Implementation of enhanced biological phosphorus removal (ebpr) wastewater treatment processes enriched with different microbial communities

The EBPR (Enhanced Biological Phosphorus Removal) is a type of secondary treatment in WWTPs (WasteWater Treatment Plants), quite largely used in full-scale plants worldwide. The phosphorus occurring in aquatic systems in high amounts can cause eutrophication and consequently the death of fauna and flora. A specific biomass is used in order to remove the phosphorus, the so-called PAOs (Polyphosphate Accumulating Organisms) that accumulate the phosphorus in form of polyphosphate in their cells. Some of these organisms, the so-called DPAO (Denitrifying Polyphosphate Accumulating Organisms) use as electron acceptor the nitrate or nitrite, contributing in this way also to the removal of these compounds from the wastewater, but there could be side reactions leading to the formation of nitrous oxides. The aim of this project was to simulate in laboratory scale a EBPR, acclimatizing and enriching the specialized biomass. Two bioreactors were operated as Sequencing Batch Reactors, one enriched in Accumulibacter, the other in Tetrasphaera (both PAOs): Tetrasphaera microorganisms are able to uptake aminoacids as carbon source, Accumulibacter uptake organic carbon (volatile fatty acids, VFA). In order to measure the removal of COD, phosphorus and nitrogen-derivate compounds, different analysis were performed: spectrophotometric measure of phosphorus, nitrate, nitrite and ammonia concentrations, TOC (Total Organic Carbon, measuring the carbon consumption), VFA via HPLC (High Performance Liquid Chromatography), total and volatile suspended solids following standard methods APHA, qualitative microorganism population via FISH (Fluorescence In Situ Hybridization). Batch test were also performed to monitor the NOx production. Both specialized populations accumulated as a result of SBR operations; however, Accumulibacter were found to uptake phosphates at higher extents. Both populations were able to remove efficiently nitrates and organic compounds occurring in the feeding. The experimental work was carried out at FCT of Universidade Nova de Lisboa (FCT-UNL) from February to July 2014.

Chargaff symmetric stochastic processes

Scopo della modellizzazione delle stringhe di DNA è la formulazione di modelli matematici che generano sequenze di basi azotate compatibili con il genoma esistente. In questa tesi si prendono in esame quei modelli matematici che conservano un'importante proprietà, scoperta nel 1952 dal biochimico Erwin Chargaff, chiamata oggi "seconda regola di Chargaff". I modelli matematici che tengono conto delle simmetrie di Chargaff si dividono principalmente in due filoni: uno la ritiene un risultato dell'evoluzione sul genoma, mentre l'altro la ipotizza peculiare di un genoma primitivo e non intaccata dalle modifiche apportate dall'evoluzione. Questa tesi si propone di analizzare un modello del secondo tipo. In particolare ci siamo ispirati al modello definito da da Sobottka e Hart. Dopo un'analisi critica e lo studio del lavoro degli autori, abbiamo esteso il modello ad un più ampio insieme di casi. Abbiamo utilizzato processi stocastici come Bernoulli-scheme e catene di Markov per costruire una possibile generalizzazione della struttura proposta nell'articolo, analizzando le condizioni che implicano la validità della regola di Chargaff. I modelli esaminati sono costituiti da semplici processi stazionari o concatenazioni di processi stazionari. Nel primo capitolo vengono introdotte alcune nozioni di biologia. Nel secondo si fa una descrizione critica e prospettica del modello proposto da Sobottka e Hart, introducendo le definizioni formali per il caso generale presentato nel terzo capitolo, dove si sviluppa l'apparato teorico del modello generale.

Conditional entropy and predictability for non-reversible Markov systems

The purpose of this thesis is to clarify the role of non-equilibrium stationary currents of Markov processes in the context of the predictability of future states of the system. Once the connection between the predictability and the conditional entropy is established, we provide a comprehensive approach to the definition of a multi-particle Markov system. In particular, starting from the well-known theory of random walk on network, we derive the non-linear master equation for an interacting multi-particle system under the one-step process hypothesis, highlighting the limits of its tractability and the prop- erties of its stationary solution. Lastly, in order to study the impact of the NESS on the predictability at short times, we analyze the conditional entropy by modulating the intensity of the stationary currents, both for a single-particle and a multi-particle Markov system. The results obtained analytically are numerically tested on a 5-node cycle network and put in correspondence with the stationary entropy production. Furthermore, because of the low dimensionality of the single-particle system, an analysis of its spectral properties as a function of the modulated stationary currents is performed.