinking water softening is an essential treatment step that provides multiple benefits, including public health, reduction of environmental impact, decrease in clogging potential and improvement in heating efficiency. With approximately 35 billion cubic meters of water being softened annually worldwide, the predominant methods are conventional lime/soda-ash softening, nanofiltration, ion exchange, and seeded crystallization through pellet-water softening. This study addresses the limitations in existing predictive models for calcium carbonate (CaCO₃) precipitation kinetics in industrial-scale pellet-water softening by experimentally investigating the integral and multivariate effects of particle-, fluid-, water matrix- and reactor properties, on CaCO₃ precipitation kinetics. Fluid characterization experiments were conducted at lab-scale continuous-stirred tank reactors (CSTR), pilot-scale plug-flow reactors (PFR), and full-scale fluidized bed reactors (FBR) at the Waternet Weesperkarspel treatment plant in Amsterdam, The Netherlands. In parallel, solid characterization was performed with image analysis software on pellets and SEM on fines extracted from water samples, where both pellet and water samples were collected during FBR experiments. The calcium removal data obtained from experiments were compared with modeled CaCO₃ precipitation rates using and extending the most recently developed water softening model for pellet-water softening. The results predominantly highlight the critical role of mixing dynamics — between softening chemicals, hard influent water and seeding material — for accurate CaCO₃ precipitation predictions across various reactor types and other reactor-specific properties such as the residence time of influent hard water. Additional enhancements can be achieved by targeting fluid properties, followed by water matrix properties, and finally particle properties, though these factors exhibit a progressively smaller impact on overall water softening improvement. By implementing these prioritized optimization strategies, the operational control strategy for calcium removal will be enhanced, leading to improvements in cost-effectiveness, sustainability, and reliability in drinking water treatment processes.

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.