ACTIVATED SLUDGE SOFTWARE

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Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and shape of activated sludge flocs can indicate the conditions at the microscale, and also directly affect how well the sludge settles in a clarifier. Particle size and shape are both misleadingly 'simple' measurements. Many subtle issues, often unaddressed in informal protocols, can arise when sampling, imaging, and analyzing particles. Sampling methods may be biased or not provide enough statistical power. The samples themselves may be poorly preserved or undergo alteration during immobilization. Images may not be of sufficient quality; overlapping particles, depth of field, magnification level, and various noise can all produce poor results. Poorly specified analysis can introduce bias, such as that produced by manual image thresholding and segmentation. Affordability and throughput are desirable alongside reproducibility. An affordable, high throughput method can enable more frequent particle measurement, producing many images containing thousands of particles. A method that uses inexpensive reagents, a common dissecting microscope, and freely-available open source analysis software allows repeatable, accessible, reproducible, and partially-automated experimental results. Further, the product of such a method can be well-formatted, well-defined, and easily understood by data analysis software, easing both within-lab analyses and data sharing between labs. We present a protocol that details the steps needed to produce such a product, including: sampling, sample preparation and immobilization in agar, digital image acquisition, digital image analysis, and examples of experiment-specific figure generation from the analysis results. We have also included an open-source data analysis pipeline to support this protocol.

This review paper focuses on modelling of wastewater treatment plants (WWTP). White-box modelling is widely applied in this field, with learning, design and process optimisation as the main applications. The introduction of the ASM model family by the IWA task group was of great importance, providing researchers and practitioners with a standardised set of basis models. This paper introduces the nowadays most frequently used white-box models for description of biological nitrogen and phosphorus removal activated sludge processes. These models are mainly applicable to municipal wastewater systems, but can be adapted easily to specific situations such as the presence of industrial wastewater. Some of the main model assumptions are highlighted, and their implications for practical model application are discussed. A step-wise procedure leads from the model purpose definition to a calibrated WWTP model. Important steps in the procedure are: model purpose definition, model selection, data collection, data reconciliation, calibration of the model parameters and model unfalsification. The model purpose, defined at the beginning of the procedure, influences the model selection, the data collection and the model calibration. In the model calibration a process engineering approach, i.e. based on understanding of the process and the model structure, is needed. A calibrated WWTP model, the result of an iterative procedure, can usually be obtained by only modifying few model parameters, using the default parameter sets as a starting point. Black-box, stochastic grey-box and hybrid models are useful in WWTP applications for prediction of the influent load, for estimation of biomass activities and effluent quality parameters. These modelling methodologies thus complement the process knowledge included in white-box models with predictions based on data in areas where the white-box model assumptions are not valid or where white-box models do not provide accurate predictions. Artificial intelligence (AI) covers a large spectrum of methods, and many of them have been applied in applications related to WWTPs. AI methodologies and white-box models can interact in many ways; supervisory control systems for WWTPs are one evident application. Modular agent-based systems combining several AI and modelling methods provide a great potential. In these systems, AI methods on one hand can maximise the knowledge extracted from data and operator experience, and subsequently apply this knowledge to improve WWTP control. White-box models on the other hand allow evaluating scenarios based on the available process knowledge about the WWTP. A white-box model calibration tool, an AI based WWTP design tool and a knowledge representation tool in the WWTP domain are other potential applications where fruitful interactions between AI methods and white-box models could be developed.

The activated sludge process is the most generally applied biological wastewater treatment method. In the activated sludge process, a bacterial biomass suspension (the activated sludge) is responsible for the removal of pollutants. Depending on the design and the specific application, an activated sludge wastewater treatment plant (WWTP) can achieve biological nitrogen (N) removal and biological phosphorus (P) removal, besides removal of organic carbon substances. Evidently, many different activated sludge process configurations have evolved during the years. A review on the historical evolution of the activated sludge process can be found in, e.g. Jeppsson (1996).

The first part of this paper will focus exclusively on white-box models for description of activated sludge processes. White-box models, also called deterministic models, are based on first engineering principles, meaning that the model equations were developed from general balance equations applied to mass and other conserved quantities, resulting in a set of differential equations. An overview of the most frequently applied models will be provided, with specific attention for the assumptions or simplifications behind the models. These model assumptions are often not considered carefully by the modeller, although they provide an indication of situations where the white-box models are not valid or provide only a poor description of the process. Specifically in these cases, one could consider other modelling methodologies besides the white-box models. Another modelling approach is to combine the white-box model with knowledge-based information extraction tools. The second part of this paper will therefore focus on such alternative modelling methodologies that make use of data, and on the integration of white-box models with artificial intelligence (AI) methodologies.

The purpose of the first part of this paper is to demonstrate how the model selection, the data collection and the WWTP model calibration all relate to the modelling purpose. Note that there is an essential difference between an activated sludge model and a WWTP model. A WWTP usually consists of a set of activated sludge tanks, combined with a sedimentation tank, with a range of electron acceptor conditions occurring in the tanks. Depending on the concentrations of dissolved oxygen (DO) and

The first part of this paper has exclusively focussed on the selection, calibration and usage of white-box models for description of activated sludge processes. However, it is clear that other modelling methodologies are available and applied to the activated sludge process too. In many ways, alternative modelling methodologies are complementing and supporting the knowledge about the wastewater treatment process and its operation that is summarised in the white-box plant model. This is

Activated sludge modelling and simulation are widely applied. Learning, design and process optimisation are the main application areas of white-box WWTP models. The introduction of the ASM model family by the IWA task group was of great importance in this field, providing researchers and practitioners with a standardised set of basis models. These basis models are mainly applicable to municipal wastewater systems, but can be adapted easily to specific situations such as the presence of

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Network models and community phylogenetic analyses are applied to assess the composition, structure, and ecological assembly mechanisms of microbial communities. Here we combine both approaches to investigate the temporal dynamics of network properties in individual samples of two activated sludge systems at different adaptation stages. At initial assembly stages, we observed microbial communities adapting to activated sludge, with an increase in network modularity and co-exclusion proportion, and a decrease in network clustering, here interpreted as a consequence of niche specialization. The selective pressure of deterministic factors at wastewater treatment plants produces this trend and maintains the structure of highly functional and specialized communities responding to seasonal environmental changes. 59ce067264

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