Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Engineering Concurrent and Event-driven Web Apps: An Agent-Oriented Approach based on the simpAL Language

Web is constantly evolving, thanks to the 2.0 transition, HTML5 new features and the coming of cloud-computing, the gap between Web and traditional desktop applications is tailing off. Web-apps are more and more widespread and bring several benefits compared to traditional ones. On the other hand reference technologies, JavaScript primarly, are not keeping pace, so a paradim shift is taking place in Web programming, and so many new languages and technologies are coming out. First objective of this thesis is to survey the reference and state-of-art technologies for client-side Web programming focusing in particular on what concerns concurrency and asynchronous programming. Taking into account the problems that affect existing technologies, we finally design simpAL-web, an innovative approach to tackle Web-apps development, based on the Agent-oriented programming abstraction and the simpAL language. == Versione in italiano: Il Web è in continua evoluzione, grazie alla transizione verso il 2.0, alle nuove funzionalità introdotte con HTML5 ed all’avvento del cloud-computing, il divario tra le applicazioni Web e quelle desktop tradizionali va assottigliandosi. Le Web-apps sono sempre più diffuse e presentano diversi vantaggi rispetto a quelle tradizionali. D’altra parte le tecnologie di riferimento, JavaScript in primis, non stanno tenendo il passo, motivo per cui la programmazione Web sta andando incontro ad un cambio di paradigma e nuovi linguaggi e tecnologie stanno spuntando sempre più numerosi. Primo obiettivo di questa tesi è di passare al vaglio le tecnologie di riferimento ed allo stato dell’arte per quel che riguarda la programmmazione Web client-side, porgendo particolare attenzione agli aspetti inerenti la concorrenza e la programmazione asincrona. Considerando i principali problemi di cui soffrono le attuali tecnologie passeremo infine alla progettazione di simpAL-web, un approccio innovativo con cui affrontare lo sviluppo di Web-apps basato sulla programmazione orientata agli Agenti e sul linguaggio simpAL.

SmartForge: An event-driven architecture for simulator-based process optimization

In the metal industry, and more specifically in the forging one, scrap material is a crucial issue and reducing it would be an important goal to reach. Not only would this help the companies to be more environmentally friendly and more sustainable, but it also would reduce the use of energy and lower costs. At the same time, the techniques for Industry 4.0 and the advancements in Artificial Intelligence (AI), especially in the field of Deep Reinforcement Learning (DRL), may have an important role in helping to achieve this objective. This document presents the thesis work, a contribution to the SmartForge project, that was performed during a semester abroad at Karlstad University (Sweden). This project aims at solving the aforementioned problem with a business case of the company Bharat Forge Kilsta, located in Karlskoga (Sweden). The thesis work includes the design and later development of an event-driven architecture with microservices, to support the processing of data coming from sensors set up in the company's industrial plant, and eventually the implementation of an algorithm with DRL techniques to control the electrical power to use in it.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Effects of different hypoxia timing regimes on a transitional habitat's ( Pialassa Baiona, Ravenna, Italy) benthic community.

The aim of this thesis was to quantify experimentally in the field the effects of different timing regimes of hypoxia on the structure of benthic communities in a transitional habitat. The experiment was performed from 8 July to 29 July 2019 in a shallow subtidal area in Pialassa Baiona (Italy), a lagoon characterized by mixing regimes dominated by the tide. The benthic community was isolated using cylinders 15,5Cm x 20Cm size. Hypoxic conditions were imposed by covering the treated cylinders with a black plastic bag while control cylinders were left uncovered. We created 4 different timing regimes of hypoxia by manipulating both the duration of hypoxia (4 or 8 days) as well as the ratio between the duration of subsequent periods of hypoxia and the duration of a normoxic period between subsequent hypoxic events (D4R3/2, D8R3/2). At the end of each experimental trial, the benthic communities within each pot were retrieved, sieved in the field and subsequent analyzed in the laboratory where organisms were identified and counted. Results showed that benthic organism were generally negatively affected by hypoxic stress events. As expected, longer hypoxic events caused a stronger decrease of benthic community abundance. When the hypoxic events were interrupted by the normoxic event there were two different results. If the hypoxic period was too long, the normoxic period didn’t cause a positive recovery effect, and further decline of the benthic community was observed. Conversely normoxia had positive effects if the period of hypoxia was short enough not to compromise the benthic community. This resulted in a statistically significant interaction between the tested factors Duration and Ratio. Amphipods were the most sensitive organisms to hypoxia. We conclude that the effects of hypoxia can be greatly relieved by short normoxic periods if they happen frequently enough.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

A performance analysis of mesh models for cloud-based workflows

Nell'ambito della loro trasformazione digitale, molte organizzazioni stanno adottando nuove tecnologie per supportare lo sviluppo, l'implementazione e la gestione delle proprie architetture basate su microservizi negli ambienti cloud e tra i fornitori di cloud. In questo scenario, le service ed event mesh stanno emergendo come livelli infrastrutturali dinamici e configurabili che facilitano interazioni complesse e la gestione di applicazioni basate su microservizi e servizi cloud. L’obiettivo di questo lavoro è quello di analizzare soluzioni mesh open-source (istio, Linkerd, Apache EventMesh) dal punto di vista delle prestazioni, quando usate per gestire la comunicazione tra applicazioni a workflow basate su microservizi all’interno dell’ambiente cloud. A questo scopo è stato realizzato un sistema per eseguire il dislocamento di ognuno dei componenti all’interno di un cluster singolo e in un ambiente multi-cluster. La raccolta delle metriche e la loro sintesi è stata realizzata con un sistema personalizzato, compatibile con il formato dei dati di Prometheus. I test ci hanno permesso di valutare le prestazioni di ogni componente insieme alla sua efficacia. In generale, mentre si è potuta accertare la maturità delle implementazioni di service mesh testate, la soluzione di event mesh da noi usata è apparsa come una tecnologia ancora non matura, a causa di numerosi problemi di funzionamento.