• Journal of Korean Society of Water and Wastewater
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• v.22 no.2
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• pp.213-218
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• 2008

Prediction models for post-chlorination require complicated information of reaction time, chlorine dosage considering flow rate as well as environmental conditions such as turbidity, temperature and pH. In order to operate post-chlorination process effectively, the correlations between inlet and outlet of clear well were investigated to develop prediction models of chlorine dosages in post-chlorination process. Correlations of environmental conditions including turbidity and chlorine dosage were investigated to predict residual chlorine at the outlet of clear well. A linear regression model and autoregressive model were developed to apply for the post-chlorination which take place time delay due to detention in clear well tank. The results from autoregressive model show the correlationship of 0.915~0.995. Consequently, the autoregressive model developed in this study would be applicable for real time control for post chlorination process. As a result, the autoregressive model for post chlorination which take place time delay and have multi parameters to control system would contribute to water treatment automation system by applying the process control algorithm.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.6
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• pp.842-850
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• 2006

The role of chlorination reactions in the formation of polychlorinated dibenzofurans (PCDFs) in a municipal waste incinerator was assessed using a chlorination model for predicting PCDF isomer distributions. Complete distributions of PCDF congeners were obtained from a stoker-type municipal waste incinerator operated under 13 test conditions. Samples were collected from the flue gas prior to the gas cleaning system. While total PCDF yields varied by a factor of five to six, the distributions of congeners were similar. A chlorination model, dependent only on the observed distribution of monochlorinated isomers, was developed to predict the distributions of poly-chlorinated isomers formed by chlorination of dibenzofuran (DF). Agreement between predicted and measured PCDF isomer distributions was high for all homologues, supporting the hypothesis that DF chlorination can play an important role in the formation of PCDF byproducts.

• Korean Journal of Materials Research
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• v.29 no.7
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• pp.412-424
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• 2019

Global fitting functions for Fe-selective chlorination in ilmenite($FeTiO_2$) and successive chlorination of beneficiated $TiO_2$ are proposed and validated based on a comparison with experimental data collected from the literature. The Fe-selective chlorination reaction is expressed by the unreacted shrinking core model, which covers the diffusion-controlling step of chlorinated Fe gas that escapes through porous materials of beneficiated $TiO_2$ formed by Fe-selective chlorination, and the chemical reaction-controlling step of the surface reaction of unreacted solid ilmenite. The fitting function is applied for both chemical controlling steps of the unreacted shrinking core model. The validation shows that our fitting function is quite effective to fit with experimental data by minimum and maximum values of determination coefficients of $R^2$ as low as 0.9698 and 0.9988, respectively, for operating parameters such as temperature, $Cl_2$ pressure, carbon ratio and particle size that change comprehensively. The global fitting functions proposed in this study are expressed simply as exponential functions of chlorination rate(X) vs. time(t), and each of them are validated by a single equation for various reaction conditions. There is therefore a certain practical merit for the optimal process design and performance analysis for field engineers of chlorination reactions of ilmenite and $TiO_2$.

• Clean Technology
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• v.25 no.2
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• pp.107-113
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• 2019

Numerical model that considered the shrinking core model and elutriation and degradation of particles was developed to predict selective chlorination of ilmenite and carbo-chlorination of $TiO_2$ in a two stage fluidized bed chlorination furnace. It is possible to analyze the fluidized bed chlorination reaction to be able to reflect particle distribution for mass balances and the chlorination reaction. The numerical model showed an accuracy with error less than 6% compared with fluidized bed experiments. The chlorination degree with particle size change was greater with a smaller particle size, and there was a 100 min difference to obtain a chlorination degree of 1 between $75{\mu}m$ and $275{\mu}m$. This was not shown to such a great extent with variation of temperature ($800{\sim}1000^{\circ}C$), and there was only a 10 min difference to obtain a chlorination degree of 0.9. In the first selective chlorination process, the mass reduction rate approached to the theoretical value of 0.4735 after 180 min, and chlorination changed the Fe component into $FeCl_2$ or $FeCl_3$ and showed nearly 1. In the second carbo-chlorination process, the chlorination degree of $TiO_2$ approached 0.98 and the mass fraction reached 0.02 with conversion into $TiCl_4$. In the first selective chlorination process, 98% of $TiO_2$ was produced at 180 min, and this was changed into 99% of $TiCl_4$ after an additional 90 min. Also the mass reduction rate of $TiO_2$ was reduced to 99% in the second continuous carbo-chlorination process.

