A framework for model-based assessment of resilience in water resource recovery facilities against power outage

Current practice to enhance resilience in Water Resource Recovery Facilities (WRRFs) is to ensure redundancy or back-up for most critical equipment (e.g. pumps or blowers). Model-based assessment allows evaluation of different strategies for quantitatively and efficiently enhancing resilience and justifying the allocation of resources. The goal of this study is to provide guidance for the development of tailored deterministic models of full-scale WRRFs. A framework for model-based resilience assessment is proposed that provides guidance on data collection, model selection, model calibration and scenario analysis. The framework is embedded into the Good Modeling Practice (GMP) Unified Protocol, providing a new application for resilience assessment and an initial set of stressors for WRRFs. The usefulness of the framework is illustrated through a resilience assessment of the WRRF of Girona against power outage. Results show that, for the Girona facility, limited energy back-up can cause non-compliance of WRRF discharge limits in the case of a blower power shut-down of 6 h, and around 12 h when the blower shut-down is also combined with a shut-down of the recirculation pumps. The best option to enhance resilience would be increasing the power back-up by 218%, which allows the plant to run with recirculation pumps and blowers at minimum capacity. In such a case, resilience can be further enhanced by manipulating the air supply valves to optimise the air distribution, to balance oxygen needs in each reactor with the overall system pressure. We conclude that, with industry consensus on what is considered an acceptable level of resilience, a framework for resilience assessment would be a useful tool to enhance the resilience of our current water infrastructure. Further research is needed to establish if the permit structure should accommodate levels sof functionality to account for stress events.

Ammonia Removal Using Biotrickling Filters: Part B: Determination of the Nitrogen Accumulation in the Scrubbing Liquid at a Livestock Facility Using Electrical Conductivity Measurement

It was demonstrated that the daily monitoring of electrical conductivity (EC) of scrubbing water can easily be used to determine the performance of biotrickling filters treating ammonia (NH3) emissions, generated by livestock facilities. Two different measurement campaigns were carried out on a pilot-scale biotrickling filter installed at a pig facility. Different phases of the operation were observed for each campaign, in accordance with EC values. For EC ranges of between 5 and 40 mS cm−1, performance was similar for both campaigns, indicating that the nitrogen accumulated in water (φ) was controlled by the operating conditions and biotrickling filter design (φ = 205 gN day−1 corresponding to 1.71 mS cm−1 day−1). Due to the correlation established in Part A of this study, the performance of the biotrickling filter can be directly expressed as gN h−1 m−3packing material without gas-phase monitoring. Thus, for an Empty Bed Residence Time of 1 s, the nitrogen accumulation capacity of the biotrickling filter was 24 gN h−1 m−3packing material. For higher EC values, the ammonia mass transfer slowed down and stopped with EC at around 50–60 mS cm−1 (campaign #1) and 70 mS cm−1 (campaign #2). It was evidenced that the mass transfer stopped due to ammonia mass transfer limitation controlled by the driving force, although biomass inhibition can occur at these levels of nitrogen concentration in the scrubbing liquid. EC monitoring can also be used to assess the ratio of nitrogen accumulated in water φ and amount of ammonia entering the system φmax. Thus φ/φmax ratios of 41% and 27% were recorded for campaign #1 and #2 respectively.

Community composition of marine and brackish water ammonia-oxidizing consortia developed for aquaculture application

To mitigate the toxicity of ammonia in aquaculture systems, marine and brackish water ammonia-oxidizing bacterial consortia have been developed and are used for activation of nitrifying bioreactors integrated to recirculating aquaculture systems. To shed more light on to these biological entities, diversity of both the consortia were analyzed based on random cloning of 16S rRNA gene and ammonia-oxidizing bacterial specific amoA gene sequences. The dendrograms of representative clones on the basis of amplified ribosomal DNA restriction analysis generated 22 and 19 clusters for marine and brackish water nitrifying consortia, respectively. Phylogenetic analysis demonstrated the presence of various autotrophic nitrifiers belonging to α-, β- and γ-Proteobacteria, anaerobic ammonia oxidizers, heterotrophic denitrifiers, Bacteroidetes, and Actinobacteria. Distribution patterns of the organisms within the two consortia were determined using the software Geneious and diversity indices were investigated using Mega 5.0, VITCOMIC and Primer 7. The abundance of ammonia oxidizers was found in the order of 2.21 ± 0.25 × 10⁹ copies/g wet weight of marine consortium and 6.20 ± 0.23 × 10⁷ copies/g of brackish water consortium. Besides, marine ammonia-oxidizing consortium exhibited higher mean population diversity and Shannon Wiener diversity than the brackish water counterparts.

Continuum and discrete approach in modeling biofilm development and structure: a review

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions.