Title: Enhancing wastewater treatment efficiency by using ammonium-based aeration control technology for energy reduction and effluent quality improvement

Authors: Maimun Huja Husin; Mohd Fua'ad Rahmat; Norhaliza Abdul Wahab; Mohamad Faizrizwan Mohd Sabri; Shamsiah Suhaili

Addresses: Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia ' School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia ' School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia ' Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia ' Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

Abstract: Motivated by the increasing operational costs and stringent effluent requirements faced by wastewater treatment plants (WWTP) operating activated sludge systems, this study explores the potential of ammonium-based aeration control (ABAC) technology to enhance the current dissolved oxygen (DO) control strategies. The primary objectives are to reduce energy consumption and improve effluent quality to meet regulatory standards. In this study, ABAC and nitrate (SNO₂) control were proposed and analysed using the benchmark simulation model no. 1 (BSM1) simulation model. Both ABAC and nitrate control strategies were designed using neural network (NN). Comparative analysis against standard BSM1 DO proportional integral (PI) and SNO₂ PI, as well as NN ABAC combined with SNO₂ PI, reveals the superior effectiveness of the proposed control configuration. Specifically, it achieves the lowest violations of total nitrogen, ammonia, and suspended solids limits, highlighting its potential to improve the effluent quality of the WWTP. The proposed strategy shows the most significant improvements across energy consumption, effluent quality, operational costs, and regulatory compliance.

Keywords: activated sludge; BSM1; benchmark simulation model no. 1; neural network; wastewater; wastewater treatment.

DOI: 10.1504/IJMIC.2025.150867

International Journal of Modelling, Identification and Control, 2025 Vol.46 No.2, pp.124 - 137

Received: 02 Feb 2024
Accepted: 11 Nov 2024
Published online: 24 Dec 2025 *

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