Sviluppo e Applicazione di Metodologie per l'Ottimizzazione di Sistemi Energetici

La riduzione dei consumi di combustibili fossili e lo sviluppo di tecnologie per il risparmio energetico sono una questione di centrale importanza sia per l’industria che per la ricerca, a causa dei drastici effetti che le emissioni di inquinanti antropogenici stanno avendo sull’ambiente. Mentre un crescente numero di normative e regolamenti vengono emessi per far fronte a questi problemi, la necessità di sviluppare tecnologie a basse emissioni sta guidando la ricerca in numerosi settori industriali. Nonostante la realizzazione di fonti energetiche rinnovabili sia vista come la soluzione più promettente nel lungo periodo, un’efficace e completa integrazione di tali tecnologie risulta ad oggi impraticabile, a causa sia di vincoli tecnici che della vastità della quota di energia prodotta, attualmente soddisfatta da fonti fossili, che le tecnologie alternative dovrebbero andare a coprire. L’ottimizzazione della produzione e della gestione energetica d’altra parte, associata allo sviluppo di tecnologie per la riduzione dei consumi energetici, rappresenta una soluzione adeguata al problema, che può al contempo essere integrata all’interno di orizzonti temporali più brevi. L’obiettivo della presente tesi è quello di investigare, sviluppare ed applicare un insieme di strumenti numerici per ottimizzare la progettazione e la gestione di processi energetici che possa essere usato per ottenere una riduzione dei consumi di combustibile ed un’ottimizzazione dell’efficienza energetica. La metodologia sviluppata si appoggia su un approccio basato sulla modellazione numerica dei sistemi, che sfrutta le capacità predittive, derivanti da una rappresentazione matematica dei processi, per sviluppare delle strategie di ottimizzazione degli stessi, a fronte di condizioni di impiego realistiche. Nello sviluppo di queste procedure, particolare enfasi viene data alla necessità di derivare delle corrette strategie di gestione, che tengano conto delle dinamiche degli impianti analizzati, per poter ottenere le migliori prestazioni durante l’effettiva fase operativa. Durante lo sviluppo della tesi il problema dell’ottimizzazione energetica è stato affrontato in riferimento a tre diverse applicazioni tecnologiche. Nella prima di queste è stato considerato un impianto multi-fonte per la soddisfazione della domanda energetica di un edificio ad uso commerciale. Poiché tale sistema utilizza una serie di molteplici tecnologie per la produzione dell’energia termica ed elettrica richiesta dalle utenze, è necessario identificare la corretta strategia di ripartizione dei carichi, in grado di garantire la massima efficienza energetica dell’impianto. Basandosi su un modello semplificato dell’impianto, il problema è stato risolto applicando un algoritmo di Programmazione Dinamica deterministico, e i risultati ottenuti sono stati comparati con quelli derivanti dall’adozione di una più semplice strategia a regole, provando in tal modo i vantaggi connessi all’adozione di una strategia di controllo ottimale. Nella seconda applicazione è stata investigata la progettazione di una soluzione ibrida per il recupero energetico da uno scavatore idraulico. Poiché diversi layout tecnologici per implementare questa soluzione possono essere concepiti e l’introduzione di componenti aggiuntivi necessita di un corretto dimensionamento, è necessario lo sviluppo di una metodologia che permetta di valutare le massime prestazioni ottenibili da ognuna di tali soluzioni alternative. Il confronto fra i diversi layout è stato perciò condotto sulla base delle prestazioni energetiche del macchinario durante un ciclo di scavo standardizzato, stimate grazie all’ausilio di un dettagliato modello dell’impianto. Poiché l’aggiunta di dispositivi per il recupero energetico introduce gradi di libertà addizionali nel sistema, è stato inoltre necessario determinare la strategia di controllo ottimale dei medesimi, al fine di poter valutare le massime prestazioni ottenibili da ciascun layout. Tale problema è stato di nuovo risolto grazie all’ausilio di un algoritmo di Programmazione Dinamica, che sfrutta un modello semplificato del sistema, ideato per lo scopo. Una volta che le prestazioni ottimali per ogni soluzione progettuale sono state determinate, è stato possibile effettuare un equo confronto fra le diverse alternative. Nella terza ed ultima applicazione è stato analizzato un impianto a ciclo Rankine organico (ORC) per il recupero di cascami termici dai gas di scarico di autovetture. Nonostante gli impianti ORC siano potenzialmente in grado di produrre rilevanti incrementi nel risparmio di combustibile di un veicolo, è necessario per il loro corretto funzionamento lo sviluppo di complesse strategie di controllo, che siano in grado di far fronte alla variabilità della fonte di calore per il processo; inoltre, contemporaneamente alla massimizzazione dei risparmi di combustibile, il sistema deve essere mantenuto in condizioni di funzionamento sicure. Per far fronte al problema, un robusto ed efficace modello dell’impianto è stato realizzato, basandosi sulla Moving Boundary Methodology, per la simulazione delle dinamiche di cambio di fase del fluido organico e la stima delle prestazioni dell’impianto. Tale modello è stato in seguito utilizzato per progettare un controllore predittivo (MPC) in grado di stimare i parametri di controllo ottimali per la gestione del sistema durante il funzionamento transitorio. Per la soluzione del corrispondente problema di ottimizzazione dinamica non lineare, un algoritmo basato sulla Particle Swarm Optimization è stato sviluppato. I risultati ottenuti con l’adozione di tale controllore sono stati confrontati con quelli ottenibili da un classico controllore proporzionale integrale (PI), mostrando nuovamente i vantaggi, da un punto di vista energetico, derivanti dall’adozione di una strategia di controllo ottima.

