Optimization of the energy consumption in activated sludge process using deep learning selective modeling

This paper presents a method using an artificial neural network for creating an optimal model of energy consumption in wastewater treatment plant (WWTP) using activated sludge process. The advantage of this method is the use data usually measured in most of WWTP to optimize the energy consumption of...

Full description

Saved in:
Bibliographic Details
Published in:Biomass & bioenergy Vol. 132; p. 105420
Main Authors: Oulebsir, Rafik, Lefkir, Abdelouahab, Safri, Abdelhamid, Bermad, Abdelmalek
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2020
Subjects:
Activated sludge
Deep learning
Energy
energy conservation
neural networks
Optimization
pollution
WasteWater
wastewater treatment
Deep learning
Activated sludge
WasteWater
Energy
Optimization
ISSN:0961-9534, 1873-2909
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a method using an artificial neural network for creating an optimal model of energy consumption in wastewater treatment plant (WWTP) using activated sludge process. The advantage of this method is the use data usually measured in most of WWTP to optimize the energy consumption of the biological process. This method consists of selecting the data that represent the best energy consumption using different performance criteria then use this data to train a deep neural network. The procedure of selection is divided into two parts, in the first selection we selected the data that respect the environmental standards, and in the second part we selected the data with optimal energy consumption using different pollution indicators, and this data was used to train a deep neural network, finally the best model was used to estimate the energy savings on the data not selected. The model showed good results with a coefficient of determination that varies between 90% and 92% in training period and 74%–82% in testing period, the application of the best model on the data not selected showed a gain in energy for the most of the data. •High values of performance criteria in training and testing period•Determination of three classes of energy consumption: Underconsumption, overconsumption and optimal consumption.•Determination of warning limits of overconsumption of energy.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0961-9534
1873-2909
DOI:10.1016/j.biombioe.2019.105420