• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.652-658
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• 1997

Coagulant feeding control is very important in the water treatment process. Coagulant feeding is related to the raw water quality such as turbidity, alkalinity, water temperature, pH and so on. However, since the process of chemical reaction has not been clarified so far, coagulant dosing rate has been decided by jar-test. In order to overcome the difficulty mentioned above, Fuzzy Neural Network to fuse fuzzy logic and neural network was proposed, and the scheme was applied to the automatic determination of coagulant dosing rate. This algorithm can automatically identify the if-then rules, tune the membership functions by utilizing expert's experimental data. The proposed scheme is evaluated by computer simulation and interfaced with coagulant feeder operated by magnetic flowmeter, control valve and PLC. It is shown that coagulant feeding according to real time sensing of water quality is very effective.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.101-107
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• 1997

Recently, as the raw water quality becomes to be polluted and the seasonal and local variation of water quality becomes to be severe, an exact control of coagulant dosing have been required in the water treat- ment plant. The amounts of coagulant is related to the raw water quality such as turbidity, alkalinity, water temperature, pH and edectrical conductivity. However the process of chemical reaction has not been clarified so far, so the dosing rate has been decided by jar-test, which is taken one or two hours. For the sake of this coagulant dosing control, fuzzy neural network to fuse fuzzy logic and neural network was proposed, and the scheme was applied to automatic determination of coagulant dosing rate. This controller can automatically identify the if-then rules and tune the membership functions by utilizing expert's cintrol data. It is shown that determination of coagulant dosing rate according to real time sensing of water quality is very effect.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.3
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• pp.312-319
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• 2004

The optimum dosage control is presumably the goal of every water treatment plant. However it is difficult to determine the dosage rate of coagulant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the real number genetic-fuzzy system was used in determining the dosage rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population which consists of codings of parameter set. To apply this algorithms, we made the real number rule table and membership function from the actual operation data of the water treatment plant. We determined optimum dosages of coagulant(LAS) using the fuzzy operation and compared them with the dosage rate of the actual operation data.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.278-283
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• 2005

Generally Jar-Test is available to determine the coagulant dosage rate. Disadventages associated with Jar-Test are that regular samples have to be taken requiring manual intervention and the limitation to feedback control. To deal with this difficulty, determined optimized dosage rates of coagulant to investigates the union operation method of the statistical equation which uses the multi-regression method and the SCD.

• 유체기계공업학회:학술대회논문집
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• 2004.12a
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• pp.275-281
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• 2004

Generally Jar-Test is available to determine the coagulant dosage rate. Disadventages associated with Jar-Test are that regular samples have to be taken requiring manual intervention and the limitation to feedback control. To deal with this difficulty, determined optimized dosage rates of coagulant to Investigates the union operation method of the statistical equation which uses the multi-regression method and the SCD.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.2-123
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• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1318-1321
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• 2005

As recently raw water quality has been polluted as well as its quality has been remarkably varied according to season and region, the precise control of coagulant dosage is being keenly required in water treatment plants. The amount of coagulant is closely related to raw water quality such as turbidity, alkalinity, water temperature, pH, electrical conductivity, etc. Since the optimum quantity of chemicals is not yet finalized, so dosage rate must be decided by using jar test that takes one or two hours. Hereupon, the output signal of stream current and multi-regression on historical data were proposed to be applied to the coagulant dosing control. In consequence of applying the scheme to automatic determination of the dosage rate, it was testified that the determination of dosage rate was very effective in case it is performed as to real-time sensing of water quality and the output signal of stream current.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.1
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• pp.92-97
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• 2005

This research is to investigate the effect of the dual coagulant using inorganic coagulants($AICl3{\cdot}6H2O$) and polymer on the coagulant process. Jar-test was conducted by using Kaolin injected raw water. PDA(Photometric Dispersion Analyzer) equipment in order to analyze the size of the particles and the characteristics of the shapes. The change in the rate of sample ores' residual deposited after coagulants were also compared. According to the result derived from this experiment, the concentration of inorganic coagulant reduced 50% and the residual was lower by using dual coagulants compared to using single coagulant. However the dual coagulant required sufficient mixing time, and affected particle characteristics, with the effect of the injection order of coagulants, the simultaneous injection of inorganic coagulant and polymer showed the most effective in the particle removal.