| Issue |
E3S Web Conf.
Volume 647, 2025
2025 The 8th International Conference on Renewable Energy and Environment Engineering (REEE 2025)
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 7 | |
| Section | Environmental Pollution Control and Remediation | |
| DOI | https://doi.org/10.1051/e3sconf/202564703003 | |
| Published online | 29 August 2025 | |
Optimizing Electric Power Consumption in a Climate-changing Environment: A Study of the Conventional Activated Sludge Process
1 Tshwane University of Technology, Faculty of Engineering and the Built Environment, Department of Civil Engineering, Pretoria, South Africa.
2 University of South Africa, Department of Civil Engineering, Florida, South Africa.
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Municipalities’ most commonly applied wastewater treatment technology is the conventional activated sludge process (ASP). The challenge is that the ASP consumes high levels of electric power, which results in high electric energy consumption. The power consumption can be attributed to the air blowers/pumps that function non-stop to supply oxygen for the survival of microorganisms in the ASP. This study proposes optimizing electric pump power consumption in the ASP. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to develop the pump power consumption optimization model. Particle Swarm Optimization (PSO) algorithm was applied to optimize the electric pump power consumption. The PSO algorithm output an optimal solution of 0.057396 kW after performing 1000 iterations. The percentage difference between the measured electric pump power consumption (0.087 kW) in the ASP and the optimized electric pump power consumption (0.057396 kW) was 34.03%. This significant difference implies that the PSO algorithm performance was satisfactory in optimizing the electric pump power consumption.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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