In the last decade, several process modifications took place in the Loenderveen (LDV)- Weesperkarspel (WPK) Water Treatment Plant (WTP) of Waternet. There are four process modifications initiated over time: first, a shift from garnet sand to calcite pellets as seeding materials for pellet softening process. Second, the elimination of acid dosing in the pretreatment plant at Loenderveen (LVN). Third, a set point adjustment in total hardness level to 1.4 mmol/L from 1.5 mmol/L. Finally, a switch from the acid (HCl) dosing to CO2 dosing for conditioning of softened water for further treatment process. The existing pellet softening models with linear calcium carbonate crystallization kinetic (Rietveld, 2005, van Schagen et al., 2008a) describing calcium and pH profile over the height of the bed and the supersaturated calcium concentration after bypass mixed water, are not capable to cope with these process modifications. Therefore, an improved prediction model for softening process and a set of optimal operational configurations is needed. Recent researches (Chiou, 2018, Seepma, 2018) showed the first improvement path by using a prediction model based on bi-linear kinetics and hydraulics (Hout, 2016, Kramer, 2016). Hence, in this research a model is developed on the basis of new knowledge on kinetics on 4 regions depending on saturation ratio and hydraulics in the reactor and proposed optimal operational configurations. The proposed new prediction model is based on experimental data from Continuous Stirred Batch Reactor (CSTR) and Plug Flow Reactor (PFR). The chemical model is described in 4 regions depending on the level of supersaturation. The kinetic rate constants for calcium carbonate crystallization on seeding material for these 4 regions are taken from CSTR and PFR experiments. The model is calibrated and validated based on previous experimental data from WPK and full-scale treatment plant (Schooten, 1985, Seepma, 2018, Schetters, 2013). Finally, the calibrated and validated results from studied model were compared with model outcomes proposed by Rietveld., 2005. Calcium Carbonate Crystallization Potential (CCCP) is the amount of supersaturated calcium in the effluent and determines the efficiency of the entire pellet softening process. A scenario analysis is performed for summer and winter based on bypass, linear velocity and fluidized bed height, aiming for the lowest CCCP, high reliability, minimum cost and sustainability. CCCP determines the amount of chemicals used and principal cost of pellet softening process. The high reliability of the process comes from the full-scale plant operations over 30 years. The cost minimizations takes into account the chemical cost of NaOH and CO2 (dosing chemical). Ultimately, an optimal operational configuration will lead to a sustainable operational approach for pellet softening by using as little chemicals as possible. The outcomes from the scenario analysis provided an operational window of 15-25% bypass and linear flow velocity of 69-85 m/h for pellet reactors depending on temperature (0-24°C). This choice of optimal configuration comes with a cost reduction between 3-4% both in winter and summer. Previous optimum configurations suggested a bypass of 50% with linear velocity of 60-70 m/h (Rietveld, 2005) and a cost reduction of 10%. This reduction of cost is less pronounced because of process modifications, improved prediction model with 4 regional kinetics and the set of operational windows. Therefore, the process modifications induced a different set of operational criteria for optimum outcome in the pellet softening process at WPK.

For drinking water treatment applications, it is possible to predict the external porosity of an expanded bed of granular activated carbon in fluidized conditions. A new model has been developed with a 2% relative prediction error.

In order to supply sufficient and safe drinking water, water utilities use a treatment train consisting of several unit processes. One of these treatment unit processes is granular activated carbon (GAC) filtration, a crucial unit process widely used for its filtration and adsorption capabilities as a barrier for undesired macro and micro-pollutants. The point of interest for this research is one of the critical steps in the filtration part of the unit process, the backwashing procedure.
Backwashing is a cleaning procedure, which consists on stopping the normal operation of the filter and reversing the normal downward water flow. This upward flow leads to the expansion of the filter’s media and washes away any undesired particles caught in between and on the surface of the media. Inadequate backwashing can lead to unwanted operational outcomes, for instance, solids accumulation and mud balls or media washout, resulting in costly operational expenses. In addition, currently there is a tendency for water utilities to explore new sustainable GAC filter media, which have different expansion tendencies. These operational requirements create the need for the development of prediction models to estimate the expansion degree of the filter bed during backwash procedures. Additionally, a deep understanding of the phenomena that governs this unit process is required to increase its resiliency.

