Resilient control system execution agent (ReCoSEA)

In an increasingly networked world, critical infrastructure systems suffer from two types of vulnerability. The first is the traditionally recognized problem of monitoring the systems for faults and failures, recognizing and analyzing data, and responding with real understanding to the problems of the system. Increasingly complex systems create the opportunity for single points of failure to cascade when inaccurate assessment of system health increases response time or leads to faulty analysis of the problems involved. A second problem involves vulnerability to cyber intrusion, in which bad actors can mask system deterioration or present false data about system status. A resilient system will protect stability, efficiency, and security. To ensure these three states, the system must react to changing conditions within the system with coordination: no one component of the system can be allowed to react to problems without real consideration of the effects of that action on other components within the system. Systems with multi-agent design typically have three layers of action, a management layer, a coordination layer, and an execution layer. A resilient multi-agent system will emphasize functions of the execution layer, which has the responsibility of initiating actions, monitoring, analyzing, and controlling its own processes, while feeding information back to the higher levels of management and coordination. The design concept of a resilient control system execution agent (ReCoSEA) grows out of these underpinnings, and through the use of computational intelligence techniques, this paper suggests an associated design methodology

Microbial residence time is a controlling parameter of the taxonomic composition and functional profile of microbial communities.

A remaining challenge within microbial ecology is to understand the determinants of richness and diversity observed in environmental microbial communities. In a range of systems, including activated sludge bioreactors, the microbial residence time (MRT) has been previously shown to shape the microbial community composition. However, the physiological and ecological mechanisms driving this influence have remained unclear. Here, this relationship is explored by analyzing an activated sludge system fed with municipal wastewater. Using a model designed in this study based on Monod-growth kinetics, longer MRTs were shown to increase the range of growth parameters that enable persistence, resulting in increased richness and diversity in the modeled community. In laboratory experiments, six sequencing batch reactors treating domestic wastewater were operated in parallel at MRTs between 1 and 15 days. The communities were characterized using both 16S ribosomal RNA and non-target messenger RNA sequencing (metatranscriptomic analysis), and model-predicted monotonic increases in richness were confirmed in both profiles. Accordingly, taxonomic Shannon diversity also increased with MRT. In contrast, the diversity in enzyme class annotations resulting from the metatranscriptomic analysis displayed a non-monotonic trend over the MRT gradient. Disproportionately high abundances of transcripts encoding for rarer enzymes occur at longer MRTs and lead to the disconnect between taxonomic and functional diversity profiles

Construction, start-up and operation of a continuously aerated laboratory-scale SHARON reactor in view of coupling with an Anammox reactor

In this study practical experiences during start-up and operation of a laboratory-scale SHARON reactor are discussed, along with the construction of the reactor. Special attention is given to the start-up in view of possible toxic effects of high nitrogen concentrations (up to 4 000 mgN(.)l(-1)) on the nitrifier population and because the reactor was inoculated with sludge from an SBR reactor operated under completely different conditions. Because of these considerations, the reactor was first operated as an SBR to prevent biomass washout and to allow the selection of a strong nitrifying population. A month after the inoculation the reactor was switched to normal chemostat operation. As a result the nitrite oxidisers were washed out and only the ammonium oxidisers persisted in the reactor. In this contribution also some practical considerations concerning the operation of a continuously aerated SHARON reactor, such as mixing, evaporation and wall growth are discussed. These considerations are not trivial, since the reactor will be used for kinetic characterisation and modelling studies. Finally the performance of the SHARON reactor under different conditions is discussed in view of its coupling with an Anammox unit. Full nitrification was proven to be feasible for nitrogen loads up to 1.5 gTAN-N(.)l(-1.)d(-1), indicating the possibility of the SHARON process to treat highly loaded nitrogen streams. Applying different influent concentrations led to different effluent characteristics indicating the need for proper control of the SHARON reactor

