• 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 $TiO_2$.

• 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.

• Journal of computational fluids engineering
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• v.2 no.1
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• pp.93-108
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• 1997

This paper describes several computational results on the various energy and environmental problems using Patankar's SIMPLE method. The specific problems included in this study are : pollutant and flammable material dispersions in open and confined areas, buoyancy-driven flow in a lake, primary clarifier for water and waste water treatment, hood ventilation in workplace. cyclone combustor and Dow chlorination reactor. A control-volume based finite-difference method is employed together with the power-law scheme. The pressure-velocity coupling is resolved by the use of the revised version of SIMPLE, says SIMPLER and SIMPLEC. The Reynolds stresses are closed using the standard or RNG κ-ε models. A nonequilibrium turbulent reaction model is developed for the application of the chlorination process in the Dow thermal reactor. Other important empirical models and physical insights appeared in this study are presented and discussed in a brief note. The computational method developed in this study is considered, in general, as a viable tool for the design and determination of the optimal operating condition of various environmental engineering system of interest.

• 한국전산유체공학회:학술대회논문집
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• 1995.10a
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• pp.160-168
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• 1995

This paper describes computational efforts on the various energy and environmental problems using Patankar's SIMPLE method. The specific problems included in this study are : pollutant and flammable material dispersions in open and confined areas, aerator-induced flow in a lake for DO(dissolved oxygen) concentration, primary clarifier for water and waste water treatment, hood ventilation in workplace, cyclone and LNG combustors and Dow chlorination reactor. A control-volume based finite-difference method is employed together with the power-law scheme. The pressure-velocity coupling is resolved by the use of the revised version of SIMPLE, says SIMPLER and SIMPLEC. The Reynolds stresses are closed using the standard or the RNG $k-{\varepsilon}$ models. Turbulent reaction is modeled using two fast chemistry methods such as eddy breakup and conserved scalar models. Further, a nonequilibrium model is developed for the application of the chlorination process in the Dow reactor. Other important empirical models and physical insights appeared in this study are presented and discussed in a brief note. The computational method developed in this study is considered, in general, as a viable tool for the design and determination of the optimal condition of various engineering system of interest.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of Environmental Science International
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• v.2 no.1
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• pp.73-81
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• 1993

Adsorption process using granular activated carbon(GAC) has been considered as one of the most effective water treatment technologies to remove humic acid which is recon- niEed as trihalomethane(THM) precursor in chlorination. To design the most effective GAC process, it is necessary to conduct the test of adsor- ption performance by means of isothem, batch rate and column studies and to select the most effective activated carbon according to raw materials of GAC - lignite and coconut shell. The objective of this study is to investigate the adsorption performance of humid acid on two activated carbons - lignite activated carbon(LAC) and coconut shell activated car- bon(CAC) made in Korea. It is available to represent UV-abs and trihalomethane formation potential(THMFP) as concentration of humic acid due to good relationship. The adsorption capacity of humid acid is not concerned with surface area of activated carbon but with pore size related to about $100{\AA}$, and then LAC forming at the extent of mesopore is found to be eight times more effective in adsorption capacity than CAC forming at micropore. The adsorption capacity of LAC and CAC is better at pH 5.5 than at pH 7. Pore and surface diffusion coefficients calculated from the diffusion model are $7.61\times10^{-13}m^2/sec$, $3.52\times10^{-15}m^2/sec$ for CAC, and $3.38\times10^{-12}m^2$/sec and $Ds=1.48{\times}10^{-15}m^2/sec$ for GAC respectively. From the results of column test it shows that the performance of LAC is also better than CAC and the optimal EBCT(Empty Bed Contact Time) is 4.52min. and activated carbon removes selectively the components of humic acid to be easily formed to THM.