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Modeling of Nonlinear SBR Process for Nitrogen Removal via GA-based Polynomial Neural Network

유전자 알고리즘 기반 다항식 뉴럴네트워크를 이용한 비선형 질소제거 SBR 공정의 모델링

  • 김동원 (고려대학교 전기공학과) ;
  • 박장현 (목포대학교 제어시스템공학과) ;
  • 이호식 (충주대학교 환경공학과) ;
  • 박영환 (충추대학교 정보제어공학과) ;
  • 박귀태 (고려대학교 전기공학과)
  • Published : 2004.03.01

Abstract

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.

Keywords

References

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