Applied Chemical Engineering

Optimization of the design of anoxic/oxic process in wastewater treatment plant using genetic algorithms

Ahmed Makki AL-Sulaiman

Civil Eng. Dep, College of Engineering, University of Al-Qadisiyah, 58002, Qadisiyah, Iraq

Ali Turki Alzeyadi

Civil Eng. Dep, College of Engineering, University of Al-Qadisiyah, 58002, Qadisiyah, Iraq

Hasan Mahdi M. Al-Khateeb

Civil Engineering Dep, Faculty of Engineering, University of Kufa, 540011,Najaf Province, Iraq

DOI: https://doi.org/10.59429/ace.v8i3.5736

Keywords: Anoxic; oxic; optimization; wwtp; genetic algorithm

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Abstract

Optimizing the biodegradation process in wastewater treatment plants (WWTPs) is a crucial factor in enhancing the performance and cost-effectiveness of biological treatment. This study investigates the optimization of anoxic/oxic processes using genetic algorithms (GA) to minimize capital, maintenance, and operational costs while improving nitrogen removal efficiency. A WWTP in Karbala, Iraq, was selected as a case study, and GA was applied to identify the optimum design parameters for different influent conditions. The results indicate that the optimal detention time for anoxic units ranges from 3 to 4 hours, while oxic units perform best with detention times between 8 and 12 hours. The return activated sludge (RAS) cycle was optimized at 0.8–1.5 hours, with an ideal solids retention time (SRT) of 13 days. For the secondary clarifier, optimum diameters were found to be 90 m, 50 m, and 15 m at maximum, average, and minimum flowrates, respectively. The GA-based approach demonstrates robust performance in handling multi-variable optimization, ensuring stable treatment efficiency under varying influent loads. Findings highlight that efficiency increases from 85% to 98% with decreasing influent flow, while stability is maintained despite fluctuations in suspended solids. This work confirms that GA provides an effective decision-support tool for WWTP design, offering reliable parameter predictions that enhance system sustainability and adaptability. The proposed framework can guide future developments in wastewater process optimization and serve as a transferable methodology for other environmental engineering applications.

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