The main goal of this research was to predict the expansion degree of GAC in the water phase. In order to achieve this goal, two innovative approaches combining advanced laboratory techniques and prediction models was the course of action. The first approach was the development of an input model known as the AquaGAC model, to describe the different characteristics of porous media and perform checks using calculated and measured hydraulic parameters. With the combination of the AquaGAC input model and an existing fluidization model (FBI) that computed the expressions of five classical models, the prediction of porosity of the performed experiments was achieved. The second approach consisted on using a data driven model, which consisted on the combination of the outputs the FBI and AquaGAC models with several morphological parameters to derive empirical expressions that accurately estimated the external porosity.

Obtained results suggest that using the 10th percentile of the particle diameter in classical models, delivers porosity prediction errors of 10% in comparison to the 50th percentile used in practice with errors up to 25%. Based on symbolic regression, data driven models produced expressions with accurate correlations with porosity errors ranging from 2-5%.

The results of this research are encouraging as the AquaGAC input model can serve as a basis for other fluidization models that use porous media with different shapes. Recommendations are made to improve the experimental set-up, the accuracy in estimating the particle envelope and wet densities, and the quantitative evaluation of the orientation. Future research of the expansion behavior of mixtures is also recommended.

One of the main targets of Waternet, the water-cycle company of Amsterdam, is to increase the sustainability of water treatment. To achieve this goal, it is necessary to improve the efficiency and to decrease the use of chemicals during pellet softening process. However, the model of Van Schagen et al. (2008 b,c) that is currently used to optimize the pellet softening process is not predicting accurately enough the pH and calcium profile over the height of the pellet softening reactor. The calcium carbonate crystallization is calculated, in this model, using a linear relationship between the rate of crystallization and supersaturation with an additional diffusion parameter to take into account the flow conditions inside the reactor.
To determine more accurately the rate of calcium carbonate during the pellet softening process, two types of experiments were conducted during this research: STR batch and PFR fluidized bed experiments. Firstly, the experimental results were compared with the predictions of two linear models: the model of Wiechers et al. (1975) from literature and the one-rate-constant model developed in this research. Based on the results, it was concluded that it is not possible to improve the prediction of calcium carbonate crystallization kinetics if a linear model with one-rate-constant is used as proposed by Van Schagen. When the rate of crystallization is plotted against supersaturation a bending of the curve is observed at low supersaturation due to a sharp decrease in the rate of crystallization. Other researchers, such as Dreybrodt et al. (1997) has also observed that the rate of calcium carbonate crystallization is not linearly related to supersaturation when water or seeding material with inhibiting compounds is used. To describe this bending of the curve, two models were considered: the exponential model of Lasaga (1998) and the two-rate-constants model that consists of two linear equations. In this research, the two-rate-constants model was chosen instead of the exponential Lasaga model because it is easier to fit to the experimental results and gives a better overview of the dependence of the rate of crystallization from supersaturation. The two-rate-constants model significantly improves the prediction of calcium carbonate crystallization in a pellet softening fluidized bed reactor. The average relative error of this model, for the prediction of the calcium profile in a full-scale reactor, is only 2-5% while the average relative error of the one-rate-constant model is approximately 15-30%. Therefore, the two-rate-constants model predicts better the calcium carbonate crystallization and can be used to describe much more accurately the pellet softening process compared to the models found in literature.
Based on the results of the research, it can be concluded that the performance of a pellet softening fluidized bed reactor cannot be significantly improved by increasing the height of the reactor. On the other hand, it is possible that performance is enhanced by removing inhibitors such as organic carbon from the water. Nevertheless, further research is necessary to determine the effect of inhibitors, such as organic carbon, on water softening. Also, in order to determine more accurately the model parameters, the experimental set up should be adjusted in order to represent better the conditions inside a pellet softening fluidized bed reactor. In particular, increasing the height of the reactor and mixing the caustic soda at the bottom of the column is necessary.