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

Oulebsir, Rafik; Lefkir, Abdelouahab; Safri, Abdelhamid; Bermad, Abdelmalek

doi:10.1016/j.biombioe.2019.105420

Cited by 29 publications

(16 citation statements)

References 14 publications

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“…DNN were also used by Oulebsir et al to optimize the energy consumption in WWTP using an activated sludge process. The model showed good results with a R 2 varying between 90–92% in the training period and 74–82% in the testing period, and showed a gain in energy for most of the data . RF was tested as an energy consumption model, using data from 2472 WWTPs in China .…”

Section: Wastewater Treatment Modeling Using Machine Learningmentioning

confidence: 99%

A Review of Computational Modeling in Wastewater Treatment Processes

Duarte,

Martins,

Oliveira

et al. 2023

ACS EST Water

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Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

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Section: Wastewater Treatment Modeling Using Machine Learningmentioning

confidence: 99%

A Review of Computational Modeling in Wastewater Treatment Processes

Duarte,

Martins,

Oliveira

et al. 2023

ACS EST Water

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“…There are ways to improve energy efficiency, e.g. by removing the aeration process through enhanced primary solid removal, based on advanced micro sieving and filtration processes (Oulebsir et al, 2020).…”

Section: Sewage Treatment Plant (Stp)mentioning

confidence: 99%

Decarbonisation Pathways for Services

Teske

Nagrath

Niklas

2022

Achieving the Paris Climate Agreement Goals

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The decarbonisation pathways for the service sector are derived. Brief outlines of the agriculture—food and forestry—wood product sectors, fishing industry, and water utilities are presented. The projected development of product quantities or GDP and the assumed development of energy intensities are given. The industry-specific energy consumptions and CO2 emission intensities are provided in tables. The non-energy-related CO2 emissions for all sectors analysed in this chapter are discussed and quantified.

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“…As a result of the validation and testing, the Random Forest was found to be the most efficient algorithm, with an R2 of 0.71 in the test phase. In [16], conventional activated sludge WWTPs were optimized using neural networks to reduce their energy consumption. 317 WWTPs in northwest Europe were used to train this technique.…”

Section: Machine Learning Has Recently Been Explored To Controlmentioning

confidence: 99%