Hydrodynamic induced by an array of wave energy converters. Experimental and numerical analysis

The thesis analyses the hydrodynamic induced by an array of Wave energy Converters (WECs), under an experimental and numerical point of view. WECs can be considered an innovative solution able to contribute to the green energy supply and –at the same time– to protect the rear coastal area under marine spatial planning considerations. This research activity essentially rises due to this combined concept. The WEC under exam is a floating device belonging to the Wave Activated Bodies (WAB) class. Experimental data were performed at Aalborg University in different scales and layouts, and the performance of the models was analysed under a variety of irregular wave attacks. The numerical simulations performed with the codes MIKE 21 BW and ANSYS-AQWA. Experimental results were also used to calibrate the numerical parameters and/or to directly been compared to numerical results, in order to extend the experimental database. Results of the research activity are summarized in terms of device performance and guidelines for a future wave farm installation. The device length should be “tuned” based on the local climate conditions. The wave transmission behind the devices is pretty high, suggesting that the tested layout should be considered as a module of a wave farm installation. Indications on the minimum inter-distance among the devices are provided. Furthermore, a CALM mooring system leads to lower wave transmission and also larger power production than a spread mooring. The two numerical codes have different potentialities. The hydrodynamics around single and multiple devices is obtained with MIKE 21 BW, while wave loads and motions for a single moored device are derived from ANSYS-AQWA. Combining the experimental and numerical it is suggested –for both coastal protection and energy production– to adopt a staggered layout, which will maximise the devices density and minimize the marine space required for the installation.

Progetto di reti Sensori Wireless e tecniche di Fusione Sensoriale

Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Effect of hypoxia and hyperglycemia on cell bioenergetics

Mitochondria have a central role in energy supply in cells, ROS production and apoptosis and have been implicated in several human disease and mitochondrial dysfunctions in hypoxia have been related with disorders like Type II Diabetes, Alzheimer Disease, inflammation, cancer and ischemia/reperfusion in heart. When oxygen availability becomes limiting in cells, mitochondrial functions are modulated to allow biologic adaptation. Cells exposed to a reduced oxygen concentration readily respond by adaptive mechanisms to maintain the physiological ATP/ADP ratio, essential for their functions and survival. In the beginning, the AMP-activated protein kinase (AMPK) pathway is activated, but the responsiveness to prolonged hypoxia requires the stimulation of hypoxia-inducible factors (HIFs). In this work we report a study of the mitochondrial bioenergetics of primary cells exposed to a prolonged hypoxic period . To shine light on this issue we examined the bioenergetics of fibroblast mitochondria cultured in hypoxic atmospheres (1% O2) for 72 hours. Here we report on the mitochondrial organization in cells and on their contribution to the cellular energy state. Our results indicate that prolonged hypoxia cause a significant reduction of mitochondrial mass and of the quantity of the oxidative phosphorylation complexes. Hypoxia is also responsible to damage mitochondrial complexes as shown after normalization versus citrate synthase activity. HIF-1α plays a pivotal role in wound healing, and its expression in the multistage process of normal wound healing has been well characterized, it is necessary for cell motility, expression of angiogenic growth factor and recruitment of endothelial progenitor cells. We studied hypoxia in the pathological status of diabetes and complications of diabetes and we evaluated the combined effect of hyperglycemia and hypoxia on human dermal fibroblasts (HDFs) and human dermal micro-vascular endothelial cells (HDMECs) that were grown in high glucose, low glucose concentrations and mannitol as control for the osmotic challenge.

Biological analysis of the in-vitro effects of homologous recombination inhibition and its use in cancer treatment through synthetic lethality

Synthetic lethality represents an anticancer strategy that targets tumor specific gene defects. One of the most studied application is the use of PARP inhibitors (e.g. olaparib) in BRCA1/2-less cancer cells. In BRCA2-defective tumors, olaparib (OLA) inhibits DNA single-strand break repair, while BRCA2 mutations hamper homologous recombination (HR) repair. The simultaneous impairment of those pathways leads BRCA-less cells to death by synthetic lethality. The projects described in this thesis were aimed at extending the use of OLA in cancer cells that do not carry a mutation in BRCA2 by combining this drug with compounds that could mimic a BRCA-less environment via HR inhibition. We demonstrated the effectiveness of our “fully small-molecule induced synthetic lethality” by using two different approaches. In the direct approach (Project A), we identified a series of neo-synthesized compounds (named RAD51-BRCA2 disruptors) that mimic BRCA2 mutations by disrupting the RAD51-BRCA2 interaction and thus the HR pathway. Compound ARN 24089 inhibited HR in human pancreatic adenocarcinoma cell line and triggered synthetic lethality by synergizing with OLA. Interestingly, the observed synthetic lethality was triggered by tackling two biochemically different mechanisms: enzyme inhibition (PARP) and protein-protein disruption (RAD51-BRCA2). In the indirect approach (Project B), we inhibited HR by interfering with the cellular metabolism through inhibition of LDH activity. The obtained data suggest an LDH-mediated control on HR that can be exerted by regulating either the energy supply needed to this repair mechanism or the expression level of genes involved in DNA repair. LDH inhibition also succeeded in increasing the efficiency of OLA in BRCA-proficient cell lines. Although preliminary, these results highlight a complex relationship between metabolic reactions and the control of DNA integrity. Both the described projects proved that our “fully small-molecule-induced synthetic lethality” approach could be an innovative approach to unmet oncological needs.

