(189i) Dynamic Global Sensitivity Analysis on Wastewater Stabilization Pond Networks

In this work, a wastewater treatment network consisting of a system of ponds is modeled rigorously, taking into account dynamic mass balances for the main groups of bacteria, together with different types of organic load, algae biomass and nutrients. A dynamic global sensitivity analysis (DGSA) is performed on the kinetic parameter of the model system for the optimal configuration of three stabilization ponds, obtained in a previous work (Ochoa et al., 2016). The system is comprised of two aerobic ponds in series followed by a facultative one. The main objective is the determination of the most influential parameters of the model and parameter ranking, considering the whole range of parameters variation, allowing the identification of the uncertainty sources and thus determining the needed experimental measures.

DGSA is implemented using a variance based technique proposed by Sobolâ?? (1993). Other first order and total order sensitivity estimators are also studied (Saltelli et al., 2010). An important characteristic of the method is the possibility to detect interactions between uncertain parameters. The technique is implemented within gPROMS platform, a differential algebraic equation oriented environment where stochastic simulations are performed. Temporal profiles for the first order, total order and interactional sensitivity indices are calculated for 18 differential variables taking into account 20 parameters as uncertain.

Results show a great influence of the temperature coefficient (θ) over all differential variables along the entire horizon of time, not only by its first order sensitivity effects but also through its interactional effect. There is also a notable influence of the mass transfer coefficient between layers (k_m) in the facultative pond. Global sensitivity analysis results provide a valuable insight into model features.

References

Ochoa, M.P., Estrada, V., Hoch, P.M., (2016) MINLP Wastewater Stabilisation Ponds Synthesis using Rigorous Models under Different Scenarios, Computer Aided Chemical Engineering.

Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S., (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communication 181, 259-270.

Sobol', I. M. (1993) Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling & Computational Experiment, 1, 407-414.