Development of methodologies supporting the sustainable and safe integration of offshore conventional and renewable energy production

Against a backdrop of rapidly increasing worldwide population and growing energy demand, the development of renewable energy technologies has become of primary importance in the effort to reduce greenhouse gas emissions. However, it is often technically and economically infeasible to transport discontinuous renewable electricity for long distances to the shore. Another shortcoming of non-programmable renewable power is its integration into the onshore grid without affecting the dispatching process. On the other hand, the offshore oil & gas industry is striving to reduce overall carbon footprint from onsite power generators and limiting large expenses associated to carrying electricity from remote offshore facilities. Furthermore, the increased complexity and expansion towards challenging areas of offshore hydrocarbons operations call for higher attention to safety and environmental protection issues from major accident hazards. Innovative hybrid energy systems, as Power-to-Gas (P2G), Power-to-Liquid (P2L) and Gas-to-Power (G2P) options, implemented at offshore locations, would offer the opportunity to overcome challenges of both renewable and oil & gas sectors. This study aims at the development of systematic methodologies based on proper sustainability and safety performance indicators supporting the choice of P2G, P2L and G2P hybrid energy options for offshore green projects in early design phases. An in-depth analysis of the different offshore hybrid strategies was performed. The literature reviews on existing methods proposing metrics to assess sustainability of hybrid energy systems, inherent safety of process routes in conceptual design stage and environmental protection of installations from oil and chemical accidental spills were carried out. To fill the gaps, a suite of specific decision-making methodologies was developed, based on representative multi-criteria indicators addressing technical, economic, environmental and societal aspects of alternative options. A set of five case-studies was defined, covering different offshore scenarios of concern, to provide an assessment of the effectiveness and value of the developed tools.

Methodologies for the analysis and interpretation of environmental datasets from areas interested by offshore activities

The exploitation of hydrocarbon reservoirs by the oil and gas industries represents one of the most relevant and concerning anthropic stressor in various marine areas worldwide and the presence of extractive structures can have severe consequences on the marine environment. Environmental monitoring surveys are carried out to monitor the effects and impacts of offshore energy facilities. Macrobenthic communities, inhabiting the soft-bottom, represent a key component of these surveys given their great responsiveness to natural and anthropic changes. A comprehensive collection of monitoring data from four Italian seas was used to investigate distributional pattern of macrozoobenthos assemblages confirming a high spatial variability in relation to the environmental variables analyzed. Since these datasets could represent a powerful tool for the industrial and scientific research, the steps and standardized procedures needed to obtain robust and comparable high-quality data were investigated and outlined. Over recent years, decommissioning of old platforms is a growing topic in this sector, involving many actors in the various decision-making processes. A Multi-Criteria Decision Analysis, specific for the Adriatic Sea, was developed to investigate the impacts of decommissioning of a gas platform on environmental and socio-economic aspects, to select the best decommissioning scenario. From the scenarios studied, the most impacting one has resulted to be total removal, affecting all the faunal component considered in the study. Currently, the European nations are increasing the production of energy from offshore wind farms with an exponential expansion. A comparative study of methodologies used five countries of the North Sea countries was carried out to investigate the best approaches to monitor the effects of wind farms on the benthic communities. In the foreseeable future, collaboration between industry, scientific communities, national and international policies are needed to gain knowledge concerning the effects of these industrial activities on the ecological status of the ecosystems.

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions.

An alternative multi-region BEM technique for three-dimensional elastic problems

The main objective of this work is to present an alternative boundary element method (BEM) formulation for the static analysis of three-dimensional non-homogeneous isotropic solids. These problems can be solved using the classical boundary element formulation, analyzing each subregion separately and then joining them together by introducing equilibrium and displacements compatibility. Establishing relations between the displacement fundamental solutions of the different domains, the alternative technique proposed in this paper allows analyzing all the domains as one unique solid, not requiring equilibrium or compatibility equations. This formulation also leads to a smaller system of equations when compared to the usual subregion technique, and the results obtained are even more accurate. (C) 2008 Elsevier Ltd. All rights reserved.

An efficient numerical scheme for solving multi-dimensional fractional optimal control problems with a quadratic performance index

The shifted Legendre orthogonal polynomials are used for the numerical solution of a new formulation for the multi-dimensional fractional optimal control problem (M-DFOCP) with a quadratic performance index. The fractional derivatives are described in the Caputo sense. The Lagrange multiplier method for the constrained extremum and the operational matrix of fractional integrals are used together with the help of the properties of the shifted Legendre orthonormal polynomials. The method reduces the M-DFOCP to a simpler problem that consists of solving a system of algebraic equations. For confirming the efficiency and accuracy of the proposed scheme, some test problems are implemented with their approximate solutions.