The role of self-regulation as a meta-competency in developing leaders: a longitudinal field experimental study

The question of how to develop leaders so that they are more effective in a variety of situations, roles and levels has inspired a voluminous amount of research. While leader development programs such as executive coaching and 360-degree feedback have been widely practiced to meet this demand within organisations, the research in this area has only scratched the surface. Drawing from the past literature and leadership practices, the current research conceptualised self-regulation, as a metacompetency that would assist leaders to further develop the specific competencies needed to perform effectively in their leadership role, leading to an increased rating of leader effectiveness and to enhanced group performance. To test this conceptualisation, a longitudinal field experimental study was conducted across ten months with a pre- and two post-test intervention designs with a matched control group. This longitudinal field experimental compared the difference in leader and team performance after receiving self-regulation intervention that was delivered by an executive coach. Leaders in experimental group also received feedback reports from 360-degree feedback at each stage. Participants were 40 leaders, 155 followers and 8 supervisors. Leaders’ performance was measured using a multi-source perceptual measure of leader performance and objective measures of team financial and assessment performance. Analyses using repeated measure of ANCOVA on pre-test and two post-tests responses showed a significant difference between leader and team performance between experimental and control group. Furthermore, leader competencies mediated the relationship between self-regulation and performance. The implications of these findings for the theory and practice of leadership development training programs and the impact on organisational performance are discussed.

Identification of industrial sources of airborne heavy metal pollution in urban areas

The research examines the deposition of airborne particles which contain heavy metals and investigates the methods that can be used to identify their sources. The research focuses on lead and cadmium because these two metals are of growing public and scientific concern on environmental health grounds. The research consists of three distinct parts. The first is the development and evaluation of a new deposition measurement instrument - the deposit cannister - designed specifically for large-scale surveys in urban areas. The deposit cannister is specifically designed to be cheap, robust, and versatile and therefore to permit comprehensive high-density urban surveys. The siting policy reduces contamination from locally resuspended surface-dust. The second part of the research has involved detailed surveys of heavy metal deposition in Walsall, West Midlands, using the new high-density measurement method. The main survey, conducted over a six-week period in November - December 1982, provided 30-day samples of deposition at 250 different sites. The results have been used to examine the magnitude and spatial variability of deposition rates in the case-study area, and to evaluate the performance of the measurement method. The third part of the research has been to conduct a 'source-identification' exercise. The methods used have been Receptor Models - Factor Analysis and Cluster Analysis - and a predictive source-based deposition model. The results indicate that there are six main source processes contributing to deposition of metals in the Walsall area: coal combustion, vehicle emissions, ironfounding, copper refining and two general industrial/urban processes. |A source-based deposition model has been calibrated using facctorscores for one source factor as the dependent variable, rather than metal deposition rates, thus avoiding problems traditionally encountered in calibrating models in complex multi-source areas. Empirical evidence supports the hypothesised associatlon of this factor with emissions of metals from the ironfoundry industry.

