Kinetic models evaluation for chemical organic matter removal prediction in a full-scale primary facultative pond treating municipal wastewater

Abstract

This study focuses on determining the bio-kinetic coefficients of chemical oxygen demand (COD) removal in full-scale primary facultative ponds (PFPs) system on the basis of 3-year continuous operation. The mean removal of chemical oxygen demand (COD), total suspended solid (TSS) and volatile suspended solid (VSS) were 80, 59 and 49%, respectively. The first-order model paired with continuous stirred-tank reactor (CSTR) and plug flow (PF) regimes, PF k–C*, Stover-Kincannon and Grau second-order models were applied to link COD concentrations at the inlet and outlet of the system and to compare the predictive power of models for the estimation of effluent COD concentrations. The Stover-Kincannon model showed the best adaptability (r2 = 0.9294) with the maximum substrate utilization rate (Umax) of 79.14 g/L· d and saturation constant (KB) of 80.65 g/L· d, whereas the Grau second-order model was the best model to predict outlet COD concentrations (r2 = 0.6925). The computed constants, m and n, of the Grau second-order model were 0.6725 and 15.867 d−1, respectively. While the Stover-Kincannon kinetic rates obtained in this study can be used to design the PFP systems in similar operational conditions, the appropriate prediction of pond behavior can be achieved using the Grau model.

Biochemical Oxygen Demand Modeling in Waste Stabilization Pond Model

“…The study is employed formulas to evaluate and calibrate the BOD concentration of remaining and removal in the hydraulic regime (Alavi, et al , 2022;Ammary, et al , 2014;Von Sperling, 2007) as below:…”

Incorporating Inhibition Effects and Hydrolysis Biokinetics into the Mathematical Model of Anaerobic Fermentation

“…The statistical analysis involved comparing model-predicted data with actual experimental results obtained from anaerobic fermentation reactors. To evaluate the performance of the biokinetic models, various statistical metrics and error measures were employed, including the coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute error (MAE), and relative percentage error (RPE) (Alavi and Ansari, 2022;Yaqub and Lee, 2022;Zhong et al, 2021). These metrics allowed researchers to quantitatively assess the agreement between the model predictions and the experimental data.…”

Influence of initial copper concentration, pH, and cross-linked alginate-chitosan and alginate-chitosan-Aspergillus australensis composite beads on the adsorption capacity and removal efficiency of copper ions

“…Three indices were evaluated to find which kinetic model best describes the behavior of the experimental data: the coefficient of determination (R 2 ), the IA index (IA), and the root mean square error (RMSE) (Alavi & Ansari, 2022;McCuen et al, 2006;Rahman & Sathasivam, 2015;Suteu et al, 2016).…”