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H.J.M. Kramer
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An improved kinetic model and optimized configurations for pellet softening
Modeling and optimization of pellet softening process in drinking water treatment
Master thesis
(2019)
-
Rubayat Sobhan, Jan Peter van der Hoek, Herman Kramer, Peter de Moel, Eric T. Baars, Onno Kramer
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.
...
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.
An improved model of calcium carbonate crystallization
An improved kinetics-model for the calcium carbonate crystallization in the fluidized bed softening reactors at the Weesperkarspel drinking water treatment plant
Master thesis
(2018)
-
Eleftheria Chiou, Jan Peter van der Hoek, Herman Kramer, Onno Kramer, Peter de Moel, E.T. Baars
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.
...
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.
...
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.
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.
Nucleation is the initial step for the creation of new crystalline phase. A precise control over nucleation and its kinetics is important for both research and industries. Thus, alternative methods are sought after to extend the toolbox for controlling nucleation. In the 1990's, Non-Photochemical Laser Induced Nucleation (NPLIN) was suggested as a promising method to alter the nucleation kinetics. Since then, several reports have demonstrated that NPLIN dramatically reduces the nucleation induction time and controls polymorphism of various fine chemicals relevant for industrial practice. Although different hypotheses have been proposed in literature to explain the experimental observations, the mechanism behind NPLIN is still unknown.
The objective of this work is to extend the mechanistic understanding of NPLIN. This has been approached by qualitatively studying the effect of different factors on the nucleation efficiency of the non-photochemical process using unfocused pulsed laser in aqueous supersaturated solution of KCl. The factors investigated include wavelength, peak intensity, supersaturation, mixing, and impurity level of the solution. Each of these parameters are studied using high number of samples (80-100) to generate a robust set of results and to avoid the stochastic nature of nucleation.
In a separate series of experiments, an acoustic wave was detected in the solution due to the non-linear interaction of the unfocused laser with the system by measuring the pressure signal with a piezo-electric transducer placed just below the air-liquid interface. Further experiments were executed to understand the nature of the acoustic wave and its influence on NPLIN. The results show that laser could induce nucleation at significantly low peak intensities, much below the previously reported intensity threshold in literature. It is also observed that NPLIN shows a strong dependence on peak intensity, supersaturation, impurity level, and mixing of the solution while the dependence on wavelength was found to be weak. Furthermore, the acoustic wave experiments show that the laser induced pressure fluctuations do not affect the nucleation efficiency of the process. Overall, the results suggest that several mechanisms play a role during laser induced nucleation. To summarize, the research provides a robust analysis of different factors that can influence NPLIN. The results can be further utilized to enhance the understanding and applicability of the process. ...
The objective of this work is to extend the mechanistic understanding of NPLIN. This has been approached by qualitatively studying the effect of different factors on the nucleation efficiency of the non-photochemical process using unfocused pulsed laser in aqueous supersaturated solution of KCl. The factors investigated include wavelength, peak intensity, supersaturation, mixing, and impurity level of the solution. Each of these parameters are studied using high number of samples (80-100) to generate a robust set of results and to avoid the stochastic nature of nucleation.
In a separate series of experiments, an acoustic wave was detected in the solution due to the non-linear interaction of the unfocused laser with the system by measuring the pressure signal with a piezo-electric transducer placed just below the air-liquid interface. Further experiments were executed to understand the nature of the acoustic wave and its influence on NPLIN. The results show that laser could induce nucleation at significantly low peak intensities, much below the previously reported intensity threshold in literature. It is also observed that NPLIN shows a strong dependence on peak intensity, supersaturation, impurity level, and mixing of the solution while the dependence on wavelength was found to be weak. Furthermore, the acoustic wave experiments show that the laser induced pressure fluctuations do not affect the nucleation efficiency of the process. Overall, the results suggest that several mechanisms play a role during laser induced nucleation. To summarize, the research provides a robust analysis of different factors that can influence NPLIN. The results can be further utilized to enhance the understanding and applicability of the process. ...
Nucleation is the initial step for the creation of new crystalline phase. A precise control over nucleation and its kinetics is important for both research and industries. Thus, alternative methods are sought after to extend the toolbox for controlling nucleation. In the 1990's, Non-Photochemical Laser Induced Nucleation (NPLIN) was suggested as a promising method to alter the nucleation kinetics. Since then, several reports have demonstrated that NPLIN dramatically reduces the nucleation induction time and controls polymorphism of various fine chemicals relevant for industrial practice. Although different hypotheses have been proposed in literature to explain the experimental observations, the mechanism behind NPLIN is still unknown.
The objective of this work is to extend the mechanistic understanding of NPLIN. This has been approached by qualitatively studying the effect of different factors on the nucleation efficiency of the non-photochemical process using unfocused pulsed laser in aqueous supersaturated solution of KCl. The factors investigated include wavelength, peak intensity, supersaturation, mixing, and impurity level of the solution. Each of these parameters are studied using high number of samples (80-100) to generate a robust set of results and to avoid the stochastic nature of nucleation.
In a separate series of experiments, an acoustic wave was detected in the solution due to the non-linear interaction of the unfocused laser with the system by measuring the pressure signal with a piezo-electric transducer placed just below the air-liquid interface. Further experiments were executed to understand the nature of the acoustic wave and its influence on NPLIN. The results show that laser could induce nucleation at significantly low peak intensities, much below the previously reported intensity threshold in literature. It is also observed that NPLIN shows a strong dependence on peak intensity, supersaturation, impurity level, and mixing of the solution while the dependence on wavelength was found to be weak. Furthermore, the acoustic wave experiments show that the laser induced pressure fluctuations do not affect the nucleation efficiency of the process. Overall, the results suggest that several mechanisms play a role during laser induced nucleation. To summarize, the research provides a robust analysis of different factors that can influence NPLIN. The results can be further utilized to enhance the understanding and applicability of the process.
The objective of this work is to extend the mechanistic understanding of NPLIN. This has been approached by qualitatively studying the effect of different factors on the nucleation efficiency of the non-photochemical process using unfocused pulsed laser in aqueous supersaturated solution of KCl. The factors investigated include wavelength, peak intensity, supersaturation, mixing, and impurity level of the solution. Each of these parameters are studied using high number of samples (80-100) to generate a robust set of results and to avoid the stochastic nature of nucleation.
In a separate series of experiments, an acoustic wave was detected in the solution due to the non-linear interaction of the unfocused laser with the system by measuring the pressure signal with a piezo-electric transducer placed just below the air-liquid interface. Further experiments were executed to understand the nature of the acoustic wave and its influence on NPLIN. The results show that laser could induce nucleation at significantly low peak intensities, much below the previously reported intensity threshold in literature. It is also observed that NPLIN shows a strong dependence on peak intensity, supersaturation, impurity level, and mixing of the solution while the dependence on wavelength was found to be weak. Furthermore, the acoustic wave experiments show that the laser induced pressure fluctuations do not affect the nucleation efficiency of the process. Overall, the results suggest that several mechanisms play a role during laser induced nucleation. To summarize, the research provides a robust analysis of different factors that can influence NPLIN. The results can be further utilized to enhance the understanding and applicability of the process.