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The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base-consumption were found low compared to the large uncertainty observed in the antibiotic and off-gas CO(2) predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass-transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes.

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By critically evaluating previous models, a new mechanistic model is developed to describe simultaneous storage and growth processes occurring in activated sludge systems under aerobic conditions. Identifiability was considered an important criterion during the model development since it among others helps to increase the realiability and applicability of models to full-scale WWTPs. A second order model was proposed for description of the degradation of the storage products under famine conditions. The model is successfully calibrated by only using OUR data obtained from batch experiments. Calibrations were performed with biomass from full-scale WWTPs in Belgium and Spain. Predictions of the calibrated model were successfully confirmed using off-line PHB measurements, supporting the validity of the model. An iterative experimental design procedure was successfully applied and found to remarkably improve the parameter estimation accuracy for the growth on storage parameters K1 and K2, which used to have large confidence intervals when using standard experiments. The estimated biomass growth yield on substrate (0.58 mgCOD/mgCOD) is quite close to the theoretically expected range for heterotrophic growth. This became possible by properly accounting for the storage process. Moreover, the maximum growth rate was predicted in the range 0.7-1.3 per day. This range, albeit quite lower than the values reported for the growth-based ASM models, is believed to be more realistic. Finally, the new model is expected to better and more mechanistically describe simultaneous storage and growth activities of activated sludge systems and as such could contribute to improved design, operation and control of those systems.