Sensitivity analysis and calibration with bayesian inference of a mass-based discretized population balance model for struvite precipitation

Abstract

Struvite precipitation has raised as a promising solution to recover phosphorous in wastewater treatment plants (WWTP). Struvite is a fertilizer that varies its performance depending on its size. This shows the need to upgrade one-step classic kinetic precipitation models by new frameworks as the Population Balance Model (PBM). In this abstract a mass-based Discretized Population Balance Model (DPBM) used to predict struvite precipitation is presented. The model includes primary nucleation, growth and aggregation mechanisms as a function of supersaturation index and kinetic parameters. Main advantage of the mass-based definition is that mass continuity is guaranteed and that it is fully compatible with other chemical and physicochemical reactions. A sensitivity analysis performed reveals exponents of nucleation and growth as the most relevant parameters in the pH evolution during precipitation and final Particle Size Distribution (PSD). Experimental data was used to calibrate the model employing Bayesian Inference. Selected values of the parameters showed good agreement with reality.