The Influence of Experimental Data Quality and Quantity on Parameter Estimation Accuracy: Andrews Inhibition Model as a Case Study

Model parameters are usually estimated through minimization algorithms with respect to experimental data. However, students should realize that the values obtained in the classical minimization approach are not always correct and need critical evaluation though the minimum of the cost function is attained. For this purpose, a typical example of a substrate inhibition model in activated sludge processes (Andrews’ model) was used. Once the parameters were estimated, the confidence intervals were assessed through a numerical method based on the Fisher Information Matrix. Both procedures were implemented in MATLAB® (software available on request). With this exercise, the student can easily observe how the reliability of the estimated parameter value increases with the increase of data quantity and with the decrease of the data measurement error.