Progettazione, modellazione e integrazione di risorse rinnovabili per promuovere lo sfruttamento dell'energia da moto ondoso e la Blue Growth.

L’energia da onda potrebbe assumere un ruolo fondamentale per la transizione energetica durante i prossimi decenni, grazie alla sua continuità nel tempo molto superiore rispetto ad altre risorse rinnovabili e alla sua vasta distribuzione nello spazio. Tuttavia, l’energia da onda è ancora lontana dall’essere economicamente sostenibile, a causa di diverse problematiche tecnologiche e alle difficoltà finanziarie associate. In questa ricerca, si è innanzitutto affrontata una delle maggiori sfide tecniche, nello specifico la progettazione e modellazione di sistemi di ancoraggio per i dispositivi galleggianti, proponendo possibili soluzioni per la modellazione numerica di sistemi di ancoraggio complessi e per l’ottimizzazione dei dispositivi stessi. Successivamente sono state analizzate le possibili sinergie strategiche di installazioni per lo sfruttamento della energia da onda con altre risorse rinnovabili e la loro applicazione nel contesto di aree marine multiuso. In particolare, una metodologia per la valutazione della combinazione ottimale delle risorse rinnovabili è stata sviluppata e verificata in due diversi casi studio: un’isola e una piattaforma offshore. Si è così potuto evidenziare l’importante contributo della risorsa ondosa per la continuità energetica e per la riduzione della necessità di accumulo. Inoltre, è stato concepito un metodo di supporto decisionale multicriteriale per la valutazione delle opzioni di riuso delle piattaforme offshore alla fine della loro vita operativa, come alternativa al decommissionamento, nell’ottica di una gestione sostenibile e della ottimizzazione dell’uso dello spazio marino. Sulla base dei criteri selezionati, l’inclusione di attività innovative come la produzione di energia da onda si è dimostrata essere rilevante per rendere vantaggioso il riuso rispetto al decommissionamento. Numerosi studi recenti hanno infatti sottolineato che, nell’ambito della “crescita blu”, i mercati come l’oil&gas, le attività offshore e le isole stimoleranno lo sviluppo di tecnologie innovative come lo sfruttamento dell’energia da onda, promuovendo la sperimentazione e fornendo un importante contributo all’avanzamento tecnico e alla commercializzazione.

Development of processes for the valorization of lignocellulosic biomass based on renewable energies

The world grapples with climate change from fossil fuel reliance, prompting Europe to pivot to renewable energy. Among renewables, biomass is a bioenergy and bio-carbon source, used to create high-value biomolecules, replacing fossil-based products. Alkyl levulinates, derived from biomass, hold promise as bio-additives and biofuels, especially via acid solvolysis of hexose sugars, necessitating further exploration. Alkyl levulinate's potential extends to converting into γ-valerolactone (GVL), a bio-solvent produced via hydrogenation with molecular-hydrogen. Hydrogen, a key reagent and energy carrier, aids renewable energy integration. This thesis delves into a biorefinery system study, aligning with sustainability goals, integrating biomass valorization, energy production, and hydrogen generation. It investigates optimizing technologies for butyl levulinate production and subsequent GVL hydrogenation. Sustainability remains pivotal, reflecting the global shift towards renewable and carbon bio-resources. The research initially focuses on experimenting with the optimal technology for producing butyl levulinate from biomass-derived hexose fructose. It examines the solvolysis process, investigating optimal conditions, kinetic modeling, and the impact of solvents on fructose conversion. The subsequent part concentrates on the technological aspect of hydrogenating butyl levulinate into GVL. It includes conceptual design, simulation, and optimization of the fructose-to-GVL process scheme based on process intensification. In the final part, the study applies the process to a real case study in Normandy, France, adapting it to local biomass availability and wind energy. It defines a methodology for designing and integrating the energy-supply system, evaluating different scenarios. Sustainability assessment using economic, environmental, and social indicators culminates in an overall sustainability index, indicating scenarios integrating the GVL biorefinery system with wind power and hydrogen energy storage as promising due to high profitability and reduced environmental impact. Sensitivity analyses validate the methodology's reliability, potentially extending to other technological systems.

