Predicting Biochemical Oxygen Demand at the Inlet of Al-Rustumiya Wastewater Treatment Plant Using Different Mathematical Techniques


  • Saja Ali Abd Department of Water Resources Engineering, College of Engineering, University of Baghdad Baghdad, Iraq
  • Ali Omran Al-Sulttani Department of Water Resources Engineering, College of Engineering, University of Baghdad Baghdad, Iraq



Particle Swarm Optimization, Multiple Linear Regression Model, MATLAB, Sensitivity Analysis


Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated using mean square error, relative absolute mistake, and coefficient of determination. PSO utilised COD, TDS, SS, pH, and Cl- as inputs, generating a mean square error of 1029.10, an average absolute relative error of 9.41%, and a coefficient of determination of 0.89. Comparisons demonstrated that the PSO model could accurately calculate the daily BOD at Al-Rustumiya wastewater treatment plant's inlet.



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How to Cite

“Predicting Biochemical Oxygen Demand at the Inlet of Al-Rustumiya Wastewater Treatment Plant Using Different Mathematical Techniques” (2024) Journal of Engineering, 30(02), pp. 16–29. doi:10.31026/j.eng.2024.02.02.

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