Pressure Swing Adsorption on Carbon Molecular Sieves for Nitrogen Production: Modelling and Simulation with Aspen Adsorption

L’azoto è uno dei prodotti principali dell’industria chimica, utilizzato principalmente per assicurare un sicuro stoccaggio di composti infiammabili. Generatori con sistemi PSA sono spesso più economici della tradizionale distillazione criogenica. I processi PSA utilizzano una colonna a letto fisso, riempita con materiale adsorbente, che adsorbe selettivamente un componente da una miscela gassosa. L’ossigeno diffonde molto più velocemente dell'azoto nei pori di setacci molecolari carboniosi. Oltre ad un ottimo materiale adsorbente, anche il design è fondamentale per la performance di un processo PSA. La fase di adsorbimento è seguita da una fase di desorbimento. Il materiale adsorbente può essere quindi riutilizzato nel ciclo seguente. L’assenza di un simulatore di processo ha reso necessario l’uso di dati sperimentali per sviluppare nuovi processi. Un tale approccio è molto costoso e lungo. Una modellazione e simulazione matematica, che consideri tutti i fenomeni di trasporto, è richiesta per una migliore comprensione dell'adsorbente sia per l'ottimizzazione del processo. La dinamica della colonna richiede la soluzione di insiemi di PDE distribuite nel tempo e nello spazio. Questo lavoro è stato svolto presso l'Università di Scienze Applicate - Münster, Germania. Argomento di questa tesi è la modellazione e simulazione di un impianto PSA per la produzione di azoto con il simulatore di processo Aspen Adsorption con l’obiettivo di permettere in futuro ottimizzazioni di processo affidabili, attendibili ed economiche basate su computazioni numeriche. E' discussa l’ottimizzazione di parametri, dati cinetici, termodinamici e di equilibrio. Il modello è affidabile, rigoroso e risponde adeguatamente a diverse condizioni al contorno. Tuttavia non è ancora pienamente soddisfacente poiché manca una rappresentazione adeguata della cinetica ovvero dei fenomeni di trasporto di materia. La messa a punto del software permetterà in futuro di indagare velocemente nuove possibilità di operazione.

Electrolyte disorders in the human ventricle. A simulation study.

Ventricular cells are immersed in a bath of electrolytes and these ions are essential for a healthy heart and a regular rhythm. Maintaining physiological concentration of them is fundamental for reducing arrhythmias and risk of sudden cardiac death, especially in haemodialysis patients and in the heart diseases treatments. Models of electrically activity of the heart based on mathematical formulation are a part of the efforts to improve the understanding and prediction of heart behaviour. Modern models incorporate the extensive and ever increasing amounts of experimental data in incorporating biophysically detailed mechanisms to allow the detailed study of molecular and subcellular mechanisms of heart disease. The goal of this project was to simulate the effects of changes in potassium and calcium concentrations in the extracellular space between experimental data and and a description incorpored into two modern biophysically detailed models (Grandi et al. Model; O’Hara Rudy Model). Moreover the task was to analyze the changes in the ventricular electrical activity, in particular by studying the modifications on the simulated electrocardiographic signal. We used the cellular information obtained by the heart models in order to build a 1D tissue description. The fibre is composed by 165 cells, it is divided in four groups to differentiate the cell types that compound human ventricular tissue. The main results are the following: Grandi et al. (GBP) model is not even able to reproduce the correct action potential profile in hyperkalemia. Data from hospitalized patients indicates that the action potential duration (APD) should be shorter than physiological state but in this model we have the opposite. From the potassium point of view the results obtained by using O’Hara model (ORD) are in agreement with experimental data for the single cell action potential in hypokalemia and hyperkalemia, most of the currents follow the data from literature. In the 1D simulations we were able to reproduce ECGs signal in most the potassium concentrations we selected for this study and we collected data that can help physician in understanding what happens in ventricular cells during electrolyte disorder. However the model fails in the conduction of the stimulus under hyperkalemic conditions. The model emphasized the ECG modifications when the K+ is slightly more than physiological value. In the calcium setting using the ORD model we found an APD shortening in hypocalcaemia and an APD lengthening in hypercalcaemia, i.e. the opposite to experimental observation. This wrong behaviour is kept in one dimensional simulations bringing a longer QT interval in the ECG under higher [Ca2+]o conditions and vice versa. In conclusion it has highlighted that the actual ventricular models present in literature, even if they are useful in the original form, they need an improvement in the sensitivity of these two important electrolytes. We suggest an use of the GBP model with modifications introduced by Carro et al. who understood that the failure of this model is related to the Shannon et al. model (a rabbit model) from which the GBP model was built. The ORD model should be modified in the Ca2+ - dependent IcaL and in the influence of the Iks in the action potential for letting it him produce a correct action potential under different calcium concentrations. In the 1D tissue maybe a heterogeneity setting of intra and extracellular conductances for the different cell types should improve a reproduction of the ECG signal.

