Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Study on Coagulant Feeding Control of the Water Treatment Plant Using Intelligent Algorithms

지능알고리즘에 의한 정수장 약품주입제어에 관한 연구

  • 김용열 (충남대학교 기계설계공학과) ;
  • 강이석 (한국수자원공사 광주권관리단)
  • Published : 2003.01.01
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Abstract

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.

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References

  1. 환경부, 上水道施設基準, 1997. 12
  2. 山本信二, 鈴木潔, 'Automatic dosing control of coagulant and alkaline chemicals by the auto jar tester,' 일본공업용수, Vol. 407, pp. 12-17, 1992
  3. E. L. Bean, S. J. Campbell, F. R. Anspach, 'Zeta potential measurements in the control of coagulation chemical doses,' J. AWWA. Vol. 56, No. 2, pp. 214-227, 1964
  4. S. K. Dental and K. M. Kingery, 'Using streaming detectors in water treatment,' J. AWWA, Vol. 81, 3 pp. 85-94, 1989
  5. M. M. Abu-orf and S. K. Dental, 'Automatic control of polymer dose using the streaming current detector,' Water Environment Research, Vol. 70, pp. 1005-1018, 1998 https://doi.org/10.2175/106143098X123336
  6. 中村文雄, '응집제주입량 결정방식에 관한 연구 (II),' 일본수도협회잡지, 제 481호, pp. 26, 1974
  7. 김홍석, 김성헌, 'Jar-Test를 利用한 凝集劑 注入率 決定에 관한 實驗硏究,' 대한상하수도학회지, 제 2호, pp. 39-46, 1993
  8. T. Takagi, M. Sugeno, 'Fuzzy identification of systems and its applications to modeling and control,' IEEE Trans on System, Man and Cybernetics, Vol. SMC-15, No. 1, pp. 116-131, 1985 https://doi.org/10.1109/TSMC.1985.6313399
  9. 남의석, 박종진, 장석호, 차상엽, 우광방, 이봉국, 한태환, 고택범, '신경회로망을 이용한 상수처리시스템의 응집제 주입 공정 최적화,' 제어.자동화.시스템공학회 논문지, 제 3권, 제 6호, pp. 644-651, 1997
  10. 주대성, 최동진, 박희경, '인공신경회로망을 이용한 응집제 최적주입률 결정,' 한국물환경학회지, 제 15권, 제 1호, pp. 23-30, 1999
  11. 김용열, 강이석, '유전-퍼지를 이용한 정수장 응집 제주입률 결정에 관한 연구,' 제어.자동화.시스템공학논문지, 제 5권, 제 7호, pp. 876-882, 1999. 10
  12. 정우섭, 오석영, '지능형 응집제 투입 시스템의 개발,' 제어.자동화.시스템공학논문지, 제 3권, 제 6호, pp. 652-658, 1997. 12
  13. 황희수, 오성권, 우광방, '유전알고리즘과 퍼지 추론시스템의 합성,' 전기공학논문지, 제 41권, 제 9호, pp. 1095-1103, 1992
  14. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989
  15. M. Mizumoto, 'Fuzzy controls under product sum-gravity methods and new fuzzy control methods,' Fuzzy Control System, CRC Press Inc, 1994
  16. D. H. Park, A. Kandel, and G. Langholz, 'Genetic-based new fuzzy reasoning models with application to fuzzy control,' IEEE Trans. on System, Man, and Cybernetics. Vol. 24, No. 1, pp. 39-47, 1994 https://doi.org/10.1109/21.259684