### Abstract

Fuzzy neural network (FNN) was applied to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a 'U' plant, in which most of process variables were measured once an hour. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times higher accuracy than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated. Construction of the simulation model for another plant 'A', in which process variables were measured once a day, was also carried out. This FNN model also had a relatively high accuracy.

Original language | English |
---|---|

Pages (from-to) | 215-220 |

Number of pages | 6 |

Journal | Journal of Bioscience and Bioengineering |

Volume | 88 |

Issue number | 2 |

DOIs | |

Publication status | Published - Jan 1 1999 |

Externally published | Yes |

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### All Science Journal Classification (ASJC) codes

- Biotechnology
- Bioengineering
- Applied Microbiology and Biotechnology

### Cite this

*Journal of Bioscience and Bioengineering*,

*88*(2), 215-220. https://doi.org/10.1016/S1389-1723(99)80205-9

**Construction of COD simulation model for activated sludge process by fuzzy neural network.** / Tomida, Shuta; Hanai, Taizo; Ueda, Naoyasu; Honda, Hiroyuki; Kobayashi, Takeshi.

Research output: Contribution to journal › Article

*Journal of Bioscience and Bioengineering*, vol. 88, no. 2, pp. 215-220. https://doi.org/10.1016/S1389-1723(99)80205-9

}

TY - JOUR

T1 - Construction of COD simulation model for activated sludge process by fuzzy neural network

AU - Tomida, Shuta

AU - Hanai, Taizo

AU - Ueda, Naoyasu

AU - Honda, Hiroyuki

AU - Kobayashi, Takeshi

PY - 1999/1/1

Y1 - 1999/1/1

N2 - Fuzzy neural network (FNN) was applied to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a 'U' plant, in which most of process variables were measured once an hour. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times higher accuracy than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated. Construction of the simulation model for another plant 'A', in which process variables were measured once a day, was also carried out. This FNN model also had a relatively high accuracy.

AB - Fuzzy neural network (FNN) was applied to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a 'U' plant, in which most of process variables were measured once an hour. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times higher accuracy than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated. Construction of the simulation model for another plant 'A', in which process variables were measured once a day, was also carried out. This FNN model also had a relatively high accuracy.

UR - http://www.scopus.com/inward/record.url?scp=0032843665&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032843665&partnerID=8YFLogxK

U2 - 10.1016/S1389-1723(99)80205-9

DO - 10.1016/S1389-1723(99)80205-9

M3 - Article

C2 - 16232601

AN - SCOPUS:0032843665

VL - 88

SP - 215

EP - 220

JO - Journal of Bioscience and Bioengineering

JF - Journal of Bioscience and Bioengineering

SN - 1389-1723

IS - 2

ER -