Kinetic, artificial neural network, and statistical modeling to optimize the parameters of the air flotation process to remove latex suspended solids in ABS wastewater

Abstract

As China's contemporary industrial production undergoes continuous enhancement and economic development, there is a remarkable surge in the consumption of acrylonitrile–butadiene–styrene (ABS) materials. The extensive application of dissolved air flotation (DAF) has been adopted to effectively eliminate a substantial quantity of suspended solids present in ABS wastewater. Attaining precise control over the DAF process stands as the pivotal factor in enhancing the removal efficiency. In this study, we had constructed an air flotation apparatus and developed an open-structure software within the master computer. We innovatively compared the response surface methodology (RSM), artificial neural network (ANN), and adsorption kinetic model (AKM) with ABS wastewater DAF treatment process by incorporating relevant parameters. The results demonstrated that the ANN exhibited superior fitting capability in comparison to both the RSM and AKM. Drawing from the comparative results, it was evident that, in the context of COD removal, the ANN surpassed the RSM by margins of 5.06 %, 80 %, and 4.44 % in terms of R-squared (R2), Average Absolute Relative Error (AARE), and Root Mean Square Error (RMSE), respectively. The ANN outperformed the AKM by percentages of 35.71 %, 95.65 %, and 74.66 % concerning R2, AARE, and RMSE, respectively. Similar conclusions were drawn for suspended solids (SS) removal. This study provided valuable support for the adaptive adjustment of successive reaction conditions and the prediction of outlet quality, thus possessing commendable applicative significance and practical utility.