Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants – An application to the BSM2 model

Abstract

Global sensitivity analysis (GSA) is a powerful tool for quantifying the effects of model parameters on the performance outputs of engineering systems, such as wastewater treatment plants (WWTP). Due to the ever-growing sophistication of such systems and their models, significantly longer processing times are required to perform a system-wide simulation, which makes the use of traditional Monte Carlo (MC) based approaches for calculation of GSA measures, such as Sobol indices, impractical. In this work, we present a systematic framework to construct and validate highly accurate meta-models to perform an efficient GSA of complex WWTP models such as the Benchmark Simulation Model No. 2 (BSM2). The robustness and the efficacy of three meta-modeling approaches, namely polynomial chaos expansion (PCE), Gaussian process regression (GPR), and artificial neural networks (ANN), are tested on four engineering scenarios. The results reveal significant computational gains of the proposed framework over the MC-based approach without compromising accuracy.