Optimization of coagulant dosing process in water purification system using neural network

신경회로망을 이용한 상수처리시스템의 응집제 주입공정 최적화

  • Published : 1997.12.01

Abstract

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.

Keywords

References

  1. 상수도공학 박중현(외)
  2. System and Control v.28 Application of fuzzy theory to the control of coagulant injection in a water purification plant O. Yagishita;O. Itoh;M. Sugeno
  3. 상하수도학회논문지 no.2 Jar-test를 이용한 응집제 주입율 결정에 관한 실험 연구 김홍석;김성헌
  4. Control and Automation of Water and Wastewater Treatment and Transport Systems Intelligent support system for water sewage treatment plants which includes a past history learning function - coagulant injection guidance system using neuralnet algorithm instrumentation Baba K.;Enbutsu I.;Matsuzaki H.;Nogita S.
  5. 34th National Water Service Research Presentation and Lecture Meeting Estimation of chemicals feed rate by fuzzy reasoning Yagishita;Tanaka
  6. Proceedings of IJCNN '90 Explicit representation of knowledge acquired from plant historical data using neural network Baba K.;Enbutsu I.;Yoda M.
  7. Proceedings of IJCNN '91 Seattle Fuzzy rule extraction from a multilayered neural network Enbutsu I.;Baba K.;Hara N.
  8. J. AWWA v.53 Eletrophoretic studies if turbidity removal coagulant with aluminum sulfate A. P. Black;Hamnah S. A.
  9. 환경과 공해 v.4 no.1 환경관리의 이론과 계산 조영일
  10. Water supply and pollution control J. W. Clark;W. Viesman, Jr.;M. J. Hammer
  11. 박사학위논문, 한양대학교 대학원 수질변화에 대한 정수처리의 실험적 연구 정규영
  12. 상수도 시설기준 건설부
  13. J. AWWA v.83 no.6 European drinking waer standards Michael Carney
  14. Preconf. Seminar on Influence of Coagulation on the Selection, Operation, and Performance of Water Treatment Facilities Raw water quality, coagulant selection, and solid-liquid separation O'Melia, CR(et al.)
  15. Separation and fate of aluminum in water treatment Van Benschoten, J. E.