The role of self-regulation in developing leaders:a longitudinal field experiment

A longitudinal field experiment examined a leader self-regulation intervention in teams engaged in a Business Strategy Module (BSM) of a University course. The BSM, which is an integral part of the degree programme, involved teams of four or five individuals, under the direction of a leader, working on a (simulated) car manufacturing task over a period of 24. weeks. Various aspects of team performance contributed towards module assessment. All leaders received multi-source feedback of leader task-relevant capabilities (from the leader, followers and module tutor). Leaders were randomly allocated into a self-regulation intervention (15 leaders, 46 followers) or control (25 leaders, 109 followers) conditions. The intervention, which was run by an independent coach, was designed to improve leaders' use of self-regulatory processes to aid the development of task-relevant leadership competencies. Survey data was collected from the leaders and followers (on three occasions: pre- and two post-test intervention), team financial performance (three occasions: post-test) and a final team report (post-test). The leader self-regulation intervention led to increased followers' ratings of leader's effectiveness, higher team financial performance and higher final team grade compared to the control (non-intervention) condition. Furthermore, the benefits of the self-regulation intervention were mediated by leaders' attaining task-relevant competencies. © 2013.

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Apports de la texture multibande dans la classification orientée-objets d'images multisources (optique et radar).

Résumé : La texture dispose d’un bon potentiel discriminant qui complète celui des paramètres radiométriques dans le processus de classification d’image. L’indice Compact Texture Unit (CTU) multibande, récemment mis au point par Safia et He (2014), permet d’extraire la texture sur plusieurs bandes à la fois, donc de tirer parti d’un surcroît d’informations ignorées jusqu’ici dans les analyses texturales traditionnelles : l’interdépendance entre les bandes. Toutefois, ce nouvel outil n’a pas encore été testé sur des images multisources, usage qui peut se révéler d’un grand intérêt quand on considère par exemple toute la richesse texturale que le radar peut apporter en supplément à l’optique, par combinaison de données. Cette étude permet donc de compléter la validation initiée par Safia (2014) en appliquant le CTU sur un couple d’images optique-radar. L’analyse texturale de ce jeu de données a permis de générer une image en « texture couleur ». Ces bandes texturales créées sont à nouveau combinées avec les bandes initiales de l’optique, avant d’être intégrées dans un processus de classification de l’occupation du sol sous eCognition. Le même procédé de classification (mais sans CTU) est appliqué respectivement sur : la donnée Optique, puis le Radar, et enfin la combinaison Optique-Radar. Par ailleurs le CTU généré sur l’Optique uniquement (monosource) est comparé à celui dérivant du couple Optique-Radar (multisources). L’analyse du pouvoir séparateur de ces différentes bandes à partir d’histogrammes, ainsi que l’outil matrice de confusion, permet de confronter la performance de ces différents cas de figure et paramètres utilisés. Ces éléments de comparaison présentent le CTU, et notamment le CTU multisources, comme le critère le plus discriminant ; sa présence rajoute de la variabilité dans l’image permettant ainsi une segmentation plus nette, une classification à la fois plus détaillée et plus performante. En effet, la précision passe de 0.5 avec l’image Optique à 0.74 pour l’image CTU, alors que la confusion diminue en passant de 0.30 (dans l’Optique) à 0.02 (dans le CTU).

Design and evaluation of multi-band RF energy harvesting circuits and antennas for WSNs

Radio frequency (RF) energy harvesting is an emerging technology that will enable to drive the next generation of wireless sensor networks (WSNs) without the need of using batteries. In this paper, we present RF energy harvesting circuits specifically developed for GSM bands (900/1800) and a wearable dual-band antenna suitable for possible implementation within clothes for body worn applications. Besides, we address the development and experimental characterization of three different prototypes of a five-stage Dickson voltage multiplier (with match impedance circuit) responsible for harvesting the RF energy. Different printed circuit board (PCB) fabrication techniques to produce the prototypes result in different values of conversion efficiency. Therefore, we conclude that if the PCB fabrication is achieved by means of a rigorous control in the photo-positive method and chemical bath procedure applied to the PCB it allows for attaining better values for the conversion efficiency. All three prototypes (1, 2 and 3) can power supply the IRIS sensor node for RF received powers of -4 dBm, -6 dBm and -5 dBm, and conversion efficiencies of 20, 32 and 26%, respectively. © 2014 IEEE.