Reaction extents: A Divide-and-Conquer Approach for Kinetic Model Identification

Obtaining reliable wastewater treatment process models is critical for the application of model-based design, operation, and automation. For example, Masic et al. (2014) explored the use of an observer designed for nonlinear processes to estimate nitrite in a biological urine nitrification process. In this process, anthropogenic urine is used as a resource for the production of a fertilizer (Udert & Wächter, 2012). Thanks to the separated collection and treatment of urine via NoMix toilets (Larsen et al., 2001), the majority of the nitrogen and phosphorus released via human excreta is captured. The urine nitrification step has two purposes: to prevent (i) volatilization of ammonia by reducing the pH and (ii) production of malodourous compounds. If successful, one can store nitrified urine for long periods of time. The urine nitrification process operates at fairly high conversion rates and is prone to three important failures. The first failure is caused by inhibition of the ammonia oxidizing bacteria (AOB) at high free ammonia concentrations and can lead to washout of AOB as well as the nitrite oxidizing bacteria (NOB). The second failure is caused by growth of acid-tolerant AOB and causes the pH to decrease to a level where the NOB are inhibited and undesired chemical reactions occur. The third failure appears when a temporary accumulation of nitrite causes NOB inhibition, thereby reducing their activity. Such a nitrite accumulation can lead to an irrecoverable failure if the nitrite is allowed to accumulate to high levels (above 50 mg N/L). The first and second failures are mitigated easily by maintaining a safe pH via manipulation of the urine feed flow rate. The third failure is more difficult to avoid and requires a timely detection of nitrite. Masic et al. (2014) provided successful preliminary tests with a model-based observer, which highly depends on the availability of a reliable model. It is unlikely that standard parameter values apply due to the high-strength nature of human urine. For this reason, a well-calibrated model is desired. In Masic et al. (2016b) parameters were estimated to global optimality for the nitrite oxidation by NOB. The applied method, however, allows only estimating parameters of a single reaction system. To apply the same optimization method to multivariate processes, an extent-based methodology was tested in silico in Masic et al. (2016a). By means of the computation of reaction extents, one can separate the estimation of the parameters for each individual reaction. This extent-based modelling method however requires as many measured variables as the number of reactions (Rodrigues et al., 2015). For this reason, Masic et al. (2016a) simplified the model identification problem by considering a constant biomass, i.e. a net biomass growth equal to zero for both AOB and NOB. In the present study, the extent-based model identification method is modified to avoid this simplification, while allowing the application of the globally optimal parameter estimation procedure developed in Masic et al. (2016b). At the same time, the resulting model identification method is tested with experimental data for the first time - Larsen T A, Peters I, Alder A, Eggen R, Maurer M, Muncke J (2001). Peer reviewed: re-engineering the toilet for sustainable wastewater management. Env. Sci. Technol., 35, 192A-197A. - Masic A, Villez K (2014). Model-based observers for monitoring of a biological nitrification process for decentralized wastewater treatment – Initial results. 2nd IWA Specialized International Conference Ecotechnologies for Wastewater Treatment (EcoSTP2014), Verona, Italy, June 23–25, 2014, 402–405. - Masic A, Srinivasan S, Billeter J, Bonvin D, Villez K (2016a). Biokinetic model identification via extents of reaction. 5th IWA/WEF Wastewater Treatment Modelling Seminar (WWTmod2016), Annecy, France, April 2-6, 2016, appeared on USB-stick. - Masic A, Udert K, Villez K (2016b). Global parameter optimization for biokinetic modeling of simple batch experiments. Environ. Modell. and Softw., 85, 356-373. - Rodrigues D, Srinivasan S, Billeter J, Bonvin D (2015). Variant and invariant states for chemical reaction systems. Comp. Chem. Eng., 73, 23-33. - Udert K M, Wächter M (2012). Complete nutrient recovery from source-separated urine by nitrification and distillation. Wat. Res., 46, 453-464

Extent Computation under Rank-deficient Conditions

The identification of kinetic models can be simplified via the computation of extents of reaction on the basis of invariants such as stoichiometric balances. With extents, one can identify the structure and the parameters of reaction rates individually, which significantly reduces the number of parameters that need to be estimated simultaneously. So far, extent-based modeling has only been applied to cases where all the extents can be computed from measured concentrations. This generally excludes its application to many biological processes since the number of reactions tends to be larger than the number of measured quantities. This paper shows that, in some cases, such restrictions can be lifted. In addition, in contrast to most extent-based modeling studies that have dealt with simulated data, this study demonstrates the applicability of extent-based model identification using laboratory experimental data