A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor

Abstract

This study introduces the hybrid of the Bayesian optimization algorithm and support vector regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical oxygen demand, COD) and nitrogen compounds such as total Kjeldahl Nitrogen (TKN), ammonium nitrogen (NH₄-N), and nitrate nitrogen (NO₃-N) from municipal wastewater in a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. GLSCFB bioreactors treat wastewater by removing nutrients biologically. The downer of a GLSCFB bioreactor provided experimental data on TKN, NH₄-N, NO₃-N, and TCOD removal. The hybrid optimal intelligence algorithm (BOA-SVR) has improved model accuracy across multiple domains by combining BOA and SVR. The coefficient of determination (R²), residual, mean absolute error (MAE), root mean square error (RMSE), and fractional bias (FB) were used to analyze BOA-SVR model performance. The models match experimental data from four operational stages well, with R² or adj R² values above 0.99 for all responses. The model's accuracy was confirmed by relative deviations and residual plots showing dispersion around the zero-reference line. The BOA-SVR model consistently predicted dependent variables with low RMSE and MAE values (≤ 2.24 and 2.21, respectively) and near-zero FB. Computing efficiency was shown by optimizing TCOD, TKN, NH4-N, and NO3-N models in 70.61, 72.89, 74.37, and 54.07 s. A rigorous test on unseen data with different noise levels confirmed the model's stability. Furthermore, BOA-SVR performs better than traditional multiple linear regression (MLR). Overall, the BOA-SVR model predicts biological nutrient removal in municipal wastewater utilizing a GLSCFB bioreactor quickly, correctly, and efficiently, reducing experimental stress and resource use.