Predicting effluent suspended solids from a dynamic enhanced biological phosphorus removal system using a neurogenetic model

W. C. Chang; C. F. Ouyang; J. S. Chen

doi:10.1080/10934529809376782

Predicting effluent suspended solids from a dynamic enhanced biological phosphorus removal system using a neurogenetic model

W. C. Chang^*
, C. F. Ouyang
, J. S. Chen

^*Corresponding author for this work

Department of Chemical and Materials Engineering

National Central University

Research output: Contribution to journal › Journal Article › peer-review

2 Scopus citations

Abstract

This study applies the neurogenetic model, i.e. a hybrid intelligent system combining genetic algorithm (GA) with artificial neural networks (ANN), to accurately predict effluent suspended solids concentrations from an enhanced biological phosphorus removal (EBPR) system under typical diurnal variation of municipal wastewater. Continuous-flow pilot plant experiments with automatic monitoring and control facilities were performed to assess the model's applicability. The effluent suspended solids concentrations from the experiments closely corresponded to those predicted by the neurogenetic model developed herein.

Original language	English
Pages (from-to)	1185-1203
Number of pages	19
Journal	Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering
Volume	33
Issue number	6
DOIs	https://doi.org/10.1080/10934529809376782
State	Published - 1998

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation
SDG 11 Sustainable Cities and Communities
SDG 14 Life Below Water

Keywords

Artificial neural network (ANN)
Dynamic
Enhanced biological phosphorus removal (EBPR) process
Genetic algorithm (GA)

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10.1080/10934529809376782

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title = "Predicting effluent suspended solids from a dynamic enhanced biological phosphorus removal system using a neurogenetic model",

abstract = "This study applies the neurogenetic model, i.e. a hybrid intelligent system combining genetic algorithm (GA) with artificial neural networks (ANN), to accurately predict effluent suspended solids concentrations from an enhanced biological phosphorus removal (EBPR) system under typical diurnal variation of municipal wastewater. Continuous-flow pilot plant experiments with automatic monitoring and control facilities were performed to assess the model's applicability. The effluent suspended solids concentrations from the experiments closely corresponded to those predicted by the neurogenetic model developed herein.",

keywords = "Artificial neural network (ANN), Dynamic, Enhanced biological phosphorus removal (EBPR) process, Genetic algorithm (GA)",

author = "Chang, \{W. C.\} and Ouyang, \{C. F.\} and Chen, \{J. S.\}",

year = "1998",

doi = "10.1080/10934529809376782",

language = "英语",

volume = "33",

pages = "1185--1203",

journal = "Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering",

issn = "1093-4529",

publisher = "Taylor and Francis Ltd.",

number = "6",

}

Predicting effluent suspended solids from a dynamic enhanced biological phosphorus removal system using a neurogenetic model. / Chang, W. C.; Ouyang, C. F.; Chen, J. S.
In: Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering, Vol. 33, No. 6, 1998, p. 1185-1203.

Research output: Contribution to journal › Journal Article › peer-review

TY - JOUR

T1 - Predicting effluent suspended solids from a dynamic enhanced biological phosphorus removal system using a neurogenetic model

AU - Chang, W. C.

AU - Ouyang, C. F.

AU - Chen, J. S.

PY - 1998

Y1 - 1998

N2 - This study applies the neurogenetic model, i.e. a hybrid intelligent system combining genetic algorithm (GA) with artificial neural networks (ANN), to accurately predict effluent suspended solids concentrations from an enhanced biological phosphorus removal (EBPR) system under typical diurnal variation of municipal wastewater. Continuous-flow pilot plant experiments with automatic monitoring and control facilities were performed to assess the model's applicability. The effluent suspended solids concentrations from the experiments closely corresponded to those predicted by the neurogenetic model developed herein.

AB - This study applies the neurogenetic model, i.e. a hybrid intelligent system combining genetic algorithm (GA) with artificial neural networks (ANN), to accurately predict effluent suspended solids concentrations from an enhanced biological phosphorus removal (EBPR) system under typical diurnal variation of municipal wastewater. Continuous-flow pilot plant experiments with automatic monitoring and control facilities were performed to assess the model's applicability. The effluent suspended solids concentrations from the experiments closely corresponded to those predicted by the neurogenetic model developed herein.

KW - Artificial neural network (ANN)

KW - Dynamic

KW - Enhanced biological phosphorus removal (EBPR) process

KW - Genetic algorithm (GA)

UR - https://www.scopus.com/pages/publications/0032146414

U2 - 10.1080/10934529809376782

DO - 10.1080/10934529809376782

M3 - 文章

AN - SCOPUS:0032146414

SN - 1093-4529

VL - 33

SP - 1185

EP - 1203

JO - Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering

JF - Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering

IS - 6

ER -

Predicting effluent suspended solids from a dynamic enhanced biological phosphorus removal system using a neurogenetic model

Abstract

UN SDGs

Keywords

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