• Particle and aerosol research
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• v.8 no.4
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• pp.173-180
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• 2012

SiC particles, 8.3 ${\mu}m$ in volume average diameter, were chlorinated in an alumina tubular reactor, 2.4 cm in diameter and 32 cm in length, with reactor temperature varied from 100 to $1200^{\circ}C$. The flow rate of the gas admitted to the reactor was held constant at 300 cc/min, the mole fraction of chlorine in the gas at 0.1 and the reaction time at 4 h. The chlorination was negligibly small up to the temperature of $500^{\circ}C$. Thereafter, the degree of chlorination increased remarkably with increasing temperature until $900^{\circ}C$. As the temperature was increased further from 900 to $1200^{\circ}C$, the increments in chlorination degree were rather small. At $1200^{\circ}C$, the chlorination has nearly been completed. The surface area of the residual carbon varied with chlorination temperature in a manner similar to that with the variation of chlorination degree with temperature. The surface area at $1200^{\circ}C$ was 912 $m^{2}/g$. A simple model was developed to predict the conversion of a SiC under various conditions. A Langmuir-Hinshelwood type rate law with two rate constants was employed in the model. Assuming that the two rate constants, $k_{1}$ and $k_{2}$, can be expressed as $A_{1e}^{-E_{1}/RT}$ and $A_{2e}^{-E_{2}/RT}$, the four parameters, $A_{1}$, $E_{1}$, $A_{2}$, and $E_{2}$ were determined to be 32.0 m/min, 103,071 J/mol, 2.24 $m^{3}/mol$ and 39,526 J/mol, respectively, through regression to best fit experimental data.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.5
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• pp.311-321
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• 2020

In this study, a model to optimize residual chlorine concentrations in a water supply system was developed using a multi-objective genetic algorithm. Moreover, to quantify the effects of optimized residual chlorine concentration management and to consider customer service requirements, this study developed indices to quantify the spatial and temporal distributions of residual chlorine concentration. Based on the results, the most economical operational method to manage booster chlorination was derived, which would supply water that satisfies the service level required by consumers, as well as the cost-effectiveness and operation requirements relevant to the service providers. A simulation model was then created based on an actual water supply system (i.e., the Multi-regional Water Supply W in Korea). Simulated optimizations were successful, evidencing that it is possible to meet the residual chlorine concentration demanded by consumers at a low cost.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.7
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• pp.481-484
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• 2011

Although formation of organic chloramines have been studied for decades, most of them have involved model organic compounds (e.g., amino acids) but not naturally occurring organic nitrogen in water. This study investigated formation of organic chloramines during chlorination of 16 natural organic matters (NOM) solutions which were isolated from surface water and contained dissolved organic nitrogen (DON). Organic chloramine yields per chlorine consumption was $0.25mg-Cl_2/mg-Cl_2$. Upon chlorination of NOM solutions, organic chloramines were rapidly formed within 10 minutes. The average organic chloramine yields upon addition of chlorine in to NOM solutions were $0.78mg-Cl_2/mg-DON$ at 10 minutes and $0.16mg-Cl_2/mg-DON$ at 24 hours. Organic chloramine yields increased as the dissolved organic carbon/dissolved organic nitrogen (DOC/DON) ratios decreased. Chlorination of molecular weight (10,000 Da) fractionated samples showed that the influence of DON molecular weights on the organic chloramine formation was minimal.

• Resources Recycling
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• v.25 no.3
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• pp.74-81
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• 2016

Study on chlorination of ilmenite ore were carried out by using PVC(polyvinyl chloride) as the chlorinating agent in a static bed reactor for selective removal of iron. The effect of amount of PVC and reaction temperature were investigated. It was found that the removal ratio of iron increased with amount of PVC and temperature. After reaction with HCl gas generated from PVC, porous surface of the specimens were observed. As a result, HCl gas could react with iron in the central portion of ilmenite particle through these pores. Examination of data using kinetic model suggest that the selective chlorination rate is controlled by chemical reaction at the interface of particles. The activation energy for the selective chlorination of ilmenite using PVC was calculated as 20.47 kJ/mol.

• Journal of Environmental Impact Assessment
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• v.29 no.6
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• pp.403-413
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• 2020

A mathematical model comprised with eight simultaneous quasi-linear partial differential equations was suggested to provide optimal chlorination strategy. Upstream weighted finite element method was employed to construct multidimensional numerical code. The code was verified against measured concentrations in three type of reactors. Boundary conditions and reaction rate were calibrated for the sixteen cases of experimental results to regenerate the measured values. Eight reaction rate coefficients were estimated from the modeling result. The reaction rate coefficients were expressed in terms of pH and temperature. Automatic optimal algorithm was invented to estimate the reaction rate coefficients by minimizing the sum of squares of the numerical errors and combined with the model. In order to minimize the concentration of chlorine and pollutants at the final usage sites, a real-time predictive control system is imperative which can predict the water quality variables from the chlorine disinfection process at the water purification plant to the customer by means of a model and operate the disinfection process according to the influent water quality. This model can be used to build such a system in water treatment plants.