Mechanistic model advancements for optimal calcium removal in water treatment: Integral operation improvements and reactor design strategies.

Autor: Seepma SYMH; Utrecht University, Department of Earth Sciences, Princetonlaan 8A, 3584, CB Utrecht, the Netherlands; Waternet, PO Box 94370, 1090, GJ, the Netherlands. Electronic address: s.y.m.h.seepma@uu.nl., Koskamp JA; Utrecht University, Department of Earth Sciences, Princetonlaan 8A, 3584, CB Utrecht, the Netherlands., Colin MG; Waternet, PO Box 94370, 1090, GJ, the Netherlands., Chiou E; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, PO Box 5048, 2600, GA, Delft, the Netherlands; Waternet, PO Box 94370, 1090, GJ, the Netherlands., Sobhan R; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, PO Box 5048, 2600, GA, Delft, the Netherlands; Waternet, PO Box 94370, 1090, GJ, the Netherlands., Bögels TFJ; Utrecht University, Department of Earth Sciences, Princetonlaan 8A, 3584, CB Utrecht, the Netherlands; Waternet, PO Box 94370, 1090, GJ, the Netherlands., Bastiaan T; Utrecht University, Department of Earth Sciences, Princetonlaan 8A, 3584, CB Utrecht, the Netherlands; Waternet, PO Box 94370, 1090, GJ, the Netherlands., Zamanian H; Waternet, PO Box 94370, 1090, GJ, the Netherlands., Baars ET; Waternet, PO Box 94370, 1090, GJ, the Netherlands., de Moel PJ; Waternet, PO Box 94370, 1090, GJ, the Netherlands; Omnisys, Eiberlaan 23, 3871, TG Hoevelaken, the Netherlands., Wolthers M; Utrecht University, Department of Earth Sciences, Princetonlaan 8A, 3584, CB Utrecht, the Netherlands. Electronic address: m.wolthers@uu.nl., Kramer OJI; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, PO Box 5048, 2600, GA, Delft, the Netherlands; Waternet, PO Box 94370, 1090, GJ, the Netherlands; Queen Mary University of London, School of Engineering and Materials Science, Division of Chemical Engineering, Centre for Sustainable Engineering, Mile End Road E1 4NS, London, United Kingdom. Electronic address: onno.kramer@waternet.nl.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2024 Nov 10; Vol. 268 (Pt B), pp. 122781. Date of Electronic Publication: 2024 Nov 10.
DOI: 10.1016/j.watres.2024.122781
Abstrakt: 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 CaCO 3 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: