Enhancement of energy and cost efficiency in wastewater treatment plants using hybrid bio-inspired machine learning control techniques

Ateunkeng, Jean Gabain; Boum, Alexandre Teplaira; Bitjoka, Laurent

doi:10.1016/j.jece.2024.112496

Journal of Environmental Chemical Engineering

2024

DOI: 10.1016/j.jece.2024.112496

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Enhancement of energy and cost efficiency in wastewater treatment plants using hybrid bio-inspired machine learning control techniques

Jean Gabain Ateunkeng,

Alexandre Teplaira Boum,

Laurent Bitjoka

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2024

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Cited by 2 publications

References 36 publications

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Dry Weather Adaptations in Wastewater Treatment: Innovative Control Strategies for Effective Organic and Nitrogen Elimination

Kumar B,

Singh,

Bharath Bhushan

et al. 2024

E3S Web Conf.

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Through a meta-heuristic framework, this study examines various wastewater treatment methods in detail and proposes a novel application of genetic algorithms (GAs) in plant optimization. ASM models are adapted to include ion speciation and pairing models, and microplastics (MPs) are challenged, indicating the need for further research. An integrated model accounts for carbon, nitrogen, phosphorus, oxygen, and hydrogen, emphasizing pH’s crucial role in biological treatment processes by examining microbial growth rates and organic compound removal. By applying natural selection and evolutionary processes, GAs are investigated as an optimization tool for plants, improving gene sequence structures and, by extension, treatment processes. The importance of this is particularly evident when dealing with non-standard numerical solutions and algebraic calculations. A robust and adaptable wastewater treatment strategy that accommodates variable weather conditions is provided by the study, which illustrates GAs, their stopping conditions, and the selection process for fitness functions.

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Dry Weather Adaptations in Wastewater Treatment: Innovative Control Strategies for Effective Organic and Nitrogen Elimination

Kumar B,

Singh,

Bharath Bhushan

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

Guo,

Kim,

Park

et al. 2024

Water

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The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

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Enhancement of energy and cost efficiency in wastewater treatment plants using hybrid bio-inspired machine learning control techniques

Cited by 2 publications

References 36 publications

Dry Weather Adaptations in Wastewater Treatment: Innovative Control Strategies for Effective Organic and Nitrogen Elimination

Dry Weather Adaptations in Wastewater Treatment: Innovative Control Strategies for Effective Organic and Nitrogen Elimination

Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

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