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In this paper, a revised version of the Morris approach, which includes an improved sampling strategy based on trajectory design, has been adapted to the screening of the most influential parameters of a fuzzy controller applied to WWTPs. Due to the high number of parameters, a systematic approach has been proposed to apply this improved sampling strategy with low computational demand. In order to find out the proper repetition number of elementary effects of each input factor on model output (EE i) calculations, an iterative and automatic procedure has been applied. The results show that the sampling strategy has a significant effect on the parameter significance ranking and that random sampling could lead to a non-proper coverage of the parameter space.

Nutrient recovery technologies are rapidly expanding due to the need for the appropriate recycling of key elements from waste resources in order to move towards a truly sustainable modern society based on the Circular Economy.Nutrient recycling is a promising strategy for reducing the depletion of non-renewable resources and the environmental impact linked to their extraction and manufacture. However, nutrient recovery technologies are not yet fully mature, as further research is needed to optimize process efficiency and enhance their commercial applicability. This paper reviews state-of-the-art of nutrient recovery, focusing on frontier technological advances and economic and environmental innovation perspectives. The potentials and limitations of different technologies are discussed, covering systems based on membranes, photosynthesis, crystallization and other physical and biological nutrient recovery systems (e.g. incineration, composting, stripping and absorption and enhanced biological phosphorus recovery).

IWA PublishingBarat Baviera, R.; Serralta Sevilla, J.; Ruano García, MV.; Jiménez Douglas, E.; Ribes Bertomeu, J.; Seco Torrecillas, A.; Ferrer, J. (2013) INTRODUCTIONWhole wastewater treatment plant modelling is one of the most important topics for the scientific community. This issue has been tackled by two philosophical approaches: using separated models (which were developed for the different process units) that are connected to simulate the whole plant, or using one unique and general model for the whole plant. In 2004, the CALAGUA research group published the Biological Nutrient Removal Model Nº 1 (BNRM1, Seco et al., 2004) including different physical, chemical and biological processes taking place in a WWTP. The physical processes included were: settling and clarification processes (flocculated settling, hindered settling and thickening), volatile fatty acids elutriation and gas-liquid transfer. The chemical interactions considered were acid-base processes, where equilibrium conditions are assumed. The biological processes included were: organic matter, nitrogen and phosphorus removal; acidogenesis, acetogenesis and methanogenesis. This model has been successfully applied for the design and optimization of numerous WWTPs (Ruano et al., 2010). However, these applications showed that nitrogen removal via nitrite and chemical precipitation processes should be considered to properly simulate WWTPs.

In this work we address the issue of parameter subset selection within the scope of activated sludge model calibration. To this end, we evaluate two approaches: (i) systems analysis and (ii) experience-based approach. The evaluation has been carried out using a dynamic model (ASM2d) calibrated to describe nitrogen and phosphorus removal in the Haaren WWTP (The Netherlands). The parameter significance ranking shows that the temperature correction coefficients are among the most influential parameters on the model output. This outcome confronts the previous identifiability studies and the experience based approaches which excluded them from their analysis. Systems analysis reveals that parameter significance ranking and size of the identifiable parameter subset depend on the information content of data available for calibration. However, it suffers from heavy computational demand. In contrast, although the experience-based approach is computationally affordable, it is unable to take into account the information content issue and therefore can be either too optimistic (giving poorly identifiable sets) or pessimistic (small size of sets while much more can be estimated from the data). An appropriate combinations of both approaches is proposed which offers a realistic (doable) and sound approach for parameter subset selection in activated sludge modelling.