Criteria for establishing shielding of multi-detector computed tomography (MDCT) rooms.

The aim of this work is to compare two methods used for determining the proper shielding of computed tomography (CT) rooms while considering recent technological advances in CT scanners. The approaches of the German Institute for Standardisation and the US National Council on Radiation Protection and Measurements were compared and a series of radiation measurements were performed in several CT rooms at the Lausanne University Hospital. The following three-step procedure is proposed for assuring sufficient shielding of rooms hosting new CT units with spiral mode acquisition and various X-ray beam collimation widths: (1) calculate the ambient equivalent dose for a representative average weekly dose length product at the position where shielding is required; (2) from the maximum permissible weekly dose at the location of interest, calculate the transmission factor F that must be taken to ensure proper shielding and (3) convert the transmission factor into a thickness of lead shielding. A similar approach could be adopted to use when designing shielding for fluoroscopy rooms, where the basic quantity would be the dose area product instead of the load of current (milliampere-minute).

The First International Workshop on Warning Criteria for Active Slides: technical issues, problems and solutions for managing early warning systems

Early warning systems (EWSs) rely on the capacity to forecast a dangerous event with a certain amount of advance by defining warning criteria on which the safety of the population will depend. Monitoring of landslides is facilitated by new technologies, decreasing prices and easier data processing. At the same time, predicting the onset of a rapid failure or the sudden transition from slow to rapid failure and subsequent collapse, and its consequences is challenging for scientists that must deal with uncertainties and have limited tools to do so. Furthermore, EWS and warning criteria are becoming more and more a subject of concern between technical experts, researchers, stakeholders and decision makers responsible for the activation, enforcement and approval of civil protection actions. EWSs imply also a sharing of responsibilities which is often averted by technical staff, managers of technical offices and governing institutions. We organized the First International Workshop on Warning Criteria for Active Slides (IWWCAS) to promote sharing and networking among members from specialized institutions and relevant experts of EWS. In this paper, we summarize the event to stimulate discussion and collaboration between organizations dealing with the complex task of managing hazard and risk related to active slides.

Identifying strategies for mitigating the global warming impact of the EU-25 economy using a multi-objective input-output approach

Global warming mitigation has recently become a priority worldwide. A large body of literature dealing with energy related problems has focused on reducing greenhouse gases emissions at an engineering scale. In contrast, the minimization of climate change at a wider macroeconomic level has so far received much less attention. We investigate here the issue of how to mitigate global warming by performing changes in an economy. To this end, we make use of a systematic tool that combines three methods: linear programming, environmentally extended input output models, and life cycle assessment principles. The problem of identifying key economic sectors that contribute significantly to global warming is posed in mathematical terms as a bi criteria linear program that seeks to optimize simultaneously the total economic output and the total life cycle CO2 emissions. We have applied this approach to the European Union economy, finding that significant reductions in global warming potential can be attained by regulating specific economic sectors. Our tool is intended to aid policymakers in the design of more effective public policies for achieving the environmental and economic targets sought.

Harsanyi’s Social Aggregation Theorem : A Multi-Profile Approach with Variable-Population Extensions

This paper provides new versions of Harsanyi’s social aggregation theorem that are formulated in terms of prospects rather than lotteries. Strengthening an earlier result, fixed-population ex-ante utilitarianism is characterized in a multi-profile setting with fixed probabilities. In addition, we extend the social aggregation theorem to social-evaluation problems under uncertainty with a variable population and generalize our approach to uncertain alternatives, which consist of compound vectors of probability distributions and prospects.

Heuristic solution methods for multi-attribute vehicle routing problems

Le Problème de Tournées de Véhicules (PTV) est une clé importante pour gérér efficacement des systèmes logistiques, ce qui peut entraîner une amélioration du niveau de satisfaction de la clientèle. Ceci est fait en servant plus de clients dans un temps plus court. En terme général, il implique la planification des tournées d'une flotte de véhicules de capacité donnée basée à un ou plusieurs dépôts. Le but est de livrer ou collecter une certain quantité de marchandises à un ensemble des clients géographiquement dispersés, tout en respectant les contraintes de capacité des véhicules. Le PTV, comme classe de problèmes d'optimisation discrète et de grande complexité, a été étudié par de nombreux au cours des dernières décennies. Étant donné son importance pratique, des chercheurs dans les domaines de l'informatique, de la recherche opérationnelle et du génie industrielle ont mis au point des algorithmes très efficaces, de nature exacte ou heuristique, pour faire face aux différents types du PTV. Toutefois, les approches proposées pour le PTV ont souvent été accusées d'être trop concentrées sur des versions simplistes des problèmes de tournées de véhicules rencontrés dans des applications réelles. Par conséquent, les chercheurs sont récemment tournés vers des variantes du PTV qui auparavant étaient considérées trop difficiles à résoudre. Ces variantes incluent les attributs et les contraintes complexes observés dans les cas réels et fournissent des solutions qui sont exécutables dans la pratique. Ces extensions du PTV s'appellent Problème de Tournées de Véhicules Multi-Attributs (PTVMA). Le but principal de cette thèse est d'étudier les différents aspects pratiques de trois types de problèmes de tournées de véhicules multi-attributs qui seront modélisés dans celle-ci. En plus, puisque pour le PTV, comme pour la plupart des problèmes NP-complets, il est difficile de résoudre des instances de grande taille de façon optimale et dans un temps d'exécution raisonnable, nous nous tournons vers des méthodes approcheés à base d’heuristiques.

Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d'horaires

Thèse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de Troyes

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Multi-Robot Task Allocation for Inspection Problems with Cooperative Tasks Using Hybrid Genetic Algorithms

In dieser Dissertation werden Methoden zur optimalen Aufgabenverteilung in Multirobotersystemen (engl. Multi-Robot Task Allocation – MRTA) zur Inspektion von Industrieanlagen untersucht. MRTA umfasst die Verteilung und Ablaufplanung von Aufgaben für eine Gruppe von Robotern unter Berücksichtigung von operativen Randbedingungen mit dem Ziel, die Gesamteinsatzkosten zu minimieren. Dank zunehmendem technischen Fortschritt und sinkenden Technologiekosten ist das Interesse an mobilen Robotern für den Industrieeinsatz in den letzten Jahren stark gestiegen. Viele Arbeiten konzentrieren sich auf Probleme der Mobilität wie Selbstlokalisierung und Kartierung, aber nur wenige Arbeiten untersuchen die optimale Aufgabenverteilung. Da sich mit einer guten Aufgabenverteilung eine effizientere Planung erreichen lässt (z. B. niedrigere Kosten, kürzere Ausführungszeit), ist das Ziel dieser Arbeit die Entwicklung von Lösungsmethoden für das aus Inspektionsaufgaben mit Einzel- und Zweiroboteraufgaben folgende Such-/Optimierungsproblem. Ein neuartiger hybrider Genetischer Algorithmus wird vorgestellt, der einen teilbevölkerungbasierten Genetischen Algorithmus zur globalen Optimierung mit lokalen Suchheuristiken kombiniert. Zur Beschleunigung dieses Algorithmus werden auf die fittesten Individuen einer Generation lokale Suchoperatoren angewendet. Der vorgestellte Algorithmus verteilt die Aufgaben nicht nur einfach und legt den Ablauf fest, sondern er bildet auch temporäre Roboterverbünde für Zweiroboteraufgaben, wodurch räumliche und zeitliche Randbedingungen entstehen. Vier alternative Kodierungsstrategien werden für den vorgestellten Algorithmus entworfen: Teilaufgabenbasierte Kodierung: Hierdurch werden alle möglichen Lösungen abgedeckt, allerdings ist der Suchraum sehr groß. Aufgabenbasierte Kodierung: Zwei Möglichkeiten zur Zuweisung von Zweiroboteraufgaben wurden implementiert, um die Effizienz des Algorithmus zu steigern. Gruppierungsbasierte Kodierung: Zeitliche Randbedingungen zur Gruppierung von Aufgaben werden vorgestellt, um gute Lösungen innerhalb einer kleinen Anzahl von Generationen zu erhalten. Zwei Umsetzungsvarianten werden vorgestellt. Dekompositionsbasierte Kodierung: Drei geometrische Zerlegungen wurden entworfen, die Informationen über die räumliche Anordnung ausnutzen, um Probleme zu lösen, die Inspektionsgebiete mit rechteckigen Geometrien aufweisen. In Simulationsstudien wird die Leistungsfähigkeit der verschiedenen hybriden Genetischen Algorithmen untersucht. Dazu wurde die Inspektion von Tanklagern einer Erdölraffinerie mit einer Gruppe homogener Inspektionsroboter als Anwendungsfall gewählt. Die Simulationen zeigen, dass Kodierungsstrategien, die auf der geometrischen Zerlegung basieren, bei einer kleinen Anzahl an Generationen eine bessere Lösung finden können als die anderen untersuchten Strategien. Diese Arbeit beschäftigt sich mit Einzel- und Zweiroboteraufgaben, die entweder von einem einzelnen mobilen Roboter erledigt werden können oder die Zusammenarbeit von zwei Robotern erfordern. Eine Erweiterung des entwickelten Algorithmus zur Behandlung von Aufgaben, die mehr als zwei Roboter erfordern, ist möglich, würde aber die Komplexität der Optimierungsaufgabe deutlich vergrößern.