Effect of alkaline treatment on the removal of contaminants of emerging concern from municipal biosolids: Modelling and optimization of process parameters using RSM and ANN coupled GA

Abstract

The current study aimed in enhancing the efficiency of alkaline treatment for CECs remediation in biosolids through the application of RSM and ANN. Due to the seasonal variation of CECs in biosolids, a complete CECs profile over a period of three years were performed. Out of 64 targeted CECs, 13 PhACs (70.1 μg/kg) and 10 pesticides (57.2 μg/kg) were detected in biosolids. In order to enhance the remediation efficiency of CECs by alkaline treatment, process parameters - pH (9.0-13.0), time (3.0-12.0 h) and biosolids age (1-28 days) were optimized by statistical modelling. Using Box-Behnken design, experiments were designed and the resultant data was employed as input for model building using RSM and ANN. The developed mathematical model for alkaline treatment of biosolids using ANN predicted CECs removal with 3.2-fold lower MSE and exhibited high regression coefficient (R² > 0.99) than the conventional RSM model. Further, the multiparameter model was optimized for achieving maximum of 95.7 % CECs removal using ANN-GA. On analyzing the acute toxicity of alkaline treated residual biosolids under the optimized conditions, a reduction in LC50 by an average of 2.1-fold than initial biosolids was observed. This study not only established the application of statistical modelling in the development of an efficient remediation strategy for biosolids, which can be further applied for large-scale remediation process, but also proved the reliability and efficiency of ANN-GA.

Keywords: Artificial neural network; Biosolids; Contaminants of emerging concern; Genetic algorithm; Response surface methodology; Total organic carbon.