Particle swarm optimization for the estimation of surface complexation constants with the geochemical model PHREEQC-3.1.2

Abstract

Abstract. Sorption of metals on minerals is a key process in treatment water, natural aquatic environments, and other water-related technologies. Sorption processes are usually simulated with surface complexation models; however, identifying numeric values for the thermodynamic constants from batch experiments requires a robust parameter estimation technique that does not get trapped in local minima. Recently, particle swarm optimization (PSO) techniques have attracted many researchers as an efficient and effective optimization technique to find (near-)optimum model parameters in several fields of research. In this work, uranium at low concentrations was sorbed on quartz at different pH, and the hydroPSO R optimization package was used – the first time – to calibrate the PHREEQC geochemical model, version 3.1.2. Results show that thermodynamic parameter values identified with hydroPSO are more reliable than those identified with the well-known parameter estimation (PEST) software, when both parameter estimation software are coupled to PHREEQC using the same thermodynamic input data. In addition, post-processing tools included in hydroPSO were helpful for the correct interpretation of uncertainty in the obtained model parameters and simulated values. Thus, hydroPSO proved to be an efficient and versatile optimization tool for identifying reliable thermodynamic parameter values of the PHREEQC geochemical model.