Modeling and simulation of a synthetic genetic circuit that implements a multicelled behavior in a growing microcolony of E. coli

Synthetic Biology is a relatively new discipline, born at the beginning of the New Millennium, that brings the typical engineering approach (abstraction, modularity and standardization) to biotechnology. These principles aim to tame the extreme complexity of the various components and aid the construction of artificial biological systems with specific functions, usually by means of synthetic genetic circuits implemented in bacteria or simple eukaryotes like yeast. The cell becomes a programmable machine and its low-level programming language is made of strings of DNA. This work was performed in collaboration with researchers of the Department of Electrical Engineering of the University of Washington in Seattle and also with a student of the Corso di Laurea Magistrale in Ingegneria Biomedica at the University of Bologna: Marilisa Cortesi. During the collaboration I contributed to a Synthetic Biology project already started in the Klavins Laboratory. In particular, I modeled and subsequently simulated a synthetic genetic circuit that was ideated for the implementation of a multicelled behavior in a growing bacterial microcolony. In the first chapter the foundations of molecular biology are introduced: structure of the nucleic acids, transcription, translation and methods to regulate gene expression. An introduction to Synthetic Biology completes the section. In the second chapter is described the synthetic genetic circuit that was conceived to make spontaneously emerge, from an isogenic microcolony of bacteria, two different groups of cells, termed leaders and followers. The circuit exploits the intrinsic stochasticity of gene expression and intercellular communication via small molecules to break the symmetry in the phenotype of the microcolony. The four modules of the circuit (coin flipper, sender, receiver and follower) and their interactions are then illustrated. In the third chapter is derived the mathematical representation of the various components of the circuit and the several simplifying assumptions are made explicit. Transcription and translation are modeled as a single step and gene expression is function of the intracellular concentration of the various transcription factors that act on the different promoters of the circuit. A list of the various parameters and a justification for their value closes the chapter. In the fourth chapter are described the main characteristics of the gro simulation environment, developed by the Self Organizing Systems Laboratory of the University of Washington. Then, a sensitivity analysis performed to pinpoint the desirable characteristics of the various genetic components is detailed. The sensitivity analysis makes use of a cost function that is based on the fraction of cells in each one of the different possible states at the end of the simulation and the wanted outcome. Thanks to a particular kind of scatter plot, the parameters are ranked. Starting from an initial condition in which all the parameters assume their nominal value, the ranking suggest which parameter to tune in order to reach the goal. Obtaining a microcolony in which almost all the cells are in the follower state and only a few in the leader state seems to be the most difficult task. A small number of leader cells struggle to produce enough signal to turn the rest of the microcolony in the follower state. It is possible to obtain a microcolony in which the majority of cells are followers by increasing as much as possible the production of signal. Reaching the goal of a microcolony that is split in half between leaders and followers is comparatively easy. The best strategy seems to be increasing slightly the production of the enzyme. To end up with a majority of leaders, instead, it is advisable to increase the basal expression of the coin flipper module. At the end of the chapter, a possible future application of the leader election circuit, the spontaneous formation of spatial patterns in a microcolony, is modeled with the finite state machine formalism. The gro simulations provide insights into the genetic components that are needed to implement the behavior. In particular, since both the examples of pattern formation rely on a local version of Leader Election, a short-range communication system is essential. Moreover, new synthetic components that allow to reliably downregulate the growth rate in specific cells without side effects need to be developed. In the appendix are listed the gro code utilized to simulate the model of the circuit, a script in the Python programming language that was used to split the simulations on a Linux cluster and the Matlab code developed to analyze the data.

Development of iodine and air chemistry models for the simulation of plasma in Helicon Plasma Thrusters

The aim of this work is to analyse the chemistry models of low pressure Helicon discharges fed with iodine and air. In particular the focus of this research is to understand the plasma dynamics in order to predict propulsive performances of iodine and air-breathing Helicon Plasma Thrusters. The two systems have been simulated and analysed with the use of global models, i.e. a 0 dimensional tool to solve the set of governing equations by assuming that all quantities are volume averaged. Furthermore, some strategies have been implemented to improve the accuracy of this approach. A verification have been accomplished on the global models for both iodine and air, comparing results against simulations taken from literature. Moreover, the iodine global model has been validated against the experimental measurements of REGULUS, an helicon plasma thruster developed by the Italian company T4i, with a good agreement. From the analysis of iodine model, it has been found a significantly higher density for atomic positive ions with respect to molecular ions. Negative ions, instead, have shown to have negligible effect on the propulsive results. Also, the influence of reactions between heavy particles has been analysed with the global model. Results have demonstrated that, in the iodine case, chemistry is almost entirely affected by electronic collisions. For what concerns air-breathing results, it has been investigated the effects of the orbital height on propulsive performances. In particular, the global model has shown that at lower height, the values of thrust and specific impulse are lower due a change in atmosphere concentration. Finally, the iodine chemistry model has been introduced in the fluid code 3D-VIRTUS in order to preliminary assess the plasma properties of a Helicon discharge chamber for electric propulsion.