Systematic identifiability study based on the Fisher Information Matrix for reducing the number of parameters calibration of an activated sludge model

This work proposes a procedure for calibration and validation of complex models by systematically obtaining identifiable parameter subsets according to the available data. The procedure uses the new RDE criteria calculated from the Fisher Information Matrix (FIM) as the ratio of normalized D to modified E criteria (RDE). It does not require expert knowledge and it defines automatically the dimension of the identifiable subset without requiring a threshold for the RDE. It was applied successfully to the study of the IWA-ASM2d model, which was implemented, calibrated and validated for an anaerobic/anoxic/oxic (A2/O) pilot WWTP operated under three different influent ammonium concentrations (15, 20 and 30 mg/L) and two internal recycling ratios (IRR = 2 and 5). Starting from 51 among all the ASM2d parameters, a sensitivity analysis around the ASM2d default values was performed. From the sensitivity ranking, the 20 best-ranked parameters were named “seeds”, since each one served for growing a parameter subset for model calibration. The subset generation process added to the seed a parameter that presented the highest RDE among all the remaining parameters of the sensitivity ranking. The process of parameter addition was repeated until the RDE decreased from the current iteration to the previous one. The best subset determined by the methodology {bPAO, YPO4, μA} presented the highest possible value of the RDE. Finally, the simulation of the WWTP with this subset fitted adequately the experimental data while the parameters obtained had low confidence intervals.