Understanding the effects of bulk mixing on the determination of the affinity index: consequences for process operation and design.

The main objective of this study is to demonstrate the importance of mixing conditions as a source of inconsistencies between half-saturation indices in comparable systems (e.g. conventional activated sludge, membrane bioreactor) when operated at different conditions or different scales. As proof-of-principle, an exemplary system consisting of the second vessel of a hybrid respirometer has been studied. The system has been modeled both using an integrated computational fluid dynamics (CFD)-biokinetic model (assumed to represent the physical system) and a tanks-in-series, completely stirred tank reactor biokinetic model (representing the applied model). The results show that different mixing conditions cause deviations in the half-saturation indices calculated when matching the applied model to the physical system performance. Additionally, sensor location has been shown to impact the calculation of half-saturation indices in the respirometric system. This will only become more pronounced at larger scales. Thus, mixing conditions clearly affect operation and design of wastewater treatment reactors operated at low substrate concentrations. Both operation and design can be improved with the development and application of integrated CFD-biokinetic or compartmental models.

From the affinity constant to the half-saturation index: understanding conventional modeling concepts in novel wastewater treatment processes.

The "affinity constant" (KS) concept is applied in wastewater treatment models to incorporate the effect of substrate limitation on process performance. As an increasing number of wastewater treatment processes rely on low substrate concentrations, a proper understanding of these so-called constants is critical in order to soundly model and evaluate emerging treatment systems. In this paper, an in-depth analysis of the KS concept has been carried out, focusing on the different physical and biological phenomena that affect its observed value. By structuring the factors influencing half-saturation indices (newly proposed nomenclature) into advectional, diffusional and biological, light has been shed onto some of the apparent inconsistencies present in the literature. Particularly, the importance of non-ideal mixing as a source of variability between observed KS values in different systems has been illustrated. Additionally, discussion on the differences existent between substrates that affect half-saturation indices has been carried out; it has been shown that the observed KS for some substrates will reflect transport or biological limitations more than others. Finally, potential modeling strategies that could alleviate the shortcomings of the KS concept have been provided. These could be of special importance when considering the evaluation and design of emerging wastewater treatment processes.

Membrane bioreactor fouling behaviour assessment through principal component analysis and fuzzy clustering.

Adequate membrane bioreactor operation requires frequent evaluation of the membrane state. A data-driven approach based on principal component analysis (PCA) and fuzzy clustering extracting the necessary monitoring information solely out of transmembrane pressure data was investigated for this purpose. Out of three tested PCA techniques the two functional methods proved useful to cope with noise and outliers as opposed to the common standard PCA, while all of them presented similar capabilities for revealing data trends and patterns. The expert functional PCA approach enabled linking the two major trends in the data to reversible fouling and irreversible fouling. The B-splines approach provided a more objective way for functional representation of the data set but its complexity did not appear justified by better results. The fuzzy clustering algorithm, applied after PCA, was successful in recognizing the data trends and placing the cluster centres in meaningful positions, as such supporting data analysis. However, the algorithm did not allow a correct classification of all data. Factor analysis was used instead, exploiting the linearity of the observed two dimensional trends, to completely split the reversible and irreversible fouling effects and classify the data in a more pragmatic approach. Overall, the tested techniques appeared useful and can serve as the basis for automatic membrane fouling monitoring and control.