Integration of by-products and waste from agri-food supply chains into the «water-energy-food» nexus (RECOW-ER): the role of fungi

The agricultural sector is undoubtedly one of the sectors that has the greatest impact on the use of water and energy to produce food. The circular economy allows to reduce waste, obtaining maximum value from products and materials, through the extraction of all possible by-products from resources. Circular economy principles for agriculture include recycling, processing, and reusing agricultural waste in order to produce bioenergy, nutrients, and biofertilizers. Since agro-industrial wastes are principally composed of lignin, cellulose, and hemicellulose they can represent a suitable substrate for mushroom growth and cultivation. Mushrooms are also considered healthy foods with several medicinal properties. The thesis is structured in seven chapters. In the first chapter an introduction on the water, energy, food nexus, on agro-industrial wastes and on how they can be used for mushroom cultivation is given. Chapter 2 details the aims of this dissertation thesis. In chapters three and four, corn digestate and hazelnut shells were successfully used for mushroom cultivation and their lignocellulosic degradation capacity were assessed by using ATR-FTIR spectroscopy. In chapter five, through the use of the Surface-enhanced Raman Scattering (SERS) spectroscopy was possible to set-up a new method for studying mushroom composition and for identifying different mushroom species based on their spectrum. In chapter six, the isolation of different strains of fungi from plastic residues collected in the fields and the ability of these strains to growth and colonizing the Low-density Polyethylene (LDPE) were explored. The structural modifications of the LDPE, by the most efficient fungal strain, Cladosporium cladosporioides Clc/1 strain were monitored by using the Scanning Electron Microscope (SEM) and ATR-FTIR spectroscopy. Finally, chapter seven outlines the conclusions and some hints for future works and applications are provided.

Life Cycle Thinking for sustainability assessment and decision-making in selected food supply chains in Costa Rica

The Agenda 2030 contains 17 integrated Sustainable Development Goals (SDGs). SDG 12 for Sustainable Consumption and Production (SCP) promotes the efficient use of resources through a systemic change that decouples economic growth from environmental degradation. The Food Systems (FS) pillar in SDG 12 entails paramount relevance due to its interconnection to many other SDGs, and even when being a crucial world food supplier, the Latin American and Caribbean (LAC) Region struggles with environmental and social externalities, low investment in agriculture, inequity, food insecurity, poverty, and migration. Life Cycle Thinking (LCT) was regarded as a pertinent approach to identify hotspots and trade-offs, and support decision-making process to aid LAC Region countries as Costa Rica to diagnose sustainability and overcome certain challenges. This thesis aimed to ‘evaluate the sustainability of selected products from food supply chains in Costa Rica, to provide inputs for further sustainable decision-making, through the application of Life Cycle Thinking’. To do this, Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA) evaluated the sustainability of food-waste-to-energy alternatives, and the production of green coffee, raw milk and leafy vegetables, and identified environmental, social and cost hotspots. This approach also proved to be a useful component of decision-making and policy-making processes together with other methods. LCT scientific literature led by LAC or Costa Rican researchers is still scarce; therefore, this research contributed to improve capacities in the use of LCT in this context, while offering potential replicability of the developed frameworks in similar cases. Main limitations related to the representativeness and availability of primary data; however, future research and extension activities are foreseen to increase local data availability, capacity building, and the discussion of potential integration through Life Cycle Sustainability Assessment (LCSA).

Portfolio optimization in the energy market

Let’s put ourselves in the shoes of an energy company. Our fleet of electricity production plants mainly includes gas, hydroelectric and waste-to-energy plants. We also sold contracts for the supply of gas and electricity. For each year we have to plan the trading of the volumes needed by the plants and customers: better to fix the price of these volumes in advance with the so-called forward contracts, instead of waiting for the delivery months, exposing ourselves to price uncertainty. Here’s the thing: trying to keep uncertainty under control in a market that has never shown such extreme scenarios as in recent years: a pandemic, a worsening climate crisis and a war that is affecting economies around the world have made the energy market more volatile than ever. How to make decisions in such uncertain contexts? There is an optimization problem: given a year, we need to choose the optimal planning of volume trading times, to meet the needs of our portfolio at the best prices, taking into account the liquidity constraints given by the market and the risk constraints imposed by the company. Algorithms are needed for the generation of market scenarios over a finite time horizon, that is, a probabilistic distribution that allows a view of all the dates between now and the end of the year of interest. Algorithms are needed to solve the optimization problem: we have proposed more than one and compared them; a very simple one, which avoids considering part of the complexity, moving on to a scenario approach and finally a reinforcement learning approach.