Multi-elemental analysis of ready-to-eat “baby leaf” vegetables using microwave digestion and high-resolution continuum source atomic absorption spectrometry

The mineral content (phosphorous (P), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu)) of eight ready-to-eat baby leaf vegetables was determined. The samples were subjected to microwave-assisted digestion and the minerals were quantified by High-Resolution Continuum Source Atomic Absorption Spectrometry (HR-CS-AAS) with flame and electrothermal atomisation. The methods were optimised and validated producing low LOQs, good repeatability and linearity, and recoveries, ranging from 91% to 110% for the minerals analysed. Phosphorous was determined by a standard colorimetric method. The accuracy of the method was checked by analysing a certified reference material; results were in agreement with the quantified value. The samples had a high content of potassium and calcium, but the principal mineral was iron. The mineral content was stable during storage and baby leaf vegetables could represent a good source of minerals in a balanced diet. A linear discriminant analysis was performed to compare the mineral profile obtained and showed, as expected, that the mineral content was similar between samples from the same family. The Linear Discriminant Analysis was able to discriminate different samples based on their mineral profile.

Fast sequential multi-element determination of Ca, Mg, K, Cu, Fe, Mn and Zn for foliar diagnosis using high-resolution continuum source flame atomic absorption spectrometry: Feasibility of secondary lines, side pixel registration and least-squares background correction

The fast sequential multi-element determination of Ca, Mg, K, Cu, Fe, Mn and Zn in plant tissues by high-resolution continuum source flame atomic absorption spectrometry is proposed. For this, the main lines for Cu (324.754 nm), Fe (248.327 nm), Mn (279.482 nm) and Zn (213.857 nm) were selected, and the secondary lines for Ca (239.856 nm), Mg (202.582 nm) and K (404.414 nm) were evaluated. The side pixel registration approach was studied to reduce sensitivity and extend the linear working range for Mg by measuring at wings (202.576 nm; 202.577 nm; 202.578 nm; 202.580 nm: 202.585 nm; 202.586 nm: 202.587 nm; 202.588 nm) of the secondary line. The interference caused by NO bands on Zn at 213.857 nm was removed using the least-squares background correction. Using the main lines for Cu, Fe, Mn and Zn, secondary lines for Ca and K, and line wing at 202.588 nm for Mg, and 5 mL min(-1) sample flow-rate, calibration curves in the 0.1-0.5 mg L-1 Cu, 0.5-4.0 mg L-1 Fe, 0.5-4.0 mg L-1 Mn, 0.2-1.0 mg L-1 Zn, 10.0-100.0 mg L-1 Ca, 5.0-40.0 mg L-1 Mg and 50.0-250.0 mg L-1 K ranges were consistently obtained. Accuracy and precision were evaluated after analysis of five plant standard reference materials. Results were in agreement at a 95% confidence level (paired t-test) with certified values. The proposed method was applied to digests of sugar-cane leaves and results were close to those obtained by line-source flame atomic absorption spectrometry. Recoveries of Ca, Mg, K, Cu, Fe, Mn and Zn in the 89-103%, 84-107%, 87-103%, 85-105%, 92-106%, 91-114%, 96-114% intervals, respectively, were obtained. The limits of detection were 0.6 mg L-1 Ca, 0.4 mg L-1 Mg, 0.4 mg L-1 K, 7.7 mu g L-1 Cu, 7.7 mu g L-1 Fe, 1.5 mu g L-1 Mn and 5.9 mu g L-1 Zn. (C) 2009 Elsevier B.V. All rights reserved.

Evaluation of alternate lines of Fe for sequential multi-element determination of Cu, Fe, Mn and Zn in soil extracts by high-resolution continuum source flame atomic absorption spectrometry

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Convertitori di potenza multi-sorgente per applicazioni di energy harvesting

L’obiettivo di questa tesi è stato lo svilippo di un convertitore di potenza per applicazioni di energy harvesting in grado di convogliare l’energia estratta da diversi tipi di trasduttori di grandezze ambientali in un unico dispositivo di storage, ad es. un condensatore, utilizzabile per alimentare circuiti a basso consumo. L’idea di base è stata quella di ottimizzare il trasferimento di energia, attraverso una rete logica in grado di gestire le priorità di conversione dalle diverse tipologie di sorgenti e grazie ad una implementazione di un algoritmo di Maximum Power Point Tracking. In base alle specifiche di progetto, in una prima fase è stata sviluppata la rete a livello funzionale, poi sono stati scelti i componenti più opportuni ed infine si è verificato il funzionamento attraverso simulazioni circuitali.