• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.317-320
• /
• 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.

• 한국정밀공학회지
• /
• 제14권1호
• /
• pp.101-107
• /
• 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.

• 제어로봇시스템학회논문지
• /
• 제3권6호
• /
• pp.652-658
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.282-285
• /
• 1996

In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.

• 제어로봇시스템학회논문지
• /
• 제10권6호
• /
• pp.551-556
• /
• 2004

Coagulation process is one of the most important processes in water treatment procedures for stable and economical operation, and coagulant dosing of this process for most plants is generally determined by the jar test. However, this method does not only take a long time to analyze and get the result but also has difficulties in applying to automatic control. This paper shows the feasibility of applying neural network to control the coagulant dosing automatically in water treatment plant. To be specific, the predicted results of the neural network model is shown to be similar to that of jar test. The input variables for learning the neural network are turbidity, water temperature, pH, and alkalinity. Combining the neural network and SCD(Streaming Current Detector) for feedforward and feedback control of injecting coagulant, a rapid change of the raw water quality can be accommodated.

• 제어로봇시스템학회논문지
• /
• 제3권6호
• /
• pp.644-651
• /
• 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 water quality such as turbidity, temperature, pH and alkalinity. However, the process of chemical reaction to improve water quality (e.g., turbidity) by 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 using Jar-test data. In this paper, a systematic control strategy is proposed to derive the optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride), using Jar-test results. A neural network model is developed for coagulant dosing and purifying process by means of six input variables (turbidity, temperature, pH, alkalinity of raw water, PAC feed rate, turbidity in flocculation) and one output variable, while considering the relationships to the reaction of coagulation and flocculation. The model is utilized to derive the optimum coagulant dosage (in the sense of minimizing turbidity of water in flocculator). The ability of the proposed control scheme validated through the field test has proved to be of considerable practical value.

• 제어로봇시스템학회논문지
• /
• 제10권11호
• /
• pp.1100-1106
• /
• 2004

In this report, a self-organizing fuzzy neural network is proposed to control chemical feeding, which is one of the most important problems in water treatment process. In the case of the learning according to raw water quality, the self-organizing fuzzy network, which can be driven by plant operator, is very effective, Simulation results of the proposed method using the data of water treatment plant show good performance. This algorithm is included to chemical feeder, which is composed of PLC, magnetic flow-meter and control valve, so the intelligent control of chemical feeding is realized.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.952-955
• /
• 1996

It is difficult to determine the feeding rate of coagulant in water treatment process, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the fusion of genetic algorithms and fuzzy inference system was used in determining of feeding rate of coagulant. The genetic algorithms are excellently robust in complex operation problems, since it uses randomized operators and searches for the best chromosome without auxiliary information from a population consists of codings of parameter set. To apply this algorithms, we made the look up table and membership function from the actual operation data of water treatment process. We determined optimum dosages of coagulant (PAC, LAS etc.) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• 제어로봇시스템학회논문지
• /
• 제5권7호
• /
• pp.876-882
• /
• 1999

It is difficult to determine the feeding rate of coagulant in the water treatment process, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the genetic-fuzzy system was used in determining the feeding 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 consists of codings of parameter set. To apply this algorithms, we made the lookup table and membership function from the actual operation data of the water treatment process. We determined optimum dosages of coagulant(LAS) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• 조명전기설비학회논문지
• /
• 제12권3호
• /
• pp.104-113
• /
• 1998

일반적으로 수처리시설은 상수처리장, 하수처리장 등을 포함하며, 이중에서 상수처리공정은 취수, 응집, 침전, 여파, 살균소독처리 과정으로 구성되어 있다. 그리고, 응집.침선 처리공정은 상수처리시스템에서 가장 핵심부분 으로, 탁도의 처리에 가장 크게 영향을 주게 되며, 이에 따른 응집제의 주입공정을 개선하기 위한 노력이 필요하다. 응집제 주입공정은 응집 반응과정에 관여하는 여러 외부요소들과 탁도와의 관계가 명확히 규정되어 있지 않고, 외부환경조건에 따라 다양하게 변하는 원수로부터 적절한 응집제의 양을 간단하게 결정할 수 없는 상황이다. 따라서, 전반적인 원수처리 공정의 자동화를 위해서는 응집제 주입공정 자동화와 수처과시설의 유지관리기 능을 갖춘 운용지원시스템을 관리자에게 제공하는 것이 요구되었다. 본 논문에서는 수처리시설의 설비유지관리와 응집제 주입공정을 자동화하는 운용지원시스템의 프로토타입올 구현하고자 한다. 응집제 주업공정의 자동화를 위해서 실제 수처리공정에서 1년간 수행된 웅집제 투입양과 원수 의 수질을 결정하는 여러 요소들과의 데이터를 이용하여 신경회로망을 학습시카고, 이를 이용하여 응집제 주업량을 결정하도록 하였다. 이렇게 구축된 웅집제 주입공정 자동화는 운영지원 시스템내 에서 운영되며, 운영지원 시스템은 상수처리설비의 유지분수뜰 위한 설비관리와 상태감시를 하는 환경